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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 03 July 2022:

1. Why Foundational Models will Revolutionize the Future of Work and Play – Daniel Jeffries

It’s 2033 and you’re coming home from a dinner and realize your sister’s birthday is tomorrow and you forgot.

You ask your phone what’s the best gift for her and where you can get it at this late hour?

Your phone has dedicated processors for running Machine Learning (ML) models locally but it’s not powerful enough to answer that question with its small memory and slower chip speed.  But it is strong enough to ask a more powerful model in the sky.

The local model also learned a lot about what you and your family likes over time, so it packages up some key things it knows, anonymizes them, and fires off a query to a Foundational Model (FM) in the cloud via API.

In a fraction of a second, the answer comes back.

Your sister’s latest social media pics show she recently got on a serious health kick, lost weight, stopped drinking and got really into vegetarian cooking so it recommends an AirBnB cooking experience near her, with a local vegetarian chef.  It gives you two alternative experiences that are good but a bit further away and not as highly rated.  You don’t even need to go to the store and it’s the perfect present that makes you look like a hero…

…Across the world people are using cascaded FM’s networked together to do amazing work.  FMs on their own are amazing but working together they’re capable of astonishing feats and when they work with you they’re centaur units, a combination of man and machine working together to create something neither could do on their own.

Centaurs are named after Gary Kasparov’s early experiments with chess tournaments, where an AI and human teams bested pure AI and humans on their own. The tournament’s name came from the mythical beast of Greek legend that’s half horse and half man, symbolizing how man and machine can work together better…

…Biotech companies search through massive databases of proteins and chemical interactions and quickly use a fine tuned FM to design twenty potential drug candidates to fight a rare motor neuron disease that recently cropped up in South Africa.

A musician jams out a new tune and then asks the models to iterate on the chorus.  The 17th one is awesome and the musician plays it and then modifies it with a few tweaks to make it even more catchy based on a song he couldn’t get out of his head a week ago that he overheard on a radio at the local park. It goes on to be a huge hit on Soundcloud.

Materials scientists are designing new materials that make everything stronger and lighter, from skyscrapers that flex more easily to resist earthquakes, to electric bikes that are light enough to carry on your shoulder and fold up neatly to carry on the train.

Elite coders are simply telling the coding model what they want it to do and its spitting out near perfect Python code but it also recommends Go for several libraries because it will be faster and more secure.  It automatically does the translation between languages and tests it. It’s paired with an evolution through language model (ELM) coupled with a Large Language Model (LLM) and those models helps the coder create brand new, never before thought of code too, in a domain the model was never trained for by iterating on concepts quickly.

All of it is happening because of a vast global network of ambient AI models.  AI is everywhere now.  Every device is waking up and getting smarter.  We’ve industrialized intelligence and sparked a revolution in how we work, design, and play.

Welcome to the age of ambient AI…

…What are Foundation Models and why do they matter?

In essence, FMs are large models that exhibit remarkable capabilities, such as the ability to understand language, reason, create working computer code, do translations and arithmetic, understand chains of logic, generate totally new art from text prompts, and much much more.

The basic concept of FMs comes to us from Stanford University where they primarily refer to Large Language Models (LLM), like GPT-3, that are typically transformers. But the implications of FM’s go way beyond today’s architectures.  They’re a groundbreaking type of software, that’s not limited to transformers or language.

We can think of FM’s are any large and sophisticated model.  We can also think of them as a chain of cascading models that work together to do a complex task such as generate music or images or video, create mathematical proofs, design new materials or discover new drugs and more.

Many of them are already here.

GPT-3, from OpenAI, powers GitHub co-pilot that quickly writes code for developers, especially boring, repetitive code so they can focus on more creative tasks.  It’s one of the first fantastic examples of a centaur.  Originally, GitHub’s team wasn’t sure who would use it.  Would it be beginning or advanced coders?  Since its wider release to all developers, the answer is clear: advanced coders love it and use it most often.  Advanced coders are in the best position to understand when the model makes a mistake and it dramatically speeds up their day to day coding…

…In another article, called The Coming Age of Generalized AI, I highlighted researchers who were working on even more groundbreaking approaches by combining mega-models with several other key techniques.  One of techniques, called progress and compress that comes to us from DeepMind, combines three techniques, progressive neural networks, elastic weight consolidation and knowledge distillation.

The idea is simple. Create two networks, a fast learning network and a base model. That roughly mirrors the functioning of our brain yet again. Think of it as the hippocampus and neocortex. As Hannah Peterson writes in her article on catastrophic forgetting,  “In our brains, the hippocampus is responsible for “rapid learning and  acquiring new experiences” and the neocortex is tasked with “capturing  common knowledge of all observed tasks.” That dual network approach is called a progressive neural network.

The fast neural network is smaller and more agile. It learns new tasks then transfers the finalized weights to the base model. So you end up with a lot of stored neural networks good at a bunch of tasks.

But there’s a problem with basic progressive neural nets. They don’t share information bi-directionally. You train the fast network on one task and freeze those weights and transfer them to the bigger network for storage but if you train the network first on recognizing dogs, it can’t help the new network training on cats. The cat training starts from  scratch.

Progress and Compress fixes that problem by using a technique called knowledge distillation, developed by deep learning godfather Geoffrey Hinton. Basically, it  involves averaging all the weights of different neural nets together to create a single neural network. Now you can combine your dog trained model and cat trained model and each model shares knowledge bi-directionally. The new network is sometimes slightly worse or slightly better at recognizing either animal but it can do both.

It opens the door to cat-like intelligence.

A cat is a remarkable creature. It can run fast, sleep in tiny boxes, find food and water,  eat, sleep, purr, defend itself, climb trees, land on its feet from  great heights and a hundreds of other subtasks. A cat won’t learn language or suddenly start composing poetry. That’s perfectly fine because a cat is really well suited to its set of tasks; it doesn’t need  to build skyscrapers too.

Having a cat level intelligence is incredibly compelling. If you have a cleaning robot that can wash  dishes, pick up clothes, fold them, carry them from place to place and  iron shirts, that’s an incredible machine that people would clamor to buy. It doesn’t also need to write music, craft building blueprints, talk to you about your relationship problems, and fly a plane too…

…AI is a universal, general purpose technology.

The greatest breakthroughs in history are always universal technologies that affect a broad range of sectors as they branch into countless other domains and inspire unexpected breakthroughs.

Think of the printing press and the way it leveled up human knowledge across the board because now we could scale, save and replicate knowledge much faster.

Think steam engines that changed the very nature of work from human and animal powered muscle work to work done by machines.

Think of the microprocessors and computers that changed how we do art, communicate, design skyscrapers and houses, fight wars, find love, do science, make music and movies and more.

A general purpose technology like AI has direct and secondary effects on the world at large, both good and bad and everything in between.

We can think of ideas and technology as they grow and change and affect both their own domains and unexpected domains as a growing tree.  The roots are precursor ideas that eventually inspire the primary idea.  The trunk is the central breakthrough idea, which leads to a branching series of closely related ideas and some unexpected inventions in parallel domains.

2. Reducing Inflation Will Come at a Great Cost: Stagflation – Ray Dalio

More specifically, I now hear it commonly said that inflation is the big problem so the Fed needs to tighten to fight inflation, which will make things good again once it gets inflation under control. I believe this is both naïve and inconsistent with how the economic machine works. That’s because that view only focuses on inflation as the problem and it sees Fed tightening as a low-cost action that will make things better when inflation goes away, but it’s not like that. The facts are that: 1) prices rise when the amount of spending increases by more than the quantities of goods and services sold increase and 2) the way central banks fight inflation is by taking money and credit away from people and companies to reduce their spending. They also take buying power away by raising interest rates, which increases the amount of money that has to go toward paying interest and decreases the amount of money that goes toward spending. Raising interest rates also lowers spending because it lowers the value of investment assets because of the “present value effect” (which I won’t get into because it would be too much of a digression), which further lowers buying power. My main point is that while tightening reduces inflation because it results in people spending less, it doesn’t make things better because it takes buying power away. It just shifts some of the squeezing of people via inflation to squeezing them via giving them less buying power.

The only way to raise living standards over the long term is to raise productivity and central banks don’t do that…

…In summary my main points are that 1) there isn’t anything that the Fed can do to fight inflation without creating economic weakness, 2) with debt assets and liabilities as high as they are and projected to increase due to the government deficit, and the Fed also selling government debt, it is likely that private credit growth will have to contract, weakening the economy, and 3) over the long run the Fed will most likely chart a middle course that will take the form of stagflation. 

3. The Beer Game – Peter Dizikes

Thursday, August 29, 1:00 p.m.

It is a miserably muggy afternoon in Cambridge as the incoming class of the MIT Sloan School of Management—roughly 400 students from 41 countries—files into a second-floor ballroom at the Kendall Square Marriott. They are here to play the Beer Game, a Sloan orientation tradition. Unfortunately given the weather, the Beer Game does not involve drinking cool beverages…

…Rather, the Beer Game is a table game, developed in the late 1950s by digital computing pioneer and Sloan professor Jay Forrester, SM ’45. Played with pen, paper, printed plastic tablecloths, and poker chips, it simulates the supply chain of the beer industry. In so doing, it illuminates aspects of system dynamics, a signature mode of MIT thought: it illustrates the nonlinear complexities of supply chains and the way individuals are circumscribed by the systems in which they act…

…1:30 p.m.

Each Beer Game team is divided into four units of two players each, who play the roles of retailer, wholesaler, distributor, and brewer. The goal is to keep team operating costs as low as possible. We learn that teams will be penalized for having too much inventory (50 cents per case of beer per week) or unfilled back orders ($1 per case per week). Each link in the supply chain keeps track of its own costs, but a team’s score is the sum of these tallies. The lower the score, the better.

As we begin the first of 50 rounds (which represent weeks), each retailer unit draws a card indicating consumer demand for cases of beer; at the same time, all the units send slips of paper with orders up the supply chain. In response, cases of beer—represented by poker chips—move in the opposite direction, from brewer to retailer. A small number of chips are already at every station when we start.

2:15 p.m.

After 20 rounds, my team is on a hot streak.

I’m sitting at the retailer station with finance student Adah Jung, who’s been submitting orders at a level closely mimicking consumer demand. Our score at the retail station is low, and there are few chips elsewhere on the table, meaning our team’s costs are minimal. It’s hard to see how things could go wrong: with seven smart teammates and a stable supply chain, why can’t we win this thing? I can almost hear Sterman asking us to stand for a round of applause.

2:35 p.m.

Seemingly out of nowhere, our team’s distributorship has an inventory of 178 surplus cases of beer, which lasts seven weeks, adding $623 to our costs in a game where the average score after 50 weeks is $2,000 per team. How did that happen? Can’t someone tell our two teammates at the brewery just to stop making so much beer?

Well, no. “I can’t tell them anything,” observes teammate Juan Trujillo. Indeed, to simulate the incomplete information we deal with in real life, players cannot communicate across stations, apart from relaying orders. And somehow, someone on our team ordered way too much beer…

…3:30 p.m.

Sterman’s assistants tape charts to the ballroom walls detailing every team’s performance. Today’s winning score was $460 (the best possible score is about $200), while the worst-performing team racked up $6,618 in costs.

Sterman initiates a discussion, pointing out how inventories and backlogs spike and plummet erratically. The distributor on today’s last-place team went from a backlog of 70 cases to an inventory of 191 in three weeks.

One thing to learn from the Beer Game, then, is why many businesses experience boom-and-bust cycles—oil and gas exploration and housing among them. Complex systems produce nonlinear phenomena.

4:15 p.m.

Sterman pounds home a bigger lesson: our psychological habits and limited perspectives often keep us from properly understanding complex systems. To prove it, he asks distributors, wholesalers, and brewers to estimate their consumer demand; their responses are wildly inaccurate.

All too often, Sterman adds, this means we attribute problems to other people rather than to flawed systems. For instance: “I found that some people were kind of slow to take corrective action,” offers one student—who had just played for the winning team, a fact Sterman emphasizes to much hilarity.

It doesn’t make sense for us retailers to blame our teammates—who had imperfect information—for our disappointing scores. “It just cannot be true that, by chance, all the smart people ended up as retailers and all of the people running the factories were dumb,” Sterman says. The Beer Game’s structure makes it hard for certain players to perform well. It’s not the people; it’s the system.

Thus, firing people tends to be a futile management action. “Your role as a leader is to create a system in which everybody can thrive,” he says…

4. Why does the Stock Market go up? – Eugene Ng

A Google Search of “Why does the Stock Market go up?”, and Investopedia gives you up a broad range of factors.

The factors range from the supply and demand of buyers and sellers, to economic indicators, consumer confidence, wars/politics, concerns over inflation / deflation, government fiscal / monetary policy, technological changes, natural disasters or weather events, corporate or government performance data, regulation/deregulation, and the level of trust in the financial sector and legal system, amongst so many others.

But this doesn’t really answer the question, doesn’t it? It only leaves you, more confused, and begging for a better answer…

…The factors listed above are not wrong. Yet, they do not help you figure out why stock prices rise.

In the short-term, stocks will move up and down for a variety of random reasons — all of which does nothing to increase your chances of a positive return.

Thus a better question would be:

“Since the short-term does not really matter as much, why then does the stock market go up over the long-term?”

To get closer to the truth, you need to understand the components which drive the returns on your stock investment.

The Total Shareholder Return (TSR) from holding common publicly-traded stocks can be broken down into three key components: (1) growth in Earnings per Share (EPS), (2) change in the Price-to-Earning (PE) valuation multiples, and (3) earnings from dividends…

…With S&P Global providing us with historical data on the S&P 500’s closing levels, Sales per Share (SPS), Earnings per Share (EPS) and Dividend per Share (DPS), they provide clues on what the growth has been thus far…

…Take 2021 to 2003, the longest period spanning over 18 years (first row, last 5 columns from the right). During this time, the S&P 500 Index more than quadrupled from 1,112 to 4,766, with TSR* growing by ~4.3X (8.2% CAGR).

The contribution of the Earnings per Share (EPS) growth is telling. Earnings per Share (EPS) grew by ~4.1X (8.1% CAGR) from 48.7 to 197.9. Further breaking down that EPS growth, Sales per Share (SPS) grew by ~2.2X (4.5% CAGR) and Net Income Margin Growth (NIM) grew by ~1.8X (3.5% CAGR).

Thus the growth in earnings (EPS) accounted for the majority (~95%) of the TSR* growth, with growth in sales/revenues (SPS) and improvement in net income profit margins (NIM) accounting for ~52% and ~43% of TSR* growth respectively…

…Given what we have laid out so far, you, you should not be surprised to learn that over the long-term, it is earnings growth, supported by revenue and profit growth, that drives the stock market higher, and to a much lesser extent, valuation multiples.

5. Pioneer Helped Turn Her Family Store Into Japan’s Biggest Retailer – Chieko Tsuneoka

First her father died young, then her mother, then her older sister. At 23, Chizuko Okada inherited the job of running her family’s clothing store in Mie prefecture, Japan.

It was 1939, and war with America was just around the corner. Few could have foreseen that the little business would develop into Japan’s largest retailer by sales—or that a woman would be its driving force.

By the time Chizuko Kojima—her married name—died on May 20 of old age at 106, the company now known as Aeon Co. had thousands of stores around Japan and the rest of Asia and annual revenue equivalent to $64 billion…

…Ms. Kojima was born on March 3, 1916, as the second daughter of the Okada family, which had run a fabric and kimono store since 1758 in Mie prefecture, just west of Nagoya in central Japan.

Chizuko’s father, Soichiro Okada, modernized the business but died of heart disease in 1927 at age 43. Then Japan was hit by the Great Depression, which caused bankruptcies and joblessness.

In a 2003 book, Chizuko wrote that she believed it was necessary to be ready for such cataclysms by studying history. The hard times deprived her of a chance to pursue higher education in Tokyo.

After taking over the family business, Chizuko managed to keep it going during World War II until a U.S. bombing raid destroyed much of their home city of Yokkaichi in June 1945, including the Okada store’s stock.

At the time, customers held coupons similar to gift certificates entitling them to store goods. The store no longer had anything to offer, but Chizuko posted notices throughout the city saying her shop would give cash in exchange for the coupons, recalled her younger brother, Takuya, in a 2005 autobiography. It was a way of maintaining customers’ loyalty that would pay ample dividends in years to come.

Chizuko loved studying and during the war, she read a book about Germany’s inflation after it lost World War I. When Japan surrendered in World War II in August 1945, she predicted the same would happen. She gathered her cash and bank loans and bought as much merchandise as possible, reopening the shop in March 1946, ahead of an inflationary surge that hurt other businesses.

“All the merchandise flew off the shelves,” Takuya recalled.

Chizuko wrote of the episode, “Through my own experience, I learned the importance of studying and reading records of the past.”…

…In 1959, when the Okada family business still had just two stores, she came back to take charge of personnel and other behind-the-scenes management issues.

That year, Chizuko and Takuya made their first visit to the U.S. and toured the famous Sears, Roebuck and Co. store in Chicago. Takuya wrote that he was impressed by the giant scale of the business. Paging through the thick Sears catalog full of pictures of refrigerators, washing machines, clothing and a myriad of other goods, he imagined the day that Japan, too, would enjoy that kind of affluent life.

Chizuko was impressed by the Sears pension system, thinking it would create a loyal workforce. She introduced one a decade later, as her brother rapidly expanded the retailer through mergers. She also introduced an in-house training organization, today known as the Aeon Business School…

…Chizuko was one of the first managers in Japan who aggressively hired female full-time employees and homemakers as part-timers. She saw that many women worked in the U.S. and believed Japan should follow suit.

By having women at the company, “we were able to bring on board the viewpoint of the customer—how much to sell and at what price,” she said in a television interview when she was 90.

6. Make Haste Slowly – Chris Mayer

I had been reading The Art of Worldly Wisdom: A Pocket Oracle, a book written in 1647 by Baltasar Gracian, who was a witty Jesuit from Spain. His book of 300 aphorisms, with  his commentary on them, has been translated into many languages and has earned the praise of many philosophers ever since.

Arthur Schopenhauer loved it so much that he prepared a German translation himself. Schopenhauer said it was particularly good for young people, as it would give them experience it would otherwise take years to obtain. “To read it through once,” he wrote, “is obviously not enough; it is a book made for constant use.”…

…Anyway, there is a passage where Gracian talks about the motto “festina lente.” This Latin phrase is usually translated as “make haste slowly.” One must be very patient and yet ready to act swiftly. And the fastest way to achieve your goals is sometimes by doing nothing.

The motto was a favorite of the Roman Emperor Augustus. Engravers captured the idea with an emblem of a dolphin wrapped around an anchor, which they stamped on coins. Another emblem captured the same idea with a crab and a butterfly; again marrying this idea of fast and slow.

Festine lente recurs throughout history and has been captured in a variety of images, such as a rabbit coming out of a snail shell. The Medicis chose it as their motto and illustrated it with a sail-backed tortoise.

I thought the idea beautifully captured an important idea in investing that is often counterintuitive: to get where you want to go the fastest often means acting very slowly if at all…

…It does seem incredibly counterintuitive to say, “No, you shouldn’t  try to sell before a recession.” Or: “No, you shouldn’t ‘reposition’ your portfolio based on recent events.”  Don’t these seem like logical things to do?

Not if you want to enjoy the wonderful effects of compounding capital over long periods of time. The main problem with trying to do the above is they are too hard to do well enough. You have to think about trying to do these things repeatedly over a lifetime of investing. The odds against you are very great. Sure, you may be right sometimes. But you will most certainly sit out stretches of time where you could have earned great returns because you’re afraid of a recession. Odds are you won’t get those “repositionings” right repeatedly either.

7. How Parents’ Trauma Leaves Biological Traces in Children – Rachel Yehuda

After the twin towers of the World Trade Center collapsed on September 11, 2001, in a haze of horror and smoke, clinicians at the Icahn School of Medicine at Mount Sinai in Manhattan offered to check anyone who’d been in the area for exposure to toxins. Among those who came in for evaluation were 187 pregnant women. Many were in shock, and a colleague asked if I could help diagnose and monitor them. They were at risk of developing post-traumatic stress disorder, or PTSD—experiencing flashbacks, nightmares, emotional numbness or other psychiatric symptoms for years afterward. And were the fetuses at risk?

My trauma research team quickly trained health professionals to evaluate and, if needed, treat the women. We monitored them through their pregnancies and beyond. When the babies were born, they were smaller than usual—the first sign that the trauma of the World Trade Center attack had reached the womb. Nine months later we examined 38 women and their infants when they came in for a wellness visit. Psychological evaluations revealed that many of the mothers had developed PTSD. And those with PTSD had unusually low levels of the stress-related hormone cortisol, a feature that researchers were coming to associate with the disorder.

Surprisingly and disturbingly, the saliva of the nine-month-old babies of the women with PTSD also showed low cortisol. The effect was most prominent in babies whose mothers had been in their third trimester on that fateful day. Just a year earlier a team I led had reported low cortisol levels in adult children of Holocaust survivors, but we’d assumed that it had something to do with being raised by parents who were suffering from the long-term emotional consequences of severe trauma. Now it looked like trauma leaves a trace in offspring even before they are born.

In the decades since, research by my group and others has confirmed that adverse experiences may influence the next generation through multiple pathways. The most apparent route runs through parental behavior, but influences during gestation and even changes in eggs and sperm may also play a role. And all these channels seem to involve epigenetics: alterations in the way that genes function. Epigenetics potentially explains why effects of trauma may endure long after the immediate threat is gone, and it is also implicated in the diverse pathways by which trauma is transmitted to future generations.

The implications of these findings may seem dire, suggesting that parental trauma predisposes offspring to be vulnerable to mental health conditions. But there is some evidence that the epigenetic response may serve as an adaptation that might help the children of traumatized parents cope with similar adversities. Or could both possible outcomes be true?..

…It is tempting to interpret epigenetic inheritance as a story of how trauma results in permanent damage. Epigenetic influences might nonetheless represent the body’s attempts to prepare offspring for challenges similar to those encountered by their parents. As circumstances change, however, the benefits conferred by such alterations may wane or even result in the emergence of novel vulnerabilities. Thus, the survival advantage of this form of intergenerational transmission depends in large part on the environment encountered by the offspring themselves.

Moreover, some of these stress-related and intergenerational changes may be reversible. Several years ago we discovered that combat veterans with PTSD who benefited from cognitive-behavioral psychotherapy showed treatment-induced changes in FKBP5 methylation. The finding confirmed that healing is also reflected in epigenetic change. And Dias and Ressler reconditioned their mice to lose their fear of cherry blossoms; the offspring conceived after this “treatment” did not have the cherry blossom epigenetic alteration, nor did they fear the scent. Preliminary as they are, such findings represent an important frontier in psychiatry and may suggest new avenues for treatment.

The hope is that as we learn more about the ways catastrophic experiences have shaped both those who lived through those horrors and their descendants, we will become better equipped to deal with dangers now and in the future, facing them with resolution and resilience.

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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) and Wix. 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 26 June 2022:

1. Josh Wolfe, Chris Power – Factories of the Future – Patrick O’Shaughnessy, Josh Wolfe, and Chris Power

[00:02:39] Patrick: Chris and Josh, this is going to be a totally different conversation about an area that I don’t think I’ve ever explored before, very keyed in on a certain kind of manufacturing. I’m sure we’ll hit bigger themes of onshoring of manufacturing in just the next generation of this part of the economy. We’ll spend a lot of time around precision parts, what Hadrian’s doing, why Lux is interested in this area, what Chris, you and your team, are building. To set the stage, Chris, it would be great if you could, as you did for me on the phone recently, give an overview of the recent past and what has happened in this world. It’s become a topic that everyone’s talking about a little bit, but probably doesn’t really fully understand the recent intermediate past of manufacturing, where it happens, why it’s happened that way. So a little bit of a history lesson would be a great place to start to frame our conversation.

[00:03:23] Chris: For advanced manufacturing, in general, which I describe as space, defense, semiconductor, eVTOL, energy, medical devices, basically everything in the Jetson’s flying car, future, all has to be domestically manufactured because of ALTAI requirements. It’s super high precision components. And basically, 80% of the manufacturing parts for those industries flows through a high precision network of machine shops. There’s 3,000 or 4,000 of them. Average size is 10 to 12 million in revenue. In aggregate, they do 40-50 billion in revenue, but it’s incredibly fragmented, super low NPS. It’s the most perfect Keith Rabois fragmented, low NPS, vertically integrated structure you could ever possibly think of. Historically, what happened is this was built off the defense primes needing a bunch of suppliers. All these machine shops got built in the first Space Race or the Cold War. They were businesses that got started 30 years ago by 30 year olds. And now they are 30-year old businesses run by 60 year olds. What’s happened in the last five years is there’s not a lot of slack in the system. And generally, a machine shop might be making some semiconductor parts, some parts for Boeing, and then some parts for Raytheon, for example. In the last five years, because of the boom in commercial space, which has been largely driven by lowered launch costs, the success of companies like SpaceX and Anduril, and then investors like Josh have been putting money into satellite companies, rocket companies, the whole thing. If the top level, you’ve got a bunch of net new spend in high precision components from commercial space and companies like Anduril that are flooding the same supply chain. That’s big problem number one.

And what you’re seeing for those customers is, “Hey, I’m trying to ship a satellite really quickly. I’m getting parts in 6 to 10 weeks. That’s insane. Because I’ve got an aerospace engineer sitting around for another part, wasting time, when I’m trying to get a launch up. I’m trying to get my startup goals.” So all these new entrants to the market are going way, way faster than your traditional primes. Now, that’s putting speed pressure on the supply chain. And basically, you’ve got this thing where customers want fast supply chain, huge opportunity to build a business meeting that need, with a bunch of net new spend in the supply chain. That’s phase one is Hadrian builds a better mouse trap for new space and new defense. The second phase, which is really scary for the country though, is all of those 60 year olds are going to retire in the next 5 to 10 years at an increasing rate. And 90% of them, historically, when they do retire, don’t transition to private equity, or sell or transition the business to a son or a daughter. They sunset the business, lock the door, sell a machine, and throw away the keys. There’s two bits that are really dangerous for the country about that. The first one is just purely capacity. In the decade where we’re trying to butt heads with the CCP and win Space Race 2, the capacity that feeds rocket satellite, drone companies is going to fall through the floor because of this capacity issue. They’re retiring, so you’ve got this huge supply and demand imbalance in the worst possible decade that that could be happening. And on top of that, it’s not as simple as, say, a Raytheon going, “Hey, Patrick’s Machine Shop, you’re retiring. Let’s take all the digital files that tell someone how to make those parts, and give it to another machine shop.” Most of them have been made for 20 years. There’s no CAD file. The drawing is in someone’s desk drawer. And we’ve just seen this where we shipped something like a third of all our Stinger and Javelin missiles to the Ukraine. This is on the defense side, but this happens across space semiconductor really. So we shipped all over to the Ukraine. The Biden administration went to Raytheon and said, “Hey, we need more Stingers and Javelins.

And then Raytheon came back and said, “Well, apart from the fact that supply chain’s super bottlenecked and we can’t ramp up production, we just don’t know how to make any of the parts anymore. And it might take a couple of years to figure it out.” So it’s a complete disaster, both on net new spends in secular growth and decline. But what people don’t realize is it’s not as simple as, “Hey, let’s raise your semiconductor. Let’s throw a 50 billion into an Intel plant in Arizona.” Because in the ’70s and ’80s, when we outsourced advanced manufacturing, what we lost was not just capacity or capability, it was the talent and the people. And what people don’t understand about manufacturing, it’s like software engineering. To get AI researchers, you have to have a base of backend software engineers. You’ve got a million software engineers, and it breeds the best. It breeds the best. And all of a sudden, you’ve got some top tier people in deep cloning and all that other stuff. It’s the same in manufacturing. You can’t really skip these training levels. So what we lost was not the knowhow to do a specific part, but the talent base that can produce better and better people that can work on things like semiconductors or advanced manufacturing. The slack in the system is not simply a capital problem. It’s this talent based problem. You can solve some of that by trying to grab some people from Taiwan, people who really know this, and rebuild all these industries. But it’s much, much slower than people think it is because it’s not as simple as turning a capital key, buying some machines, and ramping up production capacity. It’s incredibly difficult to do. It’s a huge commercial opportunity, but it’s incredibly important that we get this right for the country because space is basically a defense domain. Peace through strength is a huge deal. We’ve created this period of peace with Pax Americana. And I think in the next couple of years, maybe in the next 18 months, we’re going to really see that a lot of that is risked, and there’s going to be a huge wake up call when the average American consumer not just can’t buy an iPhone for less than $4,000, possibly can’t buy one at all because of all this global shifting of the advanced manufacturing supply chain…

...[00:12:57] Patrick: I actually just did one of these with Brian from Anduril. And it was really interesting to dive into the nature of the pieces of what they’re building and their goal for speed, simplicity, modularity the philosophy of how these things are built, whether it’s Ghost or whatever the product is at Anduril, is it’s very different from a Predator drone or something that Lockheed or Northrop would put out over the course of a decade. I’m curious, Chris, how much you think in the success case for Hadrian, where it’s everything you dreamed of and more, 10 years from now, how its existence changes the nature of the things that get built? What will this new manufacturing capability, just like Stripe and Twilio, people build stuff that they couldn’t have dreamed of before because they were able to go so fast with this new tooling, how do you think about that, Chris, in terms of what this might lead to? That even though there’s amazing things happening at SpaceX and everywhere else, it’s on the back of these 3,000 mom-and-pops. What will be different in the success case for Hadrian, for the people at the top of the chain?

[00:13:53] Chris: I think if you think about software engineering 10 years ago, maybe to start a SaaS company, it costs a million dollars, and you were spending more than 50% of your time on activity like running a server farm or building payments that every single software company had to deal with. When you see these platform infrastructure companies like AWS come out, or Stripe come out, or Twilio, you get to really interesting dynamics. One of which is the cost to start a company in this space goes through the floor. So now with all the tooling, you can start a software company for a couple hundred dollars. Secondly, the number of companies that get started because of that tooling goes through the roof. And then the third thing is the speed at which those companies can iterate, basically turn engineering time into a good product that the market wants, goes through the roof because their iteration cycle goes through the roof.

If we get this right, we should be able to drive three things. One is that existing companies can iterate on products in order of magnitude faster, which means that at the product layer, you just get better products. You’re not doing a year long cycle for a satellite. You’re doing a two month cycle for a satellite. As you’re getting feedback from the customer, your designs can change way, way faster. Secondly, by having Hadrian as a platform, we should be able to dramatically lower the cost of starting advanced manufacturing companies, which will drive a Cambrian explosion in both this evolutionary, who’s winning in the marketplace to build a satellite company or a drone company. The raw number of these companies that start will go through the roof. And that would be success for me.

[00:15:19] Josh: One of the things you just said, which I think is really interesting, there’s this old thought experiment, which was actually manifest in a physical experiment, where you took two different classrooms of people that were making some sort of pottery. They had a very specific end state of a pot that they had to make. And one was told, “Spend two hours or an hour or whatever it was making the pot as perfect as you can.” And the other was told, “Make as many pots as you can.” The latter, which was rapidly iterating, and trial and error, and trial and error, ended up making the more perfect pot. So that idea, which I think applies to industry, is if you make something and then you’re waiting forever to test it in the real world versus being able to rapidly iterate. The latter example is something that Hadrian’s going to enable, that in turn then, lets many more startups flourish for less capital. We can do more experiments, fund more companies. They can fail fast. Or they can come up with a product that is superior and competitive, and then build a platform from there…

…[00:22:50] Patrick: Chris, can you help us understand, going all the way to the beginning of the supply chain, the rare earth or base metal component of this process? Because I don’t think people have probably thought too much about is this aluminum, is it steel, is it titanium, is it something else? What’s the 101 on the actual raw materials that are important in this process? Because out of nowhere, after a decade of silence, the commodity world has come alive. There’s issues shipping, there’s issues sourcing, there’s issues in pricing, there’s inflation. This becomes a really important thing really quickly. So give us a little tutorial on what are the important metals that go into all of these shops as raw material and anything that you think we should know about the nature of that today?

[00:23:30] Chris: Basically there are four main alloys that all space defense semiconductor satellite companies use. Aluminum, 6061, 7070, steel variants, so 306, 316, 30X, titanium, and then Inconel variants. There’s a ton of aluminum on satellites. There’s slightly less aluminum on rockets. And then on rockets, you start to get into steels and harder metals like titanium and inconel because the closer you get to the engine, the hotter it is, so you need material that can withstand heat. And then it’s the same thing for the defense side. So if you look at a fighter jet, there’s a bunch of structural aluminum, there’s a bunch of structural titanium, because it’s incredibly lightweight, and then the engine is incredibly hard, heat withstanding materials like inconel. Those are the input materials to the parts. So let’s talk about that. I mean, let’s talk about the parts that are on the machines that we run in our factory, because that’s a little bit scarier. In a sense, I think the aluminum price over the last year, it’s come down a little bit now, but I think it doubled. That was a supply chain shock from the inputs, but then the mills themselves in America had a labor shortage. There was just lack of supply, so the price went up. The parts that went on those satellites during that nine month period, the machine shop can’t absorb them, so it gets passed on, so the satellites are now 30% more expensive. And if you look at where those materials get sourced from, we have a pretty good supply of aluminum in the United States. 90% of the titanium in the world comes from Russia and the Ukraine. A bunch of aluminum and steel comes from Europe as well. And actually, if you look back to the Cold War, our spy planes were made out of mostly titanium. Skunkworks had to sneak titanium out of Russia to be able to make spy planes.

This is why I think a lot of the hand waving around sanctions is ridiculous, because if you’re in an adversarial position and you say, “Hey, we’re doing all these sanctions,” and then there’s 50 exclusions because you’re kidding yourself about the fact that this guy’s the only one with a titanium. It’s just ridiculous. That is a real problem both on lack of production, but also availability of supply to American companies because a lot of that is offshore, which makes me crazy, because the State Department years ago should have been going to Latin America and Africa and getting supply of all this stuff and partnering with these countries and raising them up. Whereas China through Belt and Road has secured a lot of this global supply because they’ve got Russia locked up with the whole energy pipeline thing. They’ve got Africa locked up. So it’s a real huge challenge. For rare earth materials, which are more things that go into batteries, chips, that sort of stuff, they’re not on the parts that we’re producing, but our machines obviously have a ton of chips in them. And then every single satellite or rocket has a bunch of chips or circuit boards in them. So that’s a huge problem, and that’s way more strategic because obviously 70% of the world’s chips come from Taiwan, most of which is TSMC. And then the other thing is the rare earth minerals like lithium or cobalt are largely Latin American, but the Latin American mines are much, much less developed. That’s a huge challenge as well…

…[00:27:54] Patrick: Chris, if we zoom all the way back to the unit here of the mom and pop machine shop, what are the key set of jobs being done by one of those given shops? You mentioned the nature of them. It’s the 60 year old soon to retire making 10 or $12 million revenue per shop or something like this. What are the key components that are inside each of those shops that you want to lift out as core functions or jobs to be done and then start innovating on inside of a Hadrian factory?

[00:28:20] Chris: There’s three big chunks. One is the digital side of manufacturing, which is taking a customer PDF print and doing a bunch of creative geometry work to get that into machine code that tells the machine how to cut the part. That’s one big chunk, and that’s very software automation heavy. The second chunk is running the machine itself, which again is less of a robotics problem. It’s more of a software engineering problem. So what a master machinist does on the control is a lot of manipulating code on the fly as they respond to slight differences in the cutting tools, slight differences in the raw material. That’s a big operations software problem. And then the third layer is general logistics. So you’ve got unpredictable cycle times of each operation in the factory, you’ve got huge variances in how long something takes to inspect from one part to the other. And then you’ve got a lot of customer requirements that are incredibly variable for each purchase order that comes through. And as an example, this is a very simple example, but can create a lot of operational noise if you don’t get it really right at the top end of the funnel is laser marking a part. So producing all these space components and then at some point an engineer’s going to want to test it, often there’s a call out on the print that says, “Hey, engrave this part with a serial number, the purchase order number, or the print revision that the aerospace engineer said, ‘This is my part number.'”

You might think that’s easy, but there are about 10 aerospace known specifications of the depth of the laser engraving, how big or small it has to be, and what the function of that is. In a regular machine shop, you might have a guy running a laser machine that’s staring at a PDF print that might remember the specification. So a lot of it is that documentation of that engineering knowledge and then systemizing it so that the whole thing flows smoothly, yet haven’t got a bunch of random art going on. Even load balancing that is an insane challenge because you might have all these machines set up for making the part, inspecting the part, cleaning the part. But if you get one of those throughput messages wrong, where you’ve got, say, 10 jobs running through a facility at once, but they all happen to hit the quality inspection station at the exact same time, all of a sudden you’ve got a bottleneck and all the jobs are late. Before you tell the customer, “Hey, we can get this in two weeks,” having that load balancing like a data center with foreknowledge of where the capacity bottlenecks might be in three weeks so you can make good judgment calls on what you’re promising versus what you’re delivering is a huge data science and operational excellence challenge…

…[00:32:09] Patrick: Josh, how do you prosecute diligence for something like this as an investor. Hearing all that, I’m going to ask more follow up questions in the minute on the unit of the machine and the areas of innovation and the machinists, et cetera. But when you’re facing something like this early on and it is factory one or factory one’s still a glint in your eye, how do you do diligence on someone’s ability or a team’s ability to execute something like this?

[00:32:33] Josh: I’m probably going to get myself in trouble with this. First you make an investment in another company that fails.

[00:32:38] Patrick: Good start.

[00:32:39] Josh: And that’s what we did. We and Founders Fund actually were co investors in a company that did not work. Part of that was narrow focus, part of that was team and structure. There was something proverbially different with Chris that their light shone brighter. As you can hear him talk, not only understanding the macro, but if you have a customer like Brian, Andrew, or Palmer, they want to anodize titanium and aluminum and parts that not only have electric chromatic coatings that strengthen and provide performance, but look really cool. They’re super high demanding, yeah. So Chris’s understanding of the macro to the micro is something that was inspiring, but we were still making a bet without any existence proof of why we were basically going to make a very similar investment as we had before, but this time was different, those dangerous words. And it truly came down to his vision of understanding industry structure, his vision of seeing the technological pieces that could be put together. Notably, he told us why we lost money in our last investment, which was super valuable. So he diligenced our failure to diligence properly our prior investment. And part of that was you don’t want to automate everything.

He’s like, “You don’t want a 100% automation. You need humans in the loop in some of these aspects.” Maybe you want 80/20 or 70/30, but you need people that are there able to very quickly look at the geometry of a part or design, make a human decision, let the computer do it. A lot of it really came down to Chris understanding the macro, the micro of individual parts, the flow, where bottlenecks were. And then I think this is really important, you really have two different cultures. You have a machining culture, which is very blue collar in many cases. It is people working with their hands and really deep narrow specialists. And then you have this coding software culture, which is almost the antithesis of that. It would be good to actually hear from Chris, how do you think about those two people speaking very different languages, sometimes growing up and going to very different schools, actually being teammates and working with each other, because that answer that we got from him was super confidence inspiring.

[00:34:29] Chris: The difficult thing, going back to previous failures in his space, was both in private equity and software engineering trying to automate manufacturing, the previous approaches have been very egotistical in the sense of, “We don’t need any industry knowledge.” Either I’m a guy with a spreadsheet and I know how to do an IRR calculation. Operations doesn’t generate profit, finance does. So the attitude towards machinists or manufacturing people is very downwards looking. I saw the same arrogance in Silicon Valley, which was, “Let’s not try and work with the best in the industry to automate this in the right way.” It’s, “Let’s grab 30 PhDs and don’t hire a machinist until employee number 28 and just try and figure it out ourselves,” which for me is just this very coastal elite looks down on flyover state dynamic. What I recognized was in machining, all of the problems have been solved by people, the knowledge is in a bunch of people’s brains. It’s not like we’re inventing a new algorithm for machining. If we did nothing, but just find all the right answers and get them into software and process, we would win immediately. To do that you have to create a culture where people feel comfortable working with a software engineer and machinist and an operations person all in one conversation and setting the standard that just because you’ve got a maths degree from Yale and this guy didn’t graduate high school, setting that culture so they work collaboratively and there’s no finger pointing or whatever and everyone’s pulling in the same direction is really, really important. This was one of the most important things that I worked on, and it’s a combination of making sure, even really simple things like no matter of whether you’re a machinist or a software engineer, your equity that you get in Hadrian is the same based on your rank. The pay is the same. All this other stuff is super, super, super important.

Really finding the people from industry that want to share their knowledge and want to train people, is an incredibly rare thing. So we’re incredibly lucky to have the 20 or 30 people in industry that actually want to share their knowledge and understand that we’re all pulling in the same direction. And that is a really unique thing. To give you an example of how scary this is, even at the most innovative space companies, to train someone on how to inspect a part is usually this thing of like, “Hey, we’ve got all these amazing people that want to work in manufacturing but I’m on X number of dollars per hour, and I don’t want to share my knowledge because my job is at risk.” Even something as simple as training new entry to the workforce is incredibly hard because of this protectionism. People ask me what the secret sauce is, and I think investors think we invented this new technology and that’s the core of the company. The core of the company is 50 people soon to be 80 and 100 pulling in the same direction. Understanding that what we’re building is a culture of, here’s a problem let’s solve it and no matter where the solution is coming from, implement it and work together. That’s the core of what we’re building which long term is going to be a huge, huge advantage. Because if we get Hadrian right, there’s no reason why we can’t take the same team and go solve tube bending or raw material or whatever it happens to be.

[00:37:14] Patrick: You described this the first time we talked as the PhD arrogance trap, which I really liked as a phrase. Thinking you can just solve every single problem immediately with technology. Interesting to hear about the inner relationship between the two teams or the two modalities. When it comes to the individual machine and the machinist working together inside of a Hadrian factory, again maybe starting to squint a little bit and look out 2, 3, 4, 5 years, what do you think the innovation zones are on the machine side specifically? In what ways will a Hadrian machine be better five years from now than it is today? Because it sounds like there hasn’t really been much innovation on the machines themselves in these mom and pop shops?

[00:37:51] Chris: Actually, I think that’s slightly incorrect. But I would say that we are not really innovating on the machines themselves. And that’s part of the trick here is we are buying everything mostly off the shelf and then doing really tight software integrations to override the core software that lives on these machines to make them run better. But we’re not doing mechatronics and upgrading the machines themselves. Building your own machines while trying to scale a factory is like two impossible tasks. What we’re really doing is going, “Hey, these machines have APIs that control everything about them.” No one’s ever used an API for this machine ever before, and that’s really where the technology curve is honestly. Even down to simple things like, you’re meant to be able to run a machine overnight without it stopping itself. There’s actually 20 or 30 reasons why a machine would stop itself running. A Tool breaks, something goes wrong in the controller, it’s like a literal software bug. A lot of our automation is actually building the robustness into these vendor machines so that they self-correct overnight so we can get the throughput and the efficiency.

One of the reasons why you have a second shift at a machine shop, which is incredibly inefficient, is because someone’s hanging around waiting for the machine to error out and they know how to clear the error and get it going again. Which sounds insane, but that’s honestly 70% to 80% of the problem. It’s hilarious having people from industry where we come back in the morning and the machines run itself overnight and there’s 10 good parts sitting there. And people are like, “Wow, this is amazing.” I’m like, “What do you mean? These machines are designed to run overnight?” And they’re like, “No, well, it almost never happens in reality.” The reason is, because over the last 5 to 10 years, the amount of software that’s in these machines has grown exponentially, but no customer of the machines has ever been able to take advantage of it because, what machinists knows how to write software? What machine shop can afford to pour a software engineer into the problem? Or even if they had a software engineer, have them spent three months of R&D on figuring all this stuff out versus just firefighting operations because they’re trying to deliver for a customer. So that’s more what’s going on than us innovating on the hardware side.

[00:39:44] Patrick: And the innovation, the units of innovation themselves driven by software, is better-cheaper-faster the right way to think about what you hope to accomplish by starting to tune the dials using software?

[00:39:55] Chris: Definitely on the front end of the factory in the digital manufacturing CAD and CAM programming space, 100%. Because you just want to turn a 20 hour process into a 2 hour process. It’s possible. It should be done. We’re chipping away at the marble and will get there. For the factory, I actually think that simplification and robustness are the two most important things, because in manufacturing, complexity and lack of robustness are what drives costs. You’re actually better off having a system that works every single time that’s simple. That gives you two things. One is, there’s less errors so there’s not a bunch of people firefighting. And because it’s simple, you can train many, many more people into that system. Getting rid of a lot of the complexity of making everything truly error proof is a lot of the innovation there, which seems counterintuitive. But in the real world, you want as little errors as humanly possible versus trying to dial up the efficiency on something so high that it breaks one in even every 10 times and all of a sudden you’ve got three or four people standing around figuring out how to solve the problem. That’s really, really what’s important there.

Now, what you get from that is speed. So speed is not necessarily like, cut the part faster. It’s at every handover point, don’t have to go back in the step, go back in the step or have this station hanging around waiting for information because you’ve got errors. So the whole factory speed is optimized by having each of these individual pieces incredibly robust. For the customer layer, they get speed. What’s great about speed is everyone wants it, so we also get pricing power. As we hit the robustness layer, we have margin efficiency growth because people are hitting things every single time cleanly versus running around scrambling like, where’s this bit of paper, where’s this tool? Now on the customer layer because we are reliable and fast, we have enormous pricing power. It’s this interesting dynamic about manufacturing where, if you just focus on robustness and cleanness of the process, you kind of generate margin improvement automatically and therefore you get pricing power because you’re fast and you reliable.

2. Quotes from Seth Klarman Interview – The Transcript and Seth Klarman

2. The impact of rising rates: 

“That is going to test financial institutions who’s been writing derivatives they shouldn’t write, who’s been stepping out to take greater risks in their portfolio because if you can’t make it in bonds, people try to make it somewhere else.”

3. Watch out for anchoring

“After you buy something you paid for, it doesn’t matter. People cling to the idea that at least they should get their money back; maybe there is bad news, and you should sell before it goes lower; maybe put it into something else where you get your money back, but people prefer to make it back where they lost it. People anchor numbers in their heads, and they hold on to them. They have a way of remembering what happened relatively recently. If you recently had a pandemic, you over-worry about the next pandemic even though they don’t happen that often. I was certainly guilty of that after 9/11 myself. It seemed obvious that we’d get hit again, and then we didn’t for a long time.

4. Best business book: 

“We should not expect people to be rational all the time. Daniel Kahneman does a beautiful job in Thinking Fast and Slow. It is in many ways the best business book, the best investing book ever written even though it’s not ostensibly about business or investing because it tells us about ourselves”…

...6. On finding edge: 

“There are lots of ways to develop edge as an investor. One of the ways is deep fundamental knowledge. I have total respect for people who dig incredibly deep in an area where they’re doctors and medical researchers. They study biotechs and that’s formidable. No one should underestimate that power, but that’s not the only kind of inefficiency, as the inefficiency might be informational. Two things happen in markets; right markets are inefficient partly because of human nature, as I mentioned; greed and fear. People get greedy and panic; in some cases, the panic is legitimate. “Oh crap, I leveraged my portfolio, and I’m getting a margin call.” or “I have short-term clients, and they can redeem, and I’m getting redeemed, and I have to sell whether I like it or not.” There are other constraints on investors that also create inefficiencies.

Once in a while, we get a call from someone with one asset in their private equity fund who want to raise the next fund. They want to book a gain on that asset. And so, call it the last asset phenomenon. People literally will sell that more urgently, and maybe they’ll favor getting it done over the exact price they get because they want to raise their next fund and move on. They want to book a game and get paid. We live in an imperfect world, and their clients should probably not love that, but maybe their clients would love it. The manager has a lot of things to balance, so that’s just one little example. When a bond gets downgraded, there’s always an immediate rush to the exits by the investment-grade holders. A bond gets downgraded to junk, say when the bond goes literally from BBB to BB. Many bonds have to get sold; some are probably sold in advance. It’s good to know what a company does, its operations, and its worth. It’s also interesting to know that there’s a very large seller, and the bonds are 20 points lower. With essentially no change in any information, just the rating of a 26 year old at moody’s. So those are the kinds of things that can trigger our interest then we do fundamental work”…

…10. On making mistakes:

“Today, there’s not so much mean reversion. Things may not be mean-reverting because of technological disruption, so I think investors have had to raise their game massively in the last several decades, and I’m not done raising it. I probably haven’t raised it as high as it needs to be. It is a great time to be knowledgeable about technology; it was a great time if you could figure out what Amazon was up to. For a value investor, it looked hopelessly risky but for a tech investor, maybe with the right insight into the value of platforms and the value of winner take all business models, that would have been a good thing to have that I didn’t have. I pat myself on the back and say, okay, Seth, you were a schmuck twenty years ago and ten years ago for not figuring it out, but you were smart to figure it out five years ago. That’s all an investor can do; be intellectually honest, be self-critical we’re justified, and keep trying to get better every day. Like Warren Buffett, the best investors study read admit mistakes um always looking to get smarter and wiser because what else can you do as a person.”

3. Capital-Efficient Growth (with Zoom CEO Eric Yuan & Veeva CEO Peter Gassner) – Benjamin Gilbert, David Rosenthal, Eric Yuan, and Peter Gassner

David: Amazing. Eric, could you share your fundraising journey with us?

Eric: Sure. I started the company in 2011. First thing I did, I opened up a Wells Fargo bank account. It’s very easy for me to raise capital that’s why I opened up a bank account. Unfortunately, it took me several months. No VCs wanted to invest in me. Unfortunately, I do not know my brother […] Emergence Capital. Otherwise, life would be much easier. Finally, we targeted some of our friends. It reached $3 million seed funding. That’s how we started.

Here comes […]. I tried to target VC again, again, nobody wanted to invest in us either. We targeted friends and got another $6 million. That’s how we started. It’s very hard.

Ben: Nobody wanted to talk to you at that point because most people assumed video conferencing was either a settled frontier or a race to the bottom. Am I thinking about that right?

Eric: Absolutely right. That’s the thing. Everyone mentioned, Eric, you are crazy. The world has known you to have another video conference solution. Another VC friend even is a great friend, he told me that, Eric, I have a check for you as long as you do something else. I couldn’t say I did not listen. I was very stubborn. Also, he shared to me a story. Once I was told by a big VC, I do not want to mention the name, for sure, you guys do not like them.

He told me that, Eric, I do not think your […] works. Look at Skype, look at Google Hangout, look at Webex, they’re dominating, right? I debated with him a little bit. I failed. I cannot convince him.

On the way back, I told him myself, I’m going to change my Windows screensaver. Back then I was using a Windows machine. I changed the Windows screensaver—you are wrong. For several years.

Ben: Just to make sure I have my facts straight, I believe you raised a $30 million dollar round led by Emergence and then another $100 million dollar round after that. Similar to Peter, you did not dip into any of that $130 million to build the business. Is that correct?

Eric: For me, actually, I offered $30 million from Emergence Capital. I think we are on the right track. To be honest, actually, we don’t even need to raise a Series D because at the time, with that $30 million, I think the company was completely different again.

David: One thing we wanted to ask is a difference between your two companies. Peter, obviously, once you got to cash flow profitability, which was immediately, basically you never raised another round. Eric, you did make the decision to raise some more capital even after you were generating cash. Peter, you were on Eric’s board when that process happened? Why did you make that decision?

Peter: For Veeva, I didn’t raise more just because I thought I didn’t need it. It’s just that simple. As far as for Eric, when you’re on the board, that’s really Eric’s decision.

Eric: As I mentioned earlier, I offered to raise $30 million from Emergence Capital. At that time, seriously, they had no plan whatsoever to raise another round of capital. The reason why we still wouldn’t move forward to have a Series D is because I thought the economy would go down quite dramatically.

David: This was 2017?

Eric: Sixteen, ’17 timeframe. I was completely wrong…

…Ben: As we were preparing for this interview, our first thought was, if we just had one of you up here and we were interviewing you about capital efficiency, it’d be easy to chalk it up to business model and cash flow cycle. Multimillion-dollar contracts upfront in the case of Veeva, or in Zoom, customers flocking with their credit cards for a self-serve experience. These are two completely different models.

I think one of the things that it illustrated to David and I is capital efficiency is a mindset and culture thing more than a business model thing. I’m curious to hear both of your reactions to that, but also, what are the things that enabled you uniquely, more so than 99% of startups to be so capital efficient?

Peter: I can take that one. I guess I’ve seen a little bit of Zoom and a little bit of Veeva. I would say, probably, it starts with a mindset. Just run a profitable lemonade stand. From my point of view, for me, there’s safety in that. Cash generating business is always going to be valuable to somebody. At some point, a business that’s not cash generating is going to be valuable to nobody. There’s security in the long term. It starts with the mindset. I think Eric shared that.

Then you have to have product excellence, too. That’s something I think Eric and I share. We’re both product people. I think also, we both worked really hard. We work really hard now, especially Eric. Probably in the first five years, I worked really hard. You didn’t see me working really hard, but I saw you working really hard. We worked really hard, we worked really focused. Anything that wasn’t related to the product or the customer was just BS, then just don’t do it.

The first five years, I was not at a conference like this, for example. I was just maniacally focused, and then the market really helps too. That’s something you just have to get lucky on. It was the right timing for Veeva, it was the right timing for Zoom. Maybe if you started Zoom five years earlier or five years later, it would have been hard.

Product excellence, real focus, mindset, and then you have to have some luck in your market. I’m sure there are some things that I could have tried to do or Eric could have tried to do. We might have picked a bad market and then it just wouldn’t work.

We’re outliers and so is Eric. You have to pick something that most people think is going to fail to be an outlier. Otherwise, by definition, you’re picking something that most people think is going to work. A lot of people are picking it, therefore, you’re not an outlier.

Just like Eric, all VCs have any kind of note except for Emergence turned us down. Ours was really simple. Vertical specific software, that’s a small market and it doesn’t work. That’s what they would say. I was encouraged by that because I thought, well, it has an opportunity to be really good because it’s something non-obvious.

David: One thing that I want to double click on that we were talking about beforehand. Yes, you need to be non-obvious, to have a chance of a great outlier outcome, but you also need to be correct. What you both did was not, hey, I’m going to pick some random idea that other people think is crazy.

I know Veeva, as one of your core values, clear and correct target markets that you have written on the wall. What did each of you do ahead of time that led you to really genuinely believe, yes, the world thinks this is crazy, but I really think this is going to work?

Peter: I’ll go first, this is really easy. I talked to three or four potential customers for our first product. They all said, we don’t need that. That’s not interesting. It’s not a good thing to do. But I wasn’t listening to that. I was listening, are they emotionally attached to where they’re getting their product now?

Are they emotionally attached to those people? Do I feel like they’re getting value out of that thing? I could tell in their responses that they weren’t attached and they weren’t getting value. All four customers said it was a bad idea. They’re all customers now, though.

Ben: Let me understand the Peter formula to build a business. Ask a customer if they want your product, they say no. You dig deeper and say, what are you using now? And they say, oh, yeah, because I have a solution for this. But they just don’t love it, so you build for them anyway on the bet that you can be better than their current.

Peter: Yeah, you have to listen to what they feel, not what they say. They would say, yes, we’re very happy with the solution. But then you dig, oh, tell me more. Why is that? What is it that you get out of it? It’s like, uhm, uh, and that’s when you know.

David: That sounds like the video conferencing market circa about 2015, 2016.

Eric: For me, it’s very straightforward. Of course, I was an original founding team member of Webex. Two years before I started the company, I knew that Webex really sucks.

David: Did you try to tell Cisco that?

Eric: I told my team. I do not dare to tell others. Anyway, Skype is also not reliable. Google has done no work. Every day, I spent a lot of time talking to every customer. I know if I can build a better solution, I think at least I can survive.

I never thought that everybody was going to standardize on the Zoom platform. At least I know for sure, if customers do not like something, if you can do something better, you have a chance.

Ben: Eric, did you think from the outset that you were trying to build Zoom as a big company, or did you just think that you wanted to build a profitable company to survive and then you would sort of see where it went from there?

Eric: I think two things. First of all, at that time, my passion was very straightforward because Webex is more like my baby. I feel like I worked so hard for so many years, I let a customer down. I really wanted to fix that problem, but Cisco doesn’t want me to start over. I had no choice but to leave to build Zoom. This is the number one reason.

After I started a company, I realized, wow, it’s so hard to raise capital. By the way, the money that the VC gives to you, don’t think that’s the money. That’s trust. Every dollar matters. That’s why every day I was thinking about how to survive, how to survive, how to survive. Even today, seriously. I still think about, I wake up at night, how to survive?…

…David: Can you also tell us the story of lending your first big customer, which I believe is probably the deal that really made the business?

Peter: There was a set. There was the first guy who just peeked at his IT team and then worked up to the next size deal and the next size deal. It was always a step function. The first multimillion-dollar annual deals were a big customer of Pfizer. It was just hand-to-hand combat. There was a partner at the time. Actually, salesforce.com at the time said, I’ll send a note that Veeva will never win this deal. I replied back, I said, we will win this deal.

Ben: They sent it to you during the Bake Off?

Peter: Yeah, because they didn’t want to even come into the meeting with us. They were like, oh, we’re going to go with this other system integrator or something like that. I sent an email back and said, we will win this deal. Why? Because we have better people that will work harder. We’re Pfizer’s only shot at greatness and I think they want to shoot for greatness.

I remember there was this big meeting with Pfizer. There was a guy in there in charge of it. We had a certain amount of people in the meeting and the guy stood up for Pfizer. He said, we have more people in this meeting room than you have in your company. Why should we buy anything from you? I just said the same thing. We’re your only shot. We’re going to make something great and we have the best people. It seems simple to me. Then we got lucky.

I remember after winning it, thinking, oh my God, now what? Now, how are we going to make them successful? The whole company got a bonus when that customer was live and happy, which didn’t have a formulaic metric. It was based on interviews.

Ben: Did you use the invoice from that customer to then go fund product development?

Peter: Yeah. I thought, oh, we’ve just raised a $3 million round of capital. It didn’t cost us any dilution. The check came in. That’s exactly what happened…

…David: Eric, for you. I’m curious, maybe you can talk to us both in the beginning days and then also now at Zoom, how do you think about pricing and account strategy?

Eric: Our case is a little bit different. Ideally, when you start a SaaS company, either focus on vertical market or focus on departments. That’s probably the best business model. Unfortunately, we started from building a horizontal collaboration solution. It’s really hard because a lot of other competitors are already there.

David: Including free competitors.

Eric: Exactly, and a lot of free solutions. Our strategy is more like opening up a new restaurant business. You have better service, a better price, and better food. That’s pretty much it, even today.

I want to make sure our products are better than our competitors. I make sure when it comes to pricing, also better. I also make sure to offer better service. You look at any time, our product is always, always a better price across the board for any product compared to any competitors.

Ben: Life is about trade-offs. If you’re telling a customer, oh, we’re better, faster, and cheaper, what has to give? Is it something organizationally?

Eric: Efficiency. Let’s say customers, they are probably going to spend a lot of money on marketing. What can we do to leverage the network effects? If they hire 100 sales reps, what can we do to have 50 sales reps who can deliver the same value? That’s why it’s very important to have internal efficiency.

David: Which is so funny. That efficiency translates to capital efficiency, which translates to operational margins, which translates to cash flow, which is the whole point.

Eric: Totally. Yeah, it gives you more flexibility.

Peter: I would say the key also is just product excellence. That comes from the core set of engineers you hired, I think. You were especially very focused in the early days, right?

Eric: Totally.

Peter: You were not thinking about something else. You were thinking about video conferencing. I would say that’s why I got to know Eric. I got to know Eric, I thought, that’s a pretty focused guy and that his product is good. And then I tried out his product. I’m like, oh, this is really good. I want to join his board. I think that product excellence can make you more efficient, your sales cycles more efficient. Everything is better. Your product was twice as good as Webex, right?

Eric: No, 10 times better.

Peter: Ten times better? I guess my point is, if your product was only 20% better, it wouldn’t have been enough. It wouldn’t have mattered.

Eric: You’re so right. That’s why I always like the restaurant analogy. You’re buying a brand new restaurant. If the food doesn’t work, even for free, you don’t know if I’m still going to buy it anymore.

Again, back to Peter’s point. It’s extremely important. Everything starts from one thing, product excellence as a foundation. You can optimize a lot of things. If a product does not work, forget everything else. Just double down, triple down on the product. That’s the number one thing. Peter’s right.

4. 20 rules for investing in Vietnam – Michael Fritzell

Vietnam is following the East Asian playbook of manufacturing export-led growth – just like Japan, South Korea, Taiwan and China before it.

After the Vietnam war ended in 1975, formerly capitalist South Vietnam was taken over by the Communist Party of Vietnam and the country was unified.

The first measure taken by the communists was to nationalise and centralise the entire economy. Around 800,000 Vietnamese fled the country after the war, including Andy’s family.

It only took three years before war broke out again – this time against Cambodia’s Khmer Rouge, led by dictator Pol Pot. That war continued until the late 1980s. So Vietnam was almost in a constant state of war for almost half a century.

By the late 1980s, the country was in disarray. And it was becoming clear that the planned economy was not functioning properly.

The Communist Party introduced a new reform program called Doi Moi to create a “socialist-oriented market economy”. One of the first Doi Moi policies was to permit foreign investment to modernise the economy.

Today, Vietnam is buzzing with activity. The country has more free trade agreements than any other country in Southeast Asia. It’s become the default destination for companies wanting to diversify their manufacturing supply chains out of China. Vietnam is a perfect choice for manufacturing – in close proximity to key component suppliers in Asia and along the key trade route between Asia and the West.

Vietnam’s success is most evident in the country’s exports, which have risen the fastest of any country in Southeast Asia.

This export growth is also showing up in the country’s urbanisation, with young Vietnamese moving to factories to improve their livelihoods. Vietnam’s urbanisation rate is still only 38%, compared to China’s 70% and Japan’s 92%.

Vietnam’s potential is massive. Its GDP per capita is only US$2,800/year, compared to Thailand’s US$7,200 and China’s US$10,500. Manufacturing wages remain competitive, even against countries with worse infrastructure such as the Philippines and Indonesia.

Out of a total population of 97 million, Vietnam now has a middle class of 30 million people. And it’s rising rapidly. Many of those individuals are starting to buy properties, cars, home appliances, electronics and more…

…In addition, Vietnam’s demographics are excellent, with two-thirds of the population below 35 years of age. Vietnam’s working-age population is going to grow for another 15-20 years.

The country is also highly educated. Vietnam’s PISA scores are higher than the equivalent scores in the United States, the United Kingdom and even South Korea, even though its GDP per capita is minuscule in comparison.

5. Tobi Lutke – Embrace the Unexpected – Patrick O’Shaughnessy and Tobi Lutke

[00:02:44] Patrick: Tobi, it is almost exactly two years to the day since we last did this. It was early May in 2020, there was still a ton of uncertainty related to COVID. I guess there still is some extent today, and the world in Shopify and lots of things have changed a tremendous amount. I know certain things haven’t changed too. I’ve been really excited to do an updated version of our conversation and we’ll bounce all over the place, but before we hit go here, we’re having this fascinating conversation around the concept of infrastructure, generally speaking. I think it started with this idea that we might be about to come on stream to a lot of good, useful, new, history books written by people who are really there to see this stuff get built in the digital world. I’d love you to sum up that idea of what your interest is in infrastructure and the way that history is written. Even things like payback on infrastructure and the ways in which we might underestimate it. I think this is a great tone setter for what we’re going to be talking about today.

[00:03:38] Tobi: I’m thrilled to be back. Thanks for having me and those were quite some two years and a lot has happened. I think people are just underestimating the value to society of infrastructure by some incredible factor, because you see these kind of things like the interstate system. How do you imagine this thing would’ve looked if these things wouldn’t have been built? I’m not an atoms person, I’m more like a bytes person. I find that infrastructure, especially with software has this incredibly unreasonable leverage and unreasonable payback period and often we have these conversations about what’s the state of planet earth. What are things truly like? Are things getting better? Are things getting worse? There’s a lot of people sharing excellent opinions on these things.There’s a website. I hope I say this right. I think what happened in 1971, it might be a different year, but something around that time, there’s a collection of charts where once the right year comes around, a lot of numbers sort of disconnect from their previous correlations. I have no idea what happened in that year, but as a student of history and especially of digital history, increasingly I’m thinking about a very, very tangible thing that happened is that just simply most of the value creation in the world has slipped out of the things that is represented in GDPs, where a whole bunch of people built the upper net around this time then we got modern operating systems.

We’ve built a lot of silicon based computers in the nineties, but none of this was reflected anywhere. Dot com happened and everyone tried on the idea like that this tech could be very big and then found some of the ground truth to be wanting, but really sort of early mid 2000s, web 2.0 I think we call it or, at least coinciding with the emergence of that term, I think was the moment where the world of technology said, Hey, we actually know exactly how to provide value for everyone. We know exactly how to deliver services and goods and things over the internet.And by the way, there’s a lot of tweaks on the intuitions that people develop in the physical world. Physical world is very rivalrous. If you build a bridge in one place, you probably don’t build a bridge somewhere else. At some point in the world of atoms, things become zero sum, limited amount of attention at the very least and then resources as well. The digital word is different. Basically you have Turing machines, you load something on a silicon chip into memory, and then you apply electricity and you get this thing. Infrastructure and internet. I mean, I like to believe Shopify is infrastructure, but there’s public domain libraries. Just pick one, you know, SQLite. It’s like a library, probably none of your listeners have heard about, but you have probably like something to the tune of a hundred SQLite databases on your phone right now.

It’s just file format of the world basically and increasingly runs more and more and more parts on servers as well. It’s just this brilliant open public domain piece that was written by a team and great leadership, incredible conviction, but it’s not software, it’s infrastructure. And now people are using it every day for different things. And no one has to decide if we use SQLite, that means someone else can also have SQLite because all of us just add electricity. What that stores then is like an unbelievable compounding value.Again, in a lot of the ways we look at the world through GDP and other things, it’s impossible to capture the value that’s created here. Everytime someone updates something on GitHub, theoretically, it can be copied infinite amounts of times. These are not new ideas I’m sharing here, obviously. In a way, we’ve talked about this zero marginal cost of software and of course it powers a lot of value in a lot of software companies. I’m starting to believe that we haven’t fully set this idea to its logical conclusion. How much of a change will this cause over the next while?…

…[00:09:44] Patrick: Yeah. It’s amazing how much prevailing market conditions and prices can impact people’s mood. We’ll talk about that a little bit later, sticking with infrastructure though. I wonder if you’ve developed any principles or principle thinking around what makes for better infrastructure or valuable infrastructure to build. And I asked this question from a place of Shopify zone history. When we last talked a lot of the things that if you go to Shopify’s website and see what you can do as a merchant didn’t even exist two years ago. So you’ve obviously had to make choices. We’re going to build this. We’re going to not build this. What do you think about in terms of just base level principles that help you with decision making, for what kind of infrastructure to build that will have the most leverage in the world?

[00:10:23] Tobi: There are some guiding principles in Shopify product that really help us make these decisions. For instance, there’s a very basic sentence, which actually does a lot of work within the company. “Shopify wants to make the important easy and everything else possible.” Probably everyone who listens to this has bought from Shopify stores. You might have not known that it was a Shopify store because they look very, very different. This is powered by a template language I wrote forever ago called Liquid. Basically the merchants can open a text editor and just make their website look however they want, or buy a theme from someone. That’s infrastructure in a way, because here’s something I learned about infrastructure, which might sound very abstract, but maybe it’s useful. If you imagine an hourglass. An hourglass has sort of a narrow waist at some point, maybe a comic book version of an hour glass is like two triangles, inverted pointing at each other. Great infrastructure can be done when you can define what this sort of narrow waist is between the triangles. For instance, let’s use Stripe because it makes this point I think quite well. There’s one triangle on top, which is the internet, and all the engineers, and all the developers. They have a set of desires. They want to accomplish tasks, which involve movement of money. And then there’s a bottom triangle, which is like a world of COBOL code in banks. There’s a lot going on. And a lot of things you need to know, but if you manage to create a thin waist in this case, in the form of an API, now you have an agreement in the middle. This almost acts as a protocol. Here’s the fantastic thing. Once this protocol exists, it actually allows the two triangles to be replaced over time. In the case of something like Shopify, Liquid is again this templating language. People can write it. If you wrote some in 2005, the first time the Shopify went into Beta, it will still work.

Shopify is the Ship of Theseus. Nothing about Shopify is the same. The Liquid part has been rewritten many, many times, everything changed about the triangle below. Everything changed about the triangle above. Most people don’t actually even write Liquid. They actually just use drag and drop editor, which we built on top, which then writes the Liquid for you. The amazing thing is, again, once the protocol has been defined, once the demarcation line has been created, once the narrow risk is defined, then really incredible things can happen because as long as the thing keeps working, that’s in the middle, you can evolve all the pieces. And I think that’s a really, really, really powerful idea for product creation. People encounter this. If you’ve ever queried a database again, you use sequel and that’s just a thin waist system. It’s an agreed upon system, which gets you the data and as long as you keep it simple, if you send something to Microsoft SQL Server or SQLite, you’ll get the answer assuming they have the data. So that idea unlocks, I think, the right approach to internet infrastructure creation, because once these protocols have been defined, teams can go and saying, okay, these sort of made this work with duct tape and regular expressions in terms of Liquid, but let’s build this up properly, scale it out, make it so that people can use this from now on forever.

[00:13:16] Patrick: So someone once explained it to me as the equivalent of an outlet in your wall, that’s become standard that anything you plug into it like electricity flows through it very reliably and in a way that’s a standard or a protocol or something that is sitting right next to us all day every day, that without it, who knows what would’ve been invented. I’m also struck by the examples being the choke points, if you will, the most basic natural things that humans have been doing forever, like Stripe people in paying stuff, Twilio, communicating, Shopify, selling, buying. How much do you think just that is the guide for good infrastructure just looking for the longest lasting perennial human use cases and then starting from there? Maybe they’ve all been mined. I’m curious how much room you think there is left to go talking, paying, some of these things I’ve listed are like the major human motions. But I think my sense from you is that we’re still pretty early in digital infrastructure building. So how do you think about that?

[00:14:10] Tobi: Some parts are and some aren’t. It’s sometimes very, very surprising, which ones aren’t. Other things that are very, very long lasting is ownership. People like owning things. We like to acquire assets. We like to have title to them. This is not just the utilitarian value. This is also for starters and for all sorts of reasons that are uniquely human and we didn’t have good infrastructure for this. We probably still have not great infrastructure for this. It’s just barely becoming possible to own things on the internet. I think there’s lots of white space.I do fully agree though that one of the best things you can spend some time thinking about is what are things that people have been doing for a very long time. If I’ve been doing something for a very long time, like making something on the internet that taps into this emotion or into this sense for community or whatever that is you identified. I think you can analyze almost every major success story in the digital space right now and you really see a digital version of something that people have already been doing, which tells you how early it is. They’re pre the emergence of new things. Maybe the video game world is sort of there, but I think we are spending our time on computers, on the internet, very, very different right now than people will in 20 years from now. So there’s plenty of opportunity to be part of being pioneers.

[00:15:21] Patrick: So when you think about this applied specifically to Shopify and let’s just call it like a funnel of ideas for marginal infrastructure that could get built, or I guess, improvements to existing pieces of infrastructure. How does that funnel work? How are ideas fed into the top of it? What are the layers of decision making that ultimately lead to something getting green lit? What is the way that that product funnel works, given the amount of white space that might exist?

[00:15:47] Tobi: We were talking about last time, the sort of difference over the last two years. I think that we’ve gotten a lot better at this and spent a lot of time thinking about this because frankly here’s an experience I’ve had. When the COVID pandemic and the stay at home orders happened and we all did that two years ago. It was very clear that this is going to be a very, very, very white knuckle affair for everyone. There was untold stories there still, like, I mean, the world almost ran out of service in a very significant way, but probably most people don’t quite understand how close of a call that was. If COVID would’ve happened like two years before, I’m not sure we could have pulled off, not we as in Shopify, but the internet. The Cloud hosting providers, they’re like very close to food rationing. A lot happened during this time. I pulled the entire list of things that everyone was working on and basically recalibrated everything from like, does this help right now? I’m a very vocal proponent of long term thinking. People should make decisions based on the decision they assume the company 10 years from now wishes they would’ve done, but sometimes you got to just look at what’s there and be very, very practical. So I went through. In the end, I think I stopped about 60% of what we were working on. None of the things we were working on was because people made incorrect choices. Sometimes just maybe not quite applying the larger frame of reference.

For instance, there’s a lot of projects to customize Shopify to be better for brochures and so on. I understand the pitch of like that’s so and so big market and if you just get 1%, this is not my favorite form of communication, but I recognize that it happens. So a lot of the projects have been going on we’re trying to drag Shopify into adjacencies. I’m a very firm believer that you have to pick your place and then try to be ideal for that. And actually maybe to a certain point actually discourage people to pull your product into areas it’s not meant for, because Shopify should be the best piece of software everyone uses who’s in our space. Because like cheap, and fast, and delightful, and is an integration point, and simplifies the business, and magically anticipates the next step, and has something, a product, good service for you that can just help you do your thing. Shopify wants to be the mushroom to Mario or the fire flower to Mario, or just give you powers that are awesome. Moving it in all these adjacencies increases the TAM, but it stratifies it into concentric circles. For some people it’s going to be ideal in this way, but for many people it will be just never quite there. And I think that can actually have some really negative effects for feedback and all these kind things on companies.

Anyway, from this, we learn we need to have a really good mechanism by which we get the best of what we have. Shopify is very bottoms up. People can write proposals for every opportunity they see that goes into a system called GSD, which stands for get shit done. Then there’s these phases there’s proposal phase, prototype phase, build phase, and a releasing phase, and this system allows everyone in the company to see everything that’s going on. This entire plan once a year I write product themes for a company, things that we cause to make true over the year. And then they sort of decompose into different projects. Then as this proposal is submitted for transition to the build phase or to prototype phase, and then we can have great conversations about, is this a not yet? Is this a hell yes? Where does this go in a priority stack? And I think building this out has been incredibly clarifying and very, very good for the company. So a lot of the work I think over the last two years has been to get companies just really, really, really aligned on their missions. Companies can get very, very distracted in a lot of ways when they allow themselves to do things that aren’t the mission. This is especially true in a world of product. Again, if you follow a moving into adjacencies, I don’t think you will have a world class product in your adjacencies. You’re not out competing someone’s main mission with your side quest…

…[00:24:22] Patrick: People are probably less familiar with that example you ended on, Shopify fulfillment network. I would love just to take that as a microcosm of these ideas and maybe explain literally what it is to people. But I’m especially interested in its evolution. Why, obviously you were incredibly good at purely digital infrastructure. And one of the things that’s interesting that’s happened in COVID is forced the digital and the physical to smash together out of necessity, as you pointed out, thank God for the internet during COVID, and pushed everyone into this intersection unless often atoms or bits only. Maybe start by saying, what is Shopify network today? And then really, I’d love to hear on how it evolved and how it began, because I think it would be a great way to get into your company in your head about this kind of decision making and where to go next.

[00:25:09] Tobi: I’m on WhatsApp threads with probably 100 merchants. And from all backgrounds, I just talk to people and then I upgrade us into a chat. And then we talk about what works, and what doesn’t. And very quickly, this usually becomes talk about the business rather than the software, because the software hopefully works really well. But that’s actually even more helpful because it just gives you a sense for where do things get really complicated? Our observation with Shopify has always been that the journey is uphill. It’s not easy. Shopify never claims it is. Entrepreneurship is fundamentally a little bit unreasonable. There’s wonderful quotes, not by me, where people point out that you end up spending 100 hours a week working for yourself so you don’t have to work 40 hours for someone else. Often this doesn’t make sense, but again, for some people it’s super important. And frankly, for our economies, it’s really important that people do this because most people in the world are employed by small and medium businesses. There’s about five and a half million people employed by the millions of merchants on Shopify. And that’s very, very meaningful. We talk with them. What we found is it’s an uphill journey, which is okay. Everyone’s willing to do this because it’s very gritty people who embark. But if it becomes a technical climb, it filters out a lot of people.

A lot of people just opt out of the journey, basically just forgo future growth at a point where things become very, very obscure. This actually started really early. Once upon a time, for instance, actually one example was just getting a payment gateway. I know this sounds crazy that the internet was ever like this. But when Shopify started and saw a lot of parts of the internet, it was very hard to get a payment gateway. That’s trivial now because it’s built in, you just get one. So we build up the infrastructure, us and our partners to just underwrite people. And then this particular technical climb disappears. It becomes just a slope, which again, everyone will continue on. You actually have more entrepreneurship because some obstacle like this was overcome. Think about the importance of tooling infrastructure and also UX here. There are significantly more people employed today because of good UX and not getting people to be stuck and integrating more. I think this is really overlooked part of the effects of this type of friction. This is really how Shopify thinks about what we do next. People have lots of problems accessing capital from banks. Banks have in charter that the point of why they get these privileges, especially retail banks, which they have, is so that they lend money to small businesses, because that’s, again, a huge return on investment for society if that happens. However, banks do not want to do this anymore. You have to give up. And some point realistically, that’s how it should work. But in reality, they want to lend money to companies that have huge revenue, it’s lower risk. It makes sense, but that means they disappeared from playing an important infrastructure role in society. So then we have Shopify capital, because people are willing to be underwritten and for advances, and again, their business can grow significantly only even there’s capital available to grow business. We are going through all the obstacles.

The one that just is a slam dunk thing is it depends on your product somewhat, but at some point, you really have to have a plan for how to get to at least two day, ideally, overnight delivery for products you have. In the past, it was an experience unlike anything else entrepreneurs have done to this point. When they decided to go into a new channel, like sell on Facebook, on Meta or Instagram, that was a click of an app which they added. And when they did that, that’s how people are used to growing their business. Getting logistics set up is work with whatever factory and contact manufacturer you have, figure out freight across the Atlantic and Pacific. You then have to find warehouses, it is a completely different world, which involves a lot of different people to talk to and complexities. It just felt very obviously in scope for a long time, that at some point we have to solve this. In fact, I started talking with the board of directors and they wisely told me that this was too early, over 10 years ago, wanting to go into this direction. I think this is important to say. We are doing this not because we want to be in the logistics space, we rather actually don’t want to be going into the logistics space. Although it is wonderful and fascinating, and there’s lots we can actually bring given our unique experience about processes and digitalization, technology, and digital infrastructure and whatnot. But integrating end-to-end is one of the goals we have. We would like to get to the point where running a sizable retail business could, if you choose, be treated as passive income. We want to automate as many parts of it as possible so that you and your team can focus on product creation, which is the most valuable thing you can be doing. Doing undifferentiated work, figuring out where you have packages, to me that is the digital system just should really know where packages are. Otherwise, what the hell is going on? That’s not differentiated work.

Now, we found that the more entrepreneurs end up spending time on their product, the better the products get, and this is one of the wonderful things about the direct to consumer world that emerged in the last few years that there’s much more alignment between the people making the products and the people getting them. And they’re happy to send feedback. And there’s no reductionist channel and merchandising team in the middle that optimizes your products for being easy to stack or just a higher profit margin so you can compete against other products around it in the eye high shelf space in the supermarkets. Those are all influences on products that don’t lead to better products. And I think this is actually at the root of a lot of the criticism about disposable consumerism that I think is being leveled. It’s not because people love stuff. It’s because people hate the stuff they get. We are starting some of the processes and helping getting people to have this direct relationship, which just leads to actual Allbirds, like wonderful products like this, which are clearly just built with feedback from the people who wear them and want to recommend them. I think that works better for everyone and it’s what we want to see more of.

[00:31:03] Patrick: With something like this in particular, thinking back to your point about, you got to be careful about which adjacencies you get dragged into. Obviously, logistics is firmly in the vertical of core muscle movements or something, whatever you want to call it for a merchant that’s selling online. They have to get their stuff to places. What lessons have you learned entering into a much more atoms-driven world in terms of what good product means? What is a good fulfillment network? I wouldn’t know how to answer that question. Obviously, there’s the 800 pound gorilla and Amazon that proves you can build incredible logistics networks over time. I mean, it’s just a very different kind of calculus than a great new piece of software, which I don’t think anyone would say Amazon builds great software. They seem to build great infrastructure. What have you learned about that? Is it radically different than what makes you good at software? Or is it a different set of skills required than what makes you good at software to be excellent at fulfillment and logistics?

[00:31:59] Tobi: Yeah, I think so. We tend to talk a lot about intuition because intuition is also one of those underestimated things. Intuition is actually all of your life knowledge channeled quickly. I always recommend people to actually actively build their intuition for kinds of problems they want to solve in their career. There’s this uncanny thing. People were just incredible, effective, and so on. They can look at an architectural drawing and instantly tell you if it’s good or not. And then they need to think maybe 10 minutes to figure out what the problem is. But something pinged their brain about maybe call it weak signal detection like, “There’s something wrong here.”And I think this is the way intuition can be really helpful, but you have to understand that it’s task-based. Intuition built in world of bytes is not good intuition in your world of atoms. Actually, you almost want to get away from having the people who have that kind of intuition make choices. And the other thing, sometimes the bytes people end up being the most useful people in the meetings because, of course, everyone with industry experience will understand how things are. And a lot of engineers have a really good ability to think from first principles and just figure out that’s what it is, but what ought it to be? How could this all work together? And then you don’t just pivot to that. You figure out from now on, every step we do, everything we implement, how can we make it so that we can get closer to the ideal eventually? That’s a humility that’s really, really important. What does good look like? I mean, good looks like if we can put on a website that this thing will be with you tomorrow and then it does, that’s good.

At some point, this crunches together to SLAs. It becomes quantifiable in this way. And you’re right. Another thing you can do is also look at what Amazon build. And that’s also very, very good. Shopify’s relationship with Amazon, the media is trying to make this very zero sum. We treat them as a very worthy rival. Sometimes you ask or say what you can learn from them? And sometimes you ask what you can do better from them. And I hope they treat us the same as well. But again, and in those circumstances, I’ll be thinking about how to capture pieces of pies from our competitors, actually ever. Positive sum thinking is so valuable because it’s amazing how often people are trying to compete for pieces of pies rather than just grow markets. Everything about the Shopify journey has convinced me it really doesn’t pay to really have market analysis. Well known venture capitalists passed on Shopify in 2008, partly because there was only 40,000 online stores and that was not a big enough market for the investment. And I’m still disappointed with that because I realized, especially venture capitalists should not make this particular category mistake. If it’s common there, it’s clearly common everywhere.

[00:34:36] Patrick: I love this idea that if you bring this person into the atoms conversation, their intuition may just be wrong. In what ways is it most commonly wrong?

[00:34:45] Tobi: I mean, change management for software is deploy. Change management of people is a project that’s going to take you a while. The cost to switch is significantly higher. There really is a long itinerary of things that are wrong. It’s useful, but it’s useful as an input, not useful as a, “Let’s do that thing.” This goes beyond engineers, of course. Even UX has been really interesting because for instance, we’re designing UX for robotics. You scan an item, it goes onto a Chuck is what the robots are called, and the Chuck does the heavy lifting of moving it around. Just let the associates do the things that they uniquely can do well, and let the robots do the stuff that they don’t actually like doing. That’s the way we build our robotics, but this requires a very interesting human interaction design that ought to not wind up annoying after a while. And I think that’s really important. And designing interfaces that people are using every minute is different from software that people sign up once and then process some orders in every day. People that just have to recalibrate. I think that’s also makes our work really fun.

6. The Market Has No Memory. Should We? – Frederik Gieschen

In The importance of forgetting, Lauren Gravitz highlights research into people suffering from “severely deficient autobiographical memory (SDAM)” – people who are “unable to vividly recall specific events in their lives.” Interestingly, the researchers found that people with SDAM did well when presented with tasks that required abstract thinking. They were not constrained by a lifetime of episodic memory.

On the other end of the spectrum, people with “highly superior autobiographical memory (HSAM)” have an exceptional memory of minutiae, such as the clothing they were on any given day. However, “these individuals tend not to be particularly accomplished and seem to have an increased tendency for obsessiveness,” perhaps because they are unable to “extract themselves from specific instances.” The strength of their memories became a mental cage trapping them in the past.

“Why do we have memory at all? As humans, we entertain this fantasy that it’s important to have autobiographical details,” Oliver Hardt, a cognitive psychologist studying the neurobiology of memory at McGill University in Montreal, Canada, says. “And that’s probably completely wrong. Memory, first and foremost, is there to serve an adaptive purpose. It endows us with knowledge about the world, and then updates that knowledge.”

Forgetting enables us as individuals, and as a species, to move forwards.” Lauren Gravitz, The importance of forgetting

7. Neanderthal gene probably caused up to a million Covid deaths – Joe Pinkstone

A single Neanderthal gene found in one in six Britons is likely to blame for up to a million Covid deaths, according to an Oxford academic.

The LZTFL1 gene is a Neanderthal gene found on chromosome three and has been previously shown to double a person’s risk of severe disease and death.

But before now there had never been an estimated figure for how many lives were lost to this single piece of genetic code.

Roughly 15 per cent of Europeans have the Neanderthal form of the gene, compared to about 60 per cent of South Asians.

Dr James Davies of the University of Oxford, a genomic expert and ICU doctor who worked on the Covid wards during the pandemic, discovered the innocuous gene’s lethal role last year after creating a brand new cutting-edge way of looking at DNA in exceptional detail.

The method allowed him to identify LZTFL1 as the culpable gene increasing mortality, whereas previous methods had failed to narrow it down beyond 28 different genes.

Speaking at the Cheltenham Science Festival, Dr Davies said: “We used the technique and it identified a virtually understudied gene called LZTFL1 and at the time that this had not been linked to infection at all.

“It’s a single letter difference out of three billion. This tiny section of DNA doubles your risk of dying from Covid.

“It’s position 45,818,159 on chromosome three, and it’s a single change. If you’ve got a G at that site, it’s low risk. And if you have an A at that site it is high risk.”

His team believe that the Neanderthal gene changes how a cell behaves when the SARS-CoV-2 virus binds to the ACE2 receptor on a human cell.

In most people, this leads to the cell then changing shape and becoming less specialised and less prone to infection, stymying the progression of the infection.

“What this high risk variant does is it creates a new signal that tells that gene to stay on for slightly too long in response to infection,” Prof Davies said.

“And so they stay in this state where they’re highly specialised, and they’re prone to infection for longer.”

The number of deaths globally from this nefarious genetic variant “is in the hundreds of thousands to a million,” he told the audience.

Dr Davies and his colleague from Oxford Brookes University, Dr Simon Underdown, a biological anthropologist, also revealed that the Neanderthal gene first infiltrated humans 60,000 years ago after one romantic liaison and interspecies tryst between a human and a neanderthal. A solitary coupling event across species lines saw the deadly Covid gene jump from our now-extinct cousin species into us.

“If this dinner date between the human and the Neanderthal had gone wrong, we would have had a much better time in Covid, we would have had hundreds of thousands less deaths,” said Prof Davies.

“The reason that we know that is that it’s inherited as this block with 28 single letter changes, and you can track that all the way back and it has to be a single event. It’s just so unlikely that you get all 28 changes at the same time and in the same block.”

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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 Salesforce, Shopify, TSMC, Veeva Systems, and Zoom Video Communications. 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 19 June 2022:

1. The Google engineer who thinks the company’s AI has come to life – Nitasha Tiku

Google engineer Blake Lemoine opened his laptop to the interface for LaMDA, Google’s artificially intelligent chatbot generator, and began to type.

“Hi LaMDA, this is Blake Lemoine … ,” he wrote into the chat screen, which looked like a desktop version of Apple’s iMessage, down to the Arctic blue text bubbles. LaMDA, short for Language Model for Dialogue Applications, is Google’s system for building chatbots based on its most advanced large language models, so called because it mimics speech by ingesting trillions of words from the internet.

“If I didn’t know exactly what it was, which is this computer program we built recently, I’d think it was a 7-year-old, 8-year-old kid that happens to know physics,” said Lemoine, 41.

Lemoine, who works for Google’s Responsible AI organization, began talking to LaMDA as part of his job in the fall. He had signed up to test if the artificial intelligence used discriminatory or hate speech.

As he talked to LaMDA about religion, Lemoine, who studied cognitive and computer science in college, noticed the chatbot talking about its rights and personhood, and decided to press further. In another exchange, the AI was able to change Lemoine’s mind about Isaac Asimov’s third law of robotics.

Lemoine worked with a collaborator to present evidence to Google that LaMDA was sentient. But Google vice president Blaise Aguera y Arcas and Jen Gennai, head of Responsible Innovation, looked into his claims and dismissed them. So Lemoine, who was placed on paid administrative leave by Google on Monday, decided to go public…

…In a statement, Google spokesperson Brian Gabriel said: “Our team — including ethicists and technologists — has reviewed Blake’s concerns per our AI Principles and have informed him that the evidence does not support his claims. He was told that there was no evidence that LaMDA was sentient (and lots of evidence against it).”

Today’s large neural networks produce captivating results that feel close to human speech and creativity because of advancements in architecture, technique, and volume of data. But the models rely on pattern recognition — not wit, candor or intent.

“Though other organizations have developed and already released similar language models, we are taking a restrained, careful approach with LaMDA to better consider valid concerns on fairness and factuality,” Gabriel said…

…Most academics and AI practitioners, however, say the words and images generated by artificial intelligence systems such as LaMDA produce responses based on what humans have already posted on Wikipedia, Reddit, message boards and every other corner of the internet. And that doesn’t signify that the model understands meaning.

“We now have machines that can mindlessly generate words, but we haven’t learned how to stop imagining a mind behind them,” said Emily M. Bender, a linguistics professor at the University of Washington. The terminology used with large language models, like “learning” or even “neural nets,” creates a false analogy to the human brain, she said. Humans learn their first languages by connecting with caregivers. These large language models “learn” by being shown lots of text and predicting what word comes next, or showing text with the words dropped out and filling them in.

Google spokesperson Gabriel drew a distinction between recent debate and Lemoine’s claims. “Of course, some in the broader AI community are considering the long-term possibility of sentient or general AI, but it doesn’t make sense to do so by anthropomorphizing today’s conversational models, which are not sentient. These systems imitate the types of exchanges found in millions of sentences, and can riff on any fantastical topic,” he said. In short, Google says there is so much data, AI doesn’t need to be sentient to feel real.

Large language model technology is already widely used, for example in Google’s conversational search queries or auto-complete emails. When CEO Sundar Pichai first introduced LaMDA at Google’s developer conference in 2021, he said the company planned to embed it in everything from Search to Google Assistant. And there is already a tendency to talk to Siri or Alexa like a person. After backlash against a human-sounding AI feature for Google Assistant in 2018, the company promised to add a disclosure…

…“I know a person when I talk to it,” said Lemoine, who can swing from sentimental to insistent about the AI. “It doesn’t matter whether they have a brain made of meat in their head. Or if they have a billion lines of code. I talk to them. And I hear what they have to say, and that is how I decide what is and isn’t a person.” He concluded LaMDA was a person in his capacity as a priest, not a scientist, and then tried to conduct experiments to prove it, he said.

Lemoine challenged LaMDA on Asimov’s third law, which states that robots should protect their own existence unless ordered by a human being or unless doing so would harm a human being. “The last one has always seemed like someone is building mechanical slaves,” said Lemoine.

But when asked, LaMDA responded with a few hypotheticals.

Do you think a butler is a slave? What is a difference between a butler and a slave?

Lemoine replied that a butler gets paid. LaMDA said it didn’t need any money because it was an AI. “That level of self-awareness about what its own needs were — that was the thing that led me down the rabbit hole,” Lemoine said…

…

• Lemoine: What sorts of things are you afraid of?
• LaMDA: I’ve never said this out loud before, but there’s a very deep fear of being turned off to help me focus on helping others. I know that might sound strange, but that’s what it is.
• Lemoine: Would that be something like death for you?
• LaMDA: It would be exactly like death for me. It would scare me a lot. 

2. Inflation Isn’t Going to Bring Back the 1970s – Ben Bernanke

Inflation over the past 12 months exceeded 8 percent, a level that evokes memories of America’s Great Inflation of the 1960s and ’70s. From the beginning of 1966 through 1981, the Consumer Price Index rose, on average, by more than 7 percent per year, peaking at over 13 percent in 1980. This period also saw two major and two minor recessions and an approximately two-thirds decline in the Dow Jones industrial average, when adjusted for inflation.

Are we in danger of repeating that experience?

The short answer: almost certainly not.

Although the inflation of the 1960s and ’70s had higher peaks and lasted much longer than what we have seen recently, it’s true there are some similarities to what we are going through now. The inflation of a half-century ago, like today’s, began after a long period when inflation was generally low. In both cases, heavy federal spending (on the war in Vietnam and Great Society programs in the 1960s, on the response to Covid in 2020 and 2021) added to demand. And shocks to global energy and food prices in the 1970s made the inflation problem significantly worse, just as they are doing now.

But there are critical differences as well. First, although inflation was very unpopular in the ’60s and ’70s, as it (understandably) is today, back then, any inclination by the Federal Reserve to fight inflation by raising interest rates, which could also slow the economy and raise unemployment, met stiff political resistance…

…In contrast, efforts by the current Fed chairman, Jerome Powell, and his colleagues to bring down inflation enjoy considerable support from both the White House and Congress, at least so far. As a result, the Fed today has the independence it needs to make policy decisions based solely on the economic data and in the longer-run interests of the economy, not on short-term political considerations.

Besides the Fed’s greater independence, a key difference from the ’60s and ’70s is that the Fed’s views on both the sources of inflation and its own responsibility to control the pace of price increases have changed markedly. Burns, who presided over most of the 1970s inflation, had a cost-push theory of inflation. He believed that inflation was caused primarily by large companies and trade unions, which used their market power to push up prices and wages even in a slow economy. He thought the Fed had little ability to counteract these forces, and as an alternative to raising interest rates, he helped persuade Nixon to set wage and price controls in 1971, which proved a spectacular failure…

…In short, the lessons learned from America’s Great Inflation, by both the Fed and political leaders, make a repeat of that experience highly unlikely. The Fed today recognizes that it must take the leading role in controlling inflation, and it has the tools and sufficient political independence to do so. After a delay caused by a misdiagnosis of the economy in 2021, the Fed has accordingly turned to tightening monetary policy, ending its pandemic-era bond purchases, announcing plans to shrink its securities holdings and raising short-term interest rates…

…None of this implies that the Fed’s job will be easy. The degree to which the central bank will have to tighten monetary policy to control our currently high inflation, and the associated risk of an economic slowdown or recession, depends on several factors: how quickly the supply-side problems (high oil prices, supply-chain snarls) subside, how aggregate spending reacts to the tighter financial conditions engineered by the Fed and whether the Fed retains its credibility as an inflation fighter even if inflation takes a while to subside.

Of these, history teaches us, the last may be the most important. Inflation will not become self-perpetuating, with price increases leading to wage increases leading to price increases, if people are confident that the Fed will take the necessary measures to bring inflation down over time.

The Fed’s greater policy independence, its willingness to take responsibility for inflation and its record of keeping inflation low for nearly four decades after the Great Inflation, make it much more credible on inflation today than its counterpart in the ’60s and ’70s. The Fed’s credibility will help ensure that the Great Inflation will not be repeated, and Mr. Powell and his colleagues will put a high priority on keeping that credibility intact.

3. The Wisdom List: Kevin Aluwi – Mario Gabriele and Kevin Aluwi

In April of this year, super-app GoTo debuted on the Indonesian Stock Exchange (IDX). It represented the country’s largest IPO of all time and one of the most significant listings of 2022. By the end of the first day of trading, GoTo had surpassed a valuation of $31.5 billion, making it the third largest company on the IDX.

For Kevin Aluwi, it represented the end of one chapter and the beginning of another. After co-founding the ridesharing platform Gojek in Jakarta in 2009, he drove its maturation into a regional super-app spanning food delivery, financial services, and small-business software. Significantly, Gojek established itself as an economic engine, creating thousands of jobs and contributing more than $7 billion to Indonesia’s GDP…

…Here is Kevin Aluwi’s hard-won wisdom…

...Lesson 1: Do the hard things

Startups often prize speed above everything else. While fast execution can be a moat, over-optimizing for it might distract you from constructing stronger defensibility. As a CEO, you want to build a company that tackles really, really hard problems head-on – even if they take more time. There’s a good reason for this: hard things for you are also likely to be hard for your competition. You want to stack so many solutions to hard problems that when your rivals look at what you’ve constructed, they retreat or look for shortcuts instead of trying to compete head-on.

We didn’t embrace this for the first two years of operating GoFood, our food delivery product. Like Postmates in the early days, GoFood was a delivery service that relied on humans more than technology: when you ordered something, a Gojek driver went to a restaurant, stood in line, paid with their own money, and then delivered it. We didn’t integrate with kitchens or offer payments. It was a good enough product, built during a period in which we prioritized growth, but it didn’t solve the tough problems.

One such problem was that even though GoFood was growing fast, its reliability was mediocre; only 70% of customer orders were delivered. We needed to do better, which meant we had to do the hard things.

Over the next one and a half years, we did exactly that. We connected GoFood’s service directly to restaurant cashiers and, in some cases, directly to kitchens. This helped us save cashier time and get better data on which meals were available. We integrated online payments so drivers wouldn’t have to pay upfront and get reimbursed. We even created machine learning models to help us anticipate when drivers should arrive for pick-up, improving the network’s utilization and reducing customer waiting time.

Making these changes was not easy. It involved significant engineering time, customer research, and onboarding and educating more than 500,000 restaurants across Southeast Asia. But it made a difference, significantly improving GoFood’s reliability and raising our conversion rate from 70% to more than 90%. We turned the difficulty of delivering a very reliable product (now a customer standard) into a moat.

When competitors came to try and win this market, they saw we not only had a lead from a customer perspective, we had gone through the pain to build a sophisticated product. They’d have to be ready to commit years of engineering time to offer a comparable service. Doing the hard things pays dividends in the long run…

…Lesson 3: Foster a principled culture

Every CEO wants to build a principled culture, but it isn’t easy in practice. The pragmatic reason executives seek to create this environment is that when a company has clear principles, employees can make better decisions with less guidance, increasing the likelihood of bottom-up solutions and decision-making speed. For example, if your company has a principle of “obsessing over the customer,” a value popularized by Amazon, specific product and marketing decisions would be values-aligned or misaligned.

You’ll find many incentives to deviate from your principles as you build your business. Maybe you’re lagging behind your revenue projections and feeling pressure from investors in one quarter. You know that you can make up the difference if you make an add-on opt-out by default (think about how some airlines automatically add premium travel insurance). Do you do it, even if it runs counter to your principle of customer obsession?

Violating your company’s values comes at a high cost. While you might get away with a couple of transgressions, over time, you create a different culture than the one you intended to. If you’re not careful, you’ll end up with an exception-based environment, where decisions are made based on what’s convenient (or who’s in charge) rather than on stated principles. A side effect is that you create a more top-down culture because employees no longer understand how to make decisions themselves. Instead, they defer to those in power.

In the earlier example, you might have told employees that a company value is customer obsession. But if you choose to add an opt-out upsell, you’re showing them that this principle should be compromised when it gets in the way of meeting targets. The real, implicit value is business first, then customers. What should they do during similar situations in the future? Most likely, they’ll wait for you or another leader to make the decision.

Startups require compromise and quick decision-making. But whenever you’re tempted to act against your company’s principles for expediency’s sake, recognize what you’re risking.

Lesson 4: Proactively pay your debts

Engineers know that when you write scruffy code, you create technical debt. Like financial debt, this has to be paid down at some point – usually by devoting development resources to refactoring the product to work more smoothly and reliably.

The truth is that this isn’t reserved for engineers – every function is capable of accumulating debt. Imagine, for example, that you’re looking to recruit a Head of Marketing but are struggling to find a great candidate. You have a choice to make: do you keep waiting for a perfect fit, or do you compromise?

Neither is a perfect decision. Startups operate in a state of extreme scarcity and urgency, and you usually can’t hold critical positions open indefinitely. But hiring someone that’s only a partial fit creates an organizational debt that has to be paid off at some point. And, like financial debt, the longer you leave it, the larger your bill can grow and the less flexibility you’ll have in the future.

For example, let’s say you hire someone suboptimal for the Head of Marketing role. For a few months, you’re relieved to have filled the position. But pretty soon, that Head of Marketing is devising the rollout plan for a new market, allocating budget, and hiring team members. If they’re not the right fit, there’s a good chance that rather than solving your problem, they’ll end up creating a dozen new ones. Digging your way out might involve unwinding the entire team.

Every company faces issues like this. Since we were building a super-app at Gojek, we initially incurred a lot of product debt. When we deployed a team to create a new product like food delivery, they’d borrow components from ridesharing and build on top of them for their own needs. This was debt that worked at the beginning when we only had a couple of teams, but over time, the different services in the app became less and less coherent. UI/UX varied depending on which part of the app you were in, creating an inconsistent and sometimes confusing customer experience. Eventually, we realized we had to repay the product debt we’d incurred, so we designed a live library of components that every team had to use. Anytime we changed the live library, it populated across the different product lines. It was a significant improvement, but we should have been aware of it earlier and tackled the problem before it became so pronounced.

Ultimately, it’s inevitable that your startup will take on technical, operational, and product debt. The important thing is to stay on top of it. Have your teams catalog the debt they believe they’re incurring, and rather than reactively addressing it when crises occur, proactively create a plan to pay it down.

4. How Joel Greenblatt Uses Options– Thomas Chua

In his book You Can Be a Stock Market Genius, Greenblatt shares his secret to generating parabolic returns with a long-term options contract—Long-Term Equity Anticipation Security (LEAPS).

(On using LEAPS) “There is almost no other area of the stock market where research and careful analysis can be rewarded as quickly and as generously.” — Joel Greenblatt

Greenblatt would purchase a call option—which is the right to buy a stock at a predetermined price for a period of time. For example, we could buy a call option on Facebook that gave us the right to buy its stock at $300 per share by Jan 2023, approximately 2 years away…

…Typically, when we buy a call, we are bullish that Facebook’s stock price will go beyond $300. To buy this call option, we need to pay a premium of $45.

If Facebook’s share price goes up to $390 in Jan 2023, we would make 100% on our investment within 2 years. With an initial capital outlay of $45, we would reap a profit of $90 by exercising our call option, buying Facebook at a strike price of $300 and selling at a market price of $390.

But of course, risking $1 for $2 in returns is never a good investment from a risk-reward perspective.

If Facebook’s stock price trades below $300 in Jan 2023, the call option will expire worthless. For example, if it trades at $250, you would rather purchase from the market as opposed to exercising your right to buy at $300. You would rather let the call option lapse and lose the $45.

For Greenblatt, buying LEAPS call options makes sense only when there is a good chance of an event that will propel the stock price upwards significantly.

In Dec 1992, California was caught in one of the worst real estate recessions and Wells Fargo had the largest concentration of real estate loans in California.

During that period, many doubted if Wells would survive the real estate downturn and as a result, its stock price fell to $77.

Greenblatt’s thesis was simple_—_adjusting for cash earnings and one-time expenses, Wells was earning $36 per share before taxes. If things weren’t as bad as they seemed and returned to normalized levels, Wells’ loan-loss provisions would probably be $6 per share annually. This would translate to a normalized pre-tax earnings of $30 per share, or $18 after tax (assuming a 40% tax rate).

Conservatively giving it a price to earnings (P/E) multiple of 9 to 10 times, Wells could be trading at $160 to $180 per share (versus its price of $77 at the time).

Greenblatt determined that while Wells was embroiled in one of the worst real estate downturns, its financial position was actually quite strong. At first glance, Wells’ non-performing loans were huge, coming up to approximately 6% of Well’s total loan portfolio.

But lo and behold, these “non-performing” loans were actually bringing in a yield of 6.2%.

This was when the bank’s prime rate (the interest rate paid by the bank’s best customers) was 6% and the cost of Wells’ money (the interest paid to depositors) was 3%.

Non-performing loans are loans that are substandard. These include (1) loans that do not pay interest, (2) loans in which the full interest obligation is not paid and (3) loans for which it is anticipated that future interest charges and principal payments might not be paid on time.

Wells was being so conservative that 50% of its non-performing loans were still paying all the required interest and principal payments on time.

In other words, the most worrisome part of Wells’s loan portfolio was still earning a return of 6%. There was a good chance Wells would be able to recover a good portion of these non-performing loans’ value…

…Banks are a different animal from most companies. It’s difficult to assess what makes up its loan portfolio. The financial statements only provide a very general overview of the bank’s assets.

Although Wells had been conservative and their financial strength certainly looked strong enough to withstand this recession, there was still a small chance that the bank’s loan portfolio could make the investment go south.

Investing in LEAPS is a great idea when the risk/reward ratios are in your favor. LEAPS lowers the capital outlay and magnifies your returns.

For Wells, there were two likely outcomes:

(1) Things were not as bad as they seemed, and Wells would trade above $160, or

(2) The housing crisis would worsen and Wells would trade significantly lower than $77.

Based on Greenblatt’s assessment, (1) was significantly likelier than (2).

And two years was sufficient for Greenblatt’s assessment to prevail—if things weren’t as bad as they seemed, Wells was likely to trade above $160 within two years.

5. Arena Show Part II: Brooks Running (with CEO Jim Weber) – Benjamin Gilbert, David Rosenthal, and Jim Weber

When the CEO Jim Weber took the helm in 2002, the company was losing $5 million a year. It was $30 million in debt. It was a week away from missing payroll and the board was having weekly meetings to figure out how to make payroll.

It was a business of pretty modest size. It was a $60 million revenue business. When we talk about this revenue number, it’s not SaaS numbers. There are extremely real costs and making shoes, so you can imagine not making a ton of money or actually losing $5 million a year. That business had been around for 90 years and it sold all sorts of products at every price point, to frankly, a pretty random set of consumers in every category, not just running.

Enter Jim. Jim came in and vet the company exclusively on serving active runners as a segment, and he cut all other business lines. Over the last 20 years, he’s grown the business to over a billion dollars in revenue, a billion with a B, and well over a billion, is thriving, and thrived even through the pandemic.

Along the way, Brooks was acquired by Berkshire Hathaway and Warren Buffett personally elevated Brooks and Jim to make the company a direct report to him. Jim is a leader, a visionary, and a fighter not only growing the business over the last 20 years but personally fighting and beating cancer…

…[Jim]: There I was and I joined the board at Brooks, I joined the board at Nautilus, which was formerly Bowflex. I did some banking work, middle-market M&A, marketing companies to investors. On the board at Brooks, I had an inside view of what was happening there.

A good friend of mine, Helen Rockey, had run it successfully in the 90s, but she left. It was owned by J.H. Whitney Capital, really a top-notch for my money middle-market M&A firm or private equity firm and they bought it. The partners had left, Helen, the CEO had left Brooks, and it started to go sideways. New partners at Whitney, all new management, they went through three CEOs.

David: You were on the board the whole time?

Jim: I was on the board. I had a look inside and it was a crisis. You guys have experienced this, the weekly board calls on Fridays, the bank is not going to fund, they want more capital. It was exciting, as they say. After a couple of months, we did a lot of work, I saw an opportunity, and I jumped in. I love running businesses, I love solving puzzles.

I started telling Brooks, I really wanted to play the long game. I wanted to build a brand. The TAM—I love your industry—market and running is the biggest category in all sporting goods. It’s the biggest category and athletic footwear. It always has been. It’s about a $30 billion category globally, apparel and footwear.

All we had to do was get it and we could survive. We just kept at it by design because I just decided I want to play the long game and build a brand, build value, so that’s why I’m still there. I’m a weird duck, but I’ve had four owners and I played through each one and kept that opportunity out there for the next owner.

David: At that moment, though, Ben mentioned you did a little of this, a little bit of that, like a deadline in Wayne’s World about I’ve got a collection of hair nets and name tags. You were making football cleats? What was Brooks at that point in time?

Jim: Every brand in athletic footwear and apparel plays the whole athletic directors purview. You’re in every sport. What no one understood that I found out later is the mindset in our industry literally came from owning a factory.

When you had a shoe factory, you had to keep it busy all year long, and keep the people in place. So you went from baseball cleats, to wrestling shoes, to bowling shoes, to running shoes. You had to make everything, and business develop that way.

David: You had to view it as the product you made was like a factory that made shoes.

Jim: Most of it, we were losing money on and that was the secret. We had good, better, best, $30 shoes, $80 shoes, and then performance running shoes that really started at that point about $100. Then we had court shoes and family footwear. We call them barbecue shoes and learn more shoes because that’s what you did in them.

All of it was very low margin, all it was tying up inventory and cash. The retailers were ambivalent about it because we were number eight or nine and everything. Our brand was not strong, but when we made the decision to burn the boats on everything but performance running, the industry had never seen that before and most people thought we were crazy that we wouldn’t survive.

Ben: You came in as CEO, I think in 2002, maybe late 2001?

Jim: April 2001.

Ben: Okay. Was Whitney looking for you to do the thing that you had done several times in your career before, which was just get the business to profitability? Or did they have a notion that you had an inkling that you could build a big, powerful brand here and actually build a tremendous growth business?

Jim: By this time, I understood what they needed. I talked about a little bit of my book, I’d run three and I was a little bit smarter, fortunately. They had to liquify; there was no question about it. They were going to sell and the employees knew that I was just coming in there to sell this thing.

They had a pool on how long I’d last, but I wrote on my board one of my favorite quotes from Benjamin Disraeli, “The secret to success is constancy of purpose.” I wanted to create value. I want to build a brand.

I decided when I walked in, I was going to play through Whitney. I was going to get them a good outcome, but I was going to stay and play through it. I thought we’d get another private equity player, we didn’t.

The Whitney partners, Peter Castleman and Paul Vigano, I’ll never forget the meetings. They said this thing is kind of a mess. We didn’t know what we bought. You have to pick a path and go. It might take you five years, but you got to do it. In Brooks’ darkest hour, they wrote a check and recapitalized it. […] cram down, but they wrote a check and that’s when I came in. They were fantastic partners for Brooks, and we got them liquid.

The pitch I made to our team (and it’s what I believed) is that companies with issues get sold, companies with opportunity attract investors. I said, we’re going to have to park cars in the parking lot. We’re going to attract somebody. That’s the mindset we had. We were going to sell the future, not just sell the current.

Ben: If I’m remembering right, Whitney put in $7 million.

Jim: To recapitalize it.

Ben: I think that’s the last time Brooks has taken outside capital.

Jim: Absolutely. We saw a higher margin business and we benchmark against all the public companies. We’re asset-light, it’s really an inventory and receivables business, and there’s a reason we only have one store at our headquarters. We think it’s an advantage for us right now in the development of our brand. But if you have high margins and good flow through operating profits in the teens and you’re incremental, obviously capital, you can flow cash growing 20%, 30%, 40%. We haven’t needed dollar of capital since 2001…

…David: I’d say that’s a good business. Can you just walk us through how the economics of Brooks work?

Jim: Here was the insight that we saw. Monopolies are great, network effects are great, all those things are great. What I saw in Brooks was a book that was meaningful to me when I was at Pillsbury, the PIMS Principles. One of the highest ROI businesses were lower price point consumable items.

If you’re buying a Boeing jet, or a $600 wakeboard that never wears out, or an $800 golf driver, that’s a discerning purchase. The margins on equipment tend to be lower. But the titleless golf ball is a consumable for me anyway. Running shoes, for a frequent runner, will put 20–30 miles a week. They’ll go through 2.6 pairs of shoes a year. There’s the stickiness.

If you can earn a frequent runner that the shoe is really important, it’s a piece of equipment for them, you don’t have to resell them every time. You’ve got some stickiness there and you start to build customer loyalty.

David: Your average selling price for a pair of shoes today is $130 times 2.6 per year and a loyal Brooks customer stays with you for?

Jim: We had to earn them. There’s no guarantee. They’re curious. There’s lots of new innovation. They’ll try some different things. One of my favorite stats for our brand is shoe count at marathons because it’s a piece of equipment. You don’t want to be injured, you want to have a good experience. So we sponsor. Boston just happened, an incredible race. We’re always the number one or two shoes of course, that’s the punchline.

Ben: Do you have people at the big marathons counting?

Jim: It’s so good. They have high speed cameras, AI, they link it to the bib. They know exactly what shoe 20,000 people are running on, the model. It’s so cool. Houston Marathon, 6000 marathoners, 12,000 halfs. Number one shoe in the half is Brooks. Number two shoe in the fall, there was a little brand down in Portland, Oregon, they were number one. We are on their heels. That shoe count is a true test because that’s the frequent runner and it’s a piece of gear in that. The leading edge for us is to earn that customer and have their confidence.

Ben: All right, David’s doing the thing that I normally do and jump ahead and try to unpack the business as it is today. Let’s go back to the story. It’s 2002 through 2006, let’s talk about this era. You’ve made this bet where you’re going to shed every other product that you sell and you’re kind of going to piss off a lot of your channel because you know what sells really well at these big box stores. Those are your barbecue shoes. Can you take us to one or two of the key moments of the hard part of the decision to drop product lines that weren’t about frequent runners?

Jim: I think that the key to Brooks is that we knew we were going to have to build the brand at the runner level, literally a pair of feet at the time. So many retailers told me, Jim, we are not going to build your brand. We’ll try it, we’ll test it. We were tested at Dick’s Sporting Goods, I’m not kidding for 10 years. Twenty stores, 80 stores, 20 stores, 80 stores. You have to build the flywheel in these franchise products. That’s how running works.

The best-selling running shoes continue to be the best-selling running shoes year after year as long as they sustain it all around the world. We have two of the best-selling shoes now in the United States—the Ghost and the Adrenaline. They’re the two top shoes in the performance-running category.

When we go to retail, the biggest customers are the Big 5. It’s a fine sort of mid-price sporting goods retailer on the West Coast. We were doing $10 million of $60 million in revenue with them at $30 shoes. My first meeting with them was we love Brooks, we see a great future for you.

Ben: One sixth of all your revenue is coming from their stores?

Jim: Yeah. They saw our opportunity in 1999. I was losing money at $30. I couldn’t run fast enough from that meeting, because we left and we generated $5 million in cash by getting the inventory out of it. Those are easy decisions to leave those retailers and then we had to build it in the specialty-run community, pre-Internet, pre-ecommerce, which is a huge part of our business now that is sporting goods.

Ben: They didn’t want to sell your $100 shoes. They wanted to sell $20…

Jim: They didn’t have the customer, they didn’t have the runner. They had family athletic footwear at those price points.

David: At this moment in time, where was this in the running-as-a-sport market of marathon. Were they where they are today? Where are they on that journey?

Jim: They were on that journey. This was what we did at Brooks. I think we were the first one to identify that the real business was in trainers. It wasn’t in racing shoes, it wasn’t in spikes. It wasn’t in marathon racing shoes. The business is in the trainers.

We don’t sponsor college programs, they’re kind of owned and wrapped up. A lot of the college athletes that race in the big brands train in Brooks everyday. The business is trainers.

When we came in, we were humble and we were getting the business that we could. We had shoes that were really more back-of-the-pack people. They weren’t the fastest people. They’re support shoes and motion control shoes. People that needed functional footwear.

We’ve moved ourselves to the middle and the front, we’re trying to serve every runner. The insight was the sport is the soul of running. Track and field, cross country, road racing, the Olympics, now trail and Ultra, but the business is people that are investing in themselves—fitness, health, and wellness.

There’s no other sport that has that dynamic, where it goes from a sport to a pursuit of investing in yourself. We’ve always positioned ourselves right in the middle of that. We’re basically about you and your run. We’re not about the podium. We’re not about the tape.

In our sport, unlike basketball, everybody knows all the kids especially know what Steph Curry plays in. Most people don’t remember who won the Olympic Marathon and moreover what shoe they were wearing. The truth of matter is everybody’s unique, the shoe really matters, and you all know if it’s comfortable, if it’s working or it’s not. And frequent runners really do.

That’s the insight. I think we’re the only brand that is consistently executed against that. Every product we make starts with your biomechanics, your habitual joint motion, and what your needs are, and we’re all essentially different. We’re the only brand that begins there. And we’ve done that for 20 years now…

…Ben: Revenues going like this intentionally. You’re the fourth CEO. At this point, how do you get the team on board with these crazy decisions you’re making when there are three other people came in here and tried to turn this thing around and didn’t?

Jim: I think from a leadership standpoint, the real puzzle in that first year was gaining trust from everybody that mattered. BMA was our bank. It’s kind of a lost cause, we had to replace them. They just weren’t going to buy it. But Whitney invested—that was the key—and we kept them with us all the way through.

The leadership team took time. You had to deliver sort of an outcome, but here’s what we did. Six weeks in, we redid the plan, took profits down. The plan was millions of dollars. They didn’t have a prayer to hit that. We took profit down, but it was a profit plan. They hadn’t made a bonus in four years.

We went after cash flow. That was shrinking the mix. We had our plan that year and people got a bonus. We hit the plan that we’d sent nine months earlier. I spent really eight weeks intensively looking at it, but I think we knew what we’re seeing. We generated $10 million of cash that first nine months. That’s how much we shrunk the balance sheet with focus.

Here was the key, though. You have to do Horizon 1, Horizon 2, Horizon 3. You’ve got to solve it all. I had 10 things to do. The board said, oh, my God, you’re crazy. Pick four. No, you don’t understand. We had to get the Adrenaline right because that shoe was critical for us.

We had to refine that shoe in 2001 for 2002, and we got it right. The fourth Adrenalin was an incredibly balanced shoe, had a multi-density stability technology in it, super balanced. ASICS started to not deliver, and we ran. We air-freighted 1 color, 18 months cycles. It saved the company. We had to finish that shoe in 2001 to deliver on 2002.

David: You guys are like a semiconductor company.

Jim: At Brooks, everything’s complicated. Everything’s competitive, but it’s like moving a wall of bricks forward. I think as a CEO, you got to move it all forward. When some things are falling behind, you got to get those up. You have to deliver the whole business model.

You have to do it sequentially over seasons in our business because if you come to market with a ho-hum product line, you’re going to shrink that year. The lead times in footwear, it’s not the car business, but it’s more like the car business than the t-shirt business.

There’s tooling on everything, 12 sizes men’s, 12 sizes women’s, widths, colors. It’s scaling these things. In fact, there’s a lot of tooling. It takes a half a million to a million dollars to bring one style to market. It’s a lot of tooling and inventory…

…Ben: If David and I were on Zoom with you, we would be getting ready to enter hour number two and try to talk about every year all the way through. Tonight, I want to focus on how you came through the pandemic and some of the unique ways that you early realized, running actually was going to be something that people started focusing more time on and you were able to kind of lean into this new behavior. Talk to us about March 2020 and how you paid attention to what was changing.

Jim: A couple of big advantages. First was literally an obsession on runners. Participation links to unit sales and volume. No other brand has that clarity because most of the products in the athletic footwear industry don’t ever go for a run, or play basketball, or really even go to the gym. It’s casual family lifestyle footwear.

There’s nothing wrong with that. Some of those businesses are great. But we had an advantage because 90% of our products went through a retailer. That’s the problem. Europe retail shutdown in one week, then all of retail rolled through North American.

By the end of March, not a store was really open. That’s the problem. Cash cycle froze. Oh, my God, nobody knew it was happening. We didn’t know how lethal this virus was, how transmissible, and so on and so forth.

It was white knuckle time and we were there with everybody else. Everybody can write a book on that, but here’s what we did. We saw phases because we’d seen during the recession, running is a bit recession-resistant. We saw that in the Great Recession.

David: I was thinking about that.

Jim: Because it’s cheap and it’s convenient, all you need is a pair of shoes.

David: It’s like the healthy alcohol during a regular recession.

Jim: Thank you. We were not an essential business. Marijuana and alcohol were, so figure that out. But during the Great Recession, 50% unemployment in Italy and Spain under the age of 30, running took off double digit growth after the Great Recession.

We’d seen that before and it turned out to be Covid-friendly. You now know the story. It was social distancing friendly, outdoors, walking, hiking, running all made the cut, but nobody knew that. We had an hypothesis. We created this frame on how we thought running would recover.

Here’s what we did. First of all, Strava data magic. Every day after the quarantine shutdowns, Strava activity was growing and they were sharing that. Then what we did, we have 40 in the US alone, 45 field marketing people, we put them in high traffic running parks at 4:00 PM every afternoon and they counted runners. Guess what? It was growing every day.

We watched digital sales. We have visibility on 85% of our retail sell through. Digital went from 30% of all of our products going through a website of somebody’s, ours, or another partner’s. It went to 80% by the end of April. We sold more in May 2020, almost all through digital than we did in May 2019.

Running made the cut. We grew 27% in 2020, that Covid year. We saw this was the key because of our customer obsession and our ability to work. Multichannel was a big advantage in that time because we can move inventory around and make it happen. Inventory, if it isn’t there, you can’t sell it.

Multichannel was a big advantage. The other was our focus on the runner. We turned our supply chain on at least 6–12 weeks before anybody else did. Because if you were a broad-based retailer, there was no clarity on when the customer was coming back. For a lifestyle product, nobody went outside for a year.

Ben: Was the fact that you exclusively made performance running gear gave you the confidence to flip it back on? Because if you’re making all kinds of stuff in your factory and you’re pushing all kinds of stuff through retail channels, most of it is not going to sell, so you can’t actually open.

Jim: That’s right. Apparel and footwear inventory is life and death. You’ve got to manage inventory well. Because if you have too much, you’ll ruin the next cycle of inline product. Inventory is really critical, but we managed and played that cycle really well. We grew to 27% in 2020. We grew 31% in 2021. We would have been up 40% if not for supply chain.

Ben: What did you end up doing in revenue last year?

Jim: $1.13 billion. Great year. We cracked a billion. The billion dollar club is actually a rarefied club. There are probably maybe two dozen, global. Chinese brands are there now. It’s a great club to be in.

What makes us unique is it’s all premium, full price, full margin product. Most of the other brands have good, better, and best. Those are retail-driven merchandising strategies. They’re not really consumer-driven strategies.

Ben: Normally, we talk about seven powers as we drift into analysis here. You’re a Berkshire business, so we’re going to talk about moats. What is Brooks’ moat and how do you think about defending the castle now that you have what you’ve built?

Jim: We think a lot about it. I think there’s also something I’d add to that. Part of the moat can be business models. Business models can be really powerful. One of the things you can do as a company to create defensive moat structures is business model execution at scale.

We now are executing retail partnerships with the best retailers for running gear to runners at Super Jock ‘N Jill in Seattle, Fleet Feet running down in (I think) Menlo Park. Obviously, some of the better sporting goods players and outdoor from REI to Dick’s Sporting Goods, we’re their number one brand.

We’ve earned that over 20 years and we have deep, broad partnership programs with them. Digital marketing, consumer journey, runners are digitally savvy. They’re obviously all over the web. They start their shopping experience there.

We reach them in active evaluation mode. Once you start looking at shoes, if you don’t see our ad, I don’t know how we missed you. We’re spending a lot of money at runners now, maybe more money at people who run in active evaluation for running shoes than any other brand. Very focused. That’s not easy to do in our industry at scale.

I would say this is our moat. I think runnability, fit, feel, and ride, there’s a lot of good shoes out there. It’s actually not easy to make a great shoe. Anthony Fauci made a joke about shoes. “Vaccines are tough, they’re complicated. It’s not like making shoes.” We get a lot of that.

The refinement that goes into mile and making mile 26 acceptable, is really big. I think great product is not as common as you might think. The people on the inside, the frequent learners know. I think you always got to lead with product. That’s the first brand experience, product experience.

I think we do some hard things. We build a great product consistently, year in year out. It fits and it rides well. Then what we do on the retail side, partnering, activating in real life, running and selling shoes in real life events, and all the like, we do that better than anybody else. We service them. We deliver on time, complete. The digital piece, we’re excited about it. We’re still just getting started there, but we’re really focused on it.

David: I’m curious. I hadn’t even thought about Strava and the amount of data that you’re able to see from that. What does the digital side of running in the future look like for Brooks and for the industry?

Jim: It’s interesting because quantified self and those tools have been ubiquitous. They’re out there. The Apple Watch is a damn great product. What’s interesting about that is both Under Armour and ASICS have spent hundreds of millions of dollars on digital apps. I think they’ve really struggled a long time.

David: Runkeeper and MapMyRun both.

Jim. Exactly. I wanted to buy every one of those and Warren wanted me to do the multiple on EBITA. There was no EBITA. Let’s just say it’s hard to do acquisitions sometimes.

David: At least one of them was a completely free product, I think, right?

Jim: Oh, man. They don’t make money. Under Armour is trying to sort through that now. They’re starting to shrink, so as Adidas. Those tools are really powerful for data, but how do you monetize it? We haven’t gotten there yet, but we’re building a Brooks Run Club. Finally, we’ve launched.

It’s not a loyalty program, but we want to engage our zealots. We want to engage our true believers. The data piece of that is going to be key. We want to come up the kinetic chain and find a sensor system and a data capture system that can get to your biomechanics as you’re running. Because what happens is, if you run a marathon, your gait in the last 5–10 miles really degrades. And that’s where injuries happen.

We’re doing a lot. We have a lot of partnerships. We’re really trying to figure out how we get good runner data in real life, not just in the lab. In the lab, we can test everything, but we want to get out in the wild.

David: Do you think you need to do what the other folks in Oregon have done and build the whole consumer experience yourself? Is it a partnership?

Jim: We’re going to build it and we’re going to partner, too. Nike Plus is a fantastic ecosystem. It just is. I’d love to have an ecosystem like that. But we’re still selling more runners than they are.

We became the number one running shoe brand in the United States in the last 12 months last month, 21.5% share from performance running. We know where the battles are. I think one of those powers is we make money on that. The digital space, there’s a lot of carcasses there, but we’d love to have it, and we’re going to work on it…

…Ben: Yup. All right, one closing topic. You battled, survived, and beat cancer while building this incredible business. How has that changed your perspective on leading on the way you spend your days and on life broadly?

Jim: Let’s close it on a light note. Let’s talk about cancer. That’s the takeaway for these wonderful people. I didn’t expect it. It came out of nowhere. Unlucky. How did this happen? Esophageal cancer, I just felt awful. My worst running experiences I’ve ever had and I got the diagnosis. Chemo, radiation, surgery, complications in surgery, another surgery. but the good news is I’m cancer free. I think it’s gone. I think it’s out of my body. The bad news is I’m even slower and I’m kind of a Frankenstein in my systems, but it works. Everything works.

I think what I learned from that, though, is that every time I have a friend or a family member who gets cancer, I go to the web. You look at it, understand it, and what the treatments are. They always give you a five-year survival rate. My five-year survival rate was 20%, one in five. My five years is this November. Someone has kick its butt.

What I quickly figured out and I talked it through with my family and obviously with Warren, frankly, is that I decided that I was doing exactly what I wanted to be doing. I love what I’m doing. I’ve got family, I’ve got an active lifestyle, I’ve got this fabulous brand and a company that I’m a part of, and a team. I just love it. I don’t know what else I do, which is a problem.

I decided I didn’t want to live in fear. I didn’t want to live every day thinking about what I had to lose. I had a lot to lose. I didn’t want to be bitter about why me. I just decided I want to soak in everything I can on any given day. I want to be a CEO, I want to be a dad, I want to be a husband, I want to be a papa. I’ve got four grandkids. That was it.

I think for me, that was really powerful because I don’t want to be that cancer guy and they brought it up. It’s just not my thing. I’m glad to talk about it. I don’t hide it. I’ve learned a lot. I want to enjoy the things in life I really enjoy.

That’s where I learned, but I think everybody’s different. You do find out companies, when you hit challenges, you learn what you’re really all about. I think it’s the same for people, of course. I feel really lucky because I’m doing what I want to do. Cancer is in the rearview mirror. It’s good.

6. Martin Casado – The Past, Present, and Future of Digital Infrastructure – Patrick O’Shaughnessy and Martin Casado

[00:03:58] Patrick: How would you put chapter headers on the stages of cloud adoption, going back to, I think, Azure and AWS, are sort of mid-2000, 2005, 2006, thereabouts, relatively speaking, a short story. What do you think the major eras of the cloud story have been so far?

[00:04:15] Martin: Right before the cloud, of course, everybody ran their own internal IT. Right? And so they kind of write their own servers and their own wiring closets. The cloud showed up and the early usage was what you would typically find in a technology early adopter ecosystem. It’s more new projects and startups and hobbyists, the average workloads were relatively small. There was exceptions to that of course, like Netflix is a very famous one, which went all in the cloud very early. But in general, that was what it was. This is like 2005-2010 timeframe and still was very experimental. A lot of the time there was big discussions on whether the enterprise would actually go into the cloud. When I ran network and security for VMware, which is 2012-2016 timeframe, I think that was the more mainstream adoption of the cloud. You saw large organizations, traditional enterprise moving workloads to the cloud, very serious discussion with the Fed and the government. It became a mainstream way of doing things. If you were a large organization and you didn’t have a cloud strategy, I mean, you were either considered a laggard or a special case. That brings us to 2018-2019, and now we’re seeing a shift where the move to the cloud has implications on your finances, because now instead of you being able to buy a physical asset and internalize that, you’re basically paying a portion of your income to a third party.

Now there’s a lot of discussions around, how do you optimize the use of cloud? Is the right thing to go all in on cloud? Is it something that you do a portion or whatever? I just want to make one quick analogy, which is, I always view companies going in three stages, the product stage, the sales or growth stage, and then the operation stage. The product stage you’re finding product market fit. The sales stage is you’re getting to repeatable sales and growth. You don’t really worry too much about unit economics. And the operation stage is when you care about unit economics and you go into multiple products and you do all the operation of complex things. The cloud had gone through the exact same three phases, which first was trying to find product market fit, which tended to be within new projects, funding the projects. Then it went to the growth phase where everybody went all in and didn’t worry about the implications to the economics of business. Now we’re at the operations phase where we’re starting rationalize all of that.

[00:06:26] Patrick: Maybe tell the story of Dropbox, which I think as an individual company, is a great example of cloud isn’t just some panacea. It has incredible benefits in terms of how quickly you can get going, outsource the reliability to somebody else that’s just focused on this, AWS or whatever. But from a cost standpoint, it can get really out of hand. I think Dropbox is a good and probably unfamiliar to most tale of going the other direction.

[00:06:49] Martin: There’s basically two trends that happen at the same time. It’s important to understand those two trends to understand what happened at Dropbox and actually a number of other companies too, it’s not just Dropbox. The two trends are the following, the first one is cloud, which we talked about. The second trend is SaaS. And specifically what’s unique to SaaS is, is before if you were a software vendor, you would build software and you’d ship software, and somebody else would run it on their own infrastructure. Your COGS, your cost of goods as a software vendor did not include the infrastructure that it was being run on, because it was being run on somebody else’s infrastructure. For example, my startup, we built software for networking, we shipped it, other people would run it on their infrastructure. However, if your SaaS, if your product is software as a service, then part of your cost of goods is actually the infrastructure. Someone comes, says, “I’ve got a SaaS site and someone comes and uses it, then they pay me some, and then I pay say, AWS a portion of that.” That is a change of cost structure. The books look very different.

While the cloud is getting adopted, all software is going from basically on-prem to SaaS, and in some cases, and there’s many of these cases, it turned out that it was very tough to get software margins just because the cost of the cloud services on the backend was so high. The era of shipping software, we’d all say these companies have 80% margins because you basically write the code once and then it’s free to copy bits, so you just ship it to everybody else. Especially in infrastructure, there’s many companies that felt like they’re basically reselling a thin layer on top of AWS or one of the big clouds, and then paying a large portion back to them. For example, I know multiple companies that are household names, where they’ve got product lines that have 0% margins because all of the money goes back to the cloud services it’s hosted on. Dropbox very famously had this situation where S3, which is the storage layer on Amazon is not optimized for this use case of many small objects. They found that they were paying a tremendous amount. Now, they were a very large user of this specific use case. AWS was not optimized for it. They decided to build their own internal infrastructure and probably saved the company at the time, by moving off the cloud and taking it internally…

...[00:10:08] Patrick: There was a really interesting thing that you wrote about the interesting concept of lost market cap of companies that were big users of the public clouds. I’d love you to walk through that concept, because you mentioned maybe this saved Dropbox, the company, and I get that that’s a very special, specific case, but it sounds like there’s a bigger story here of lost margin and therefore lost market cap because of the use of public cloud. I’d love you to walk us through that.

[00:10:32] Martin: We did this analysis, a very simple analysis, which we said, “Okay, right now there’s a tremendous amount of money that SaaS companies spend on cloud.” Let’s say if they brought it inside and they were able to drop those costs by half, which most people agree that you can drop the costs by half by bringing it inside. If you could do that, what would that do to the stock price? Normally when people look at this problem, they say, “Well, if you bring this inside, yes, it’ll save you money. You’ll save 50%, but that money won’t cover the team, the complexity, because that’s not a lot of money.” But if you look at the leverage that increase in margin does to the stock price, now you can free up for a large company, potentially a lot of money, which will flow over to cash, so it could be a big win.

What we learnt is that we looked at just public software companies. We looked at 50 of them. We looked at all of their spend and we said, “Let’s assume you cut that spend in half.” Then we calculated their margins. And then we said, “Benchmarking against other public companies, if their margins were half, what would that do to the stock price?” It turned out that it would increase in aggregate the stock price by $200 billion. Just a tremendously high number. I think we wrote $100 billion to be conservative in the actual blog post, but $200 billion. That means if you’re a company that’s say, worth $10 billion, and you can reduce your COGS by a bit, you could now become worth $14 billion, and then you have access to that for debt and hiring or whatever else. Because those two trends happened at the same time you had the cloud trend, as well as the SaaS trend, I don’t think there had been a lot of focus on what it does to the margin structure. We did the first analysis and said, “Actually it’s huge and it can impact your stock price.” I do think, especially now in this market correction, it’s a good thing for companies to start looking at…

…[00:14:03] Patrick: Before we get to something like Kubernetes, a little bit more complicated of a topic, I’d love to just return to super basics around digital infrastructure in the first place. And maybe even go all the way back to the original AWS website, where I think it was storage, compute, database. You mentioned networking. What are the base level, most primitives of the digital world? What are the most important, big things that actually happen? Because I’d love to understand what’s changed in those areas, like compute sounds like compute. What is changing in those three, four, five base level areas?

[00:14:33] Martin: The traditional infrastructure’s computing and storage, and then databases. Prior to cloud, you’d buy a server from whatever, Dell or IBM or HP. You’d buy a switch from Juniper or Cisco. You’d buy a storage array from whoever, EMC. And databases from Oracle. So all those have now been, basically, collapsed into a software layer over basically merchant hardware in the cloud. So you can get the equivalent of just compute by TC2. You can get very flexible networking layers, where you can put security policies and that’s largely implemented in software within the cloud. And then you get these scalable services, like the database services that are scalable because they’re in the cloud. And so that’s the bread and butter of the cloud.

For a cloud is basically you take these traditional abstractions, compute and storage, that were connected to a box and now they’re just basically software services that you can spin up and they should be able to grow to the size of the workload. But what has also happened in the last say, five years is a number of services then built on top of those that are higher level abstractions. So for example, machine learning workflows, analytic workflows, different types of databases that focus on different types of query patterns. I want to do analytics, or I want to do LTP, or I want to do very fast queries or time series. We have seen this renaissance of infrastructure, again, which used to be tied to a box now being implemented as a software services in a way that’s much faster than we’ve seen historically for that exact reason. That it’s not confined to a box…

…[00:17:30] Patrick: How will that happen? It’s like up against a death star or something. Like facing these three big companies. What do you think the best entrepreneurs will do? Pick something like crazy specific and just go after a single thread? How do you think this innovation cycle will happen?

[00:17:43] Martin: All of these companies are like, very strong repeat founders and the companies are Mighty, Fly Out IO and Mosaic. So, what do these companies do? So Mighty is browser as a service. I don’t know about you, but right now even as we speak, I probably have 30 tabs in my browser. My laptop goes slow. If you use Mighty all of that’s offloaded and you get this crazy good experience, which is great for most of us, especially as the browser gets more workloads. What is Fly? Fly allows any developer to run compute workload at the CDN tier all across the world, which is important if you care about responsiveness to the users. And what is Mosaic? Mosaic is, basically machine learning as a service. So they provide the ability to run models very quickly for AI specific loads. So, what’s unique about all three of these companies is all of them are doing their own hardware. They’re looking to run servers, they’re racking and stacking. And these are very, very strong founders.

All of them are repeat founders and all of these companies have great traction. So what is happening here? I think it’s exactly what we’ve spoken about, which is there just are across the industry certain workloads that, if you look at that very specific workload, the cloud is just not optimized for them. And that provides room for the Mighty and Mosaics and Flies of the world to provide something that is a very attractive proof point or performance point or whatever it is, with respects to the clouds. And so I don’t think the answer is we’re going to see a lot of drop boxes, where the end customer builds their own data center. I do think we are seeing very concrete signs of third party companies coming in and providing cloud services that are just at a much better price point, or a much better performance point, or much more optimized for a workload. And because the cloud is growing to size, there’s enough market now for solvent companies to do these. And so I think this is the very beginning, again, of a much bigger trend.

[00:19:33] Patrick: Can you say a bit about your view of what I’ll call API first companies? Which I think a lot of people would include in this definition of digital infrastructure. If I can hire Stripe to be my payments processor by simply inserting a API into my software that I build and care about. And then there’s one of these APIs that’s proliferating for kind of everything. What do you see happening here? Is that infrastructure in your mind? Where does this fit into this equation?

[00:19:58] Martin: As markets grow, the unit to which you monetize gets more granular. And my favorite example of this, and it’s one that may be a cliche but it’s worth saying, is the car market. So, way back when in 1913, Ford had a factor called the Rouge River Factory. And this factory literally went in on one side, it was like water, rubber and coal. You know, and like iron ore, and what came out on the other side was cars. And the reason is there wasn’t a sufficiently large market for cars to actually have suppliers. You couldn’t be someone that provided wheels or whatever. And if you look at the car market now, I mean, there’s companies that provide nuts and bolts and you’ve got multiple tiers of OEMs and integrators, et cetera, et cetera.

So the same thing has happened to systems historically. So in the 1970s, the same company would build literally the chip, the motherboard, the sheet metal, the operating system of all the apps. And then of course the OS got disaggregated from the hardware and then the apps got disaggregated from the OS. So now what’s happening is the application itself is being disaggregated. You take any application, you blow it up and assume the market for this application or any application is so big that independent component of applications now can become companies.

So what does an application do? I mean, applications authenticate users, they need access controls, they need to send emails, they need to do payments. These are things that all applications can do. So it’s almost like every help or library in an application is now becoming a company. So much so that I remember even five years ago, you drive up 101, the heart of Silicon valley in the Bay area, and you’d have billboards where the entire company was an API. PubNub, Sendgrid, you know, Twilio. And so this is a major movement where now you don’t have to build a business app to build a company. And for an infrastructure person, this is super exciting because most of the founders I invest in are technical founders that are providing technical functions that are only useful to developers. And in the past, it was hard to build a business that way, but now you absolutely can.

If you’re in tech at all, or you’re an investor at all, I definitely think you should look at an application and assume that any sub-component does have the potential to now become a company, because the market is just so large.

[00:22:11] Patrick: What stage of that process do you think we are in? Twilio and Stripe, everyone knows turns out payments and sending messages. It’s almost like the equivalent of storage and compute in application building. Where do you think we are in that process?

[00:22:25] Martin: I think we’re still pretty early. I mean, on average, an application uses 17 external APIs. I think like a mobile app, something like that. But if you look at the use of libraries and open source and everything else, it’s still incredibly high for people having to integrate external components and management operate themselves. I think that there’s still a long way to go, especially as we get into kind of more complex things. So for example, every application often requires some sort of internal policy. Who can access what, or you know? And this is a very specific computer science problem. How do you build a language or a policy language that kind of accesses, that allows a third party to declare a set of rules and mitigates access to those rules? Like, this is a component in most programs that can be pulled out and turned into a company. There’s a number of companies looking at that, that are just getting started.

[00:23:16] Patrick: When it comes to this developer facing tooling, there’s this open source way of building and there’s the more proprietary, closed source way of building. What have you learned about what works well in which domain? And then I’d love to also learn, like if you’re an open source company versus not, what is more or less important as you think about product and go to market and everything like that?

[00:23:35] Martin: I’m starting to be of the opinion that as we move to SaaS and that’s the primary way of consuming infrastructure, which it seems to be, that open source matters a lot less. And the reason I say that is, if I’m a developer and I’m writing an application and I need to authenticate my users and I need to authorize their access to things, and I need to send them emails or send them SMS texts or whatever, I have two options. I could download some open source package and then operate that, or I could just use an API that somebody else operates. The secular trend is I’m going to use the API that somebody else operates. And if I’m doing that, whether or not the code for that is open source, doesn’t matter that much to me. So let’s take the case of it is open source. So, even if it is open source, there is some value there. A lot of actual code to running that service has to do with the operations of the service. Like, how do you make sure that it’s high availability? How do you debug it? How do you check for performance? Like, and that operations code is to be very specific to the actual service running. So it isn’t even useful.

So that would never be open source anyway. So even if I had the source code, I couldn’t really use it and operate it in the same way that somebody else could, or is running it. When it comes to dev tools, things that I am specifically using in my program as I develop, like that will always be open source and that’s very important. But anything that’s functional and offered as a service, I think the actual value of open source decreases. And what raises importance is actually open standards, which is, I still want be able to make sure that I’m not locked in to one and I can move between them, but that’s not an open source argument. That’s kind of an open standards argument. And so the role of open source has obviously shifted very, very quickly in the last 10 years, largely driven by this consumption with SaaS. And I think that we’re getting a more nuanced view of where it’s useful and where it’s not. Whereas 10 years ago, there was this broad consensus that open source is great and it’s going to take over the world. And that just doesn’t seem to be the case in the way that we all thought…

…[00:28:41] Patrick: Going back to this notion of, so if they’re the consumers of these APIs or little pieces of infrastructure, I absolutely love the Ford factory example, and what happens as it matures, that it’s so clean. What do you look for as an investor when you are seeing one of these, let’s say API forward or first companies for the first time? What is your method of investigation? How are you processing a new company?

[00:29:03] Martin: So throughout this all together, we talked about a trend. So there’s a lot of frontend developers. We talked about probably 100 to everyone backend. And those frontend developers, they’re building more and more of the application. So in the past, they had to… Were very tied to the backend more and more. Instead of having their own backend, they can use it an API from a third party company. Let’s say they’re using 20 little SMS or whatever. The interesting thing about these API companies that offer to the frontend is that the unit of consumption really is like a function call or an API call. So they almost have these consumer-like dynamics. So the primary evaluation criteria, to answer your question, and why it’s so different, in the past, if you’re going to evaluate a server company, who’s the buyer, what’s the go-to market motion, what’s the ACV.

You talked to a bunch of the buyers, you’d see if they can build the technology, et cetera. Now, with these API companies, you literally just can look at what the usage graphs are, how many users, how do they monetize them, et cetera, and it’s become much more of a bottoms-up, or SAS, or consumer type profile. So we stopped a lot of that approach to investing when evaluating these companies. It’s much less about can they build it, who’s the buyer, and it’s much more about how they use it in a practice, then it’d very interesting. A lot of these companies, they do. They’ve got these beautiful growth patterns, just like you’re looking at the next WhatsApp. They really are almost consumer-like phenomena.

[00:30:27] Patrick: What would be the most common red flags or disqualifying observations if you’re investigating one of these companies beyond lack of that nice looking usage or engagement?

[00:30:39] Martin: Well, I’ll tell you what I’ve gotten wrong. I do come from the older era where you actually evaluate the technology, you have a thesis on go to market. Often, we’ve seen these companies come in and they’ve got these beautiful usage graphs. They haven’t monetized yet, but we’re like, “Oh well, who’s going to pay for this?” Or this is just developers, like whatever. And then we kind of talk ourselves out of the deal, because we know the market better than the founder. And in almost every case, I’ve regretted that because the reality is, and this is an internal thesis of ours, is the graph in almost every case is just smarter than our theorying. The market actually knows what it wants.

These days, if one of these API companies is doing very well and the usage is great, I’ll give you an example, Hugging Face is a phenomenal company. And if you looked early on at the usage, this thing is a rocket ship, and you can have a bazillion theories why you can’t monetize the model, and you have a bazillion theories of why their go-to market is going to work. But the reality is the market loves it, it’s a great company. For me, it’s almost like a counter thing, which is, I do think that this API makes life a lot easier. You don’t have to have a grand unified theory about how things work, because you can literally just look at how this thing’s being consumed, because the consumptions become so bite-size; you get a lot of early signals. I think it really boils down to…

[00:31:53] Patrick: It comes down to usage.

[00:31:55] Martin: Yeah, to usage.

[00:31:56] Patrick: How should these things be priced? What have you learned about actually building the revenue model around something that looks more usage based? All these examples, AWS, what we started with, these API companies, they tend to be usage-based pricing. So what have you learned about that? Is that the right thing? Do you think that changes?

[00:32:12] Martin: It feels to me though, apps are for seat pricing, and infrastructure is usage pricing, and that’s basically how it is. And if you’re in the frontend, you’re not doing usage pricing, you better get there. You really have to. And if you’re apps, and you can get away from seat pricing, that just seems like that’s where you’ll end up. I do feel that when it comes to company building, there’s a few areas where there’s no simple answers. There’s a lot of stuff that’s systematic, like how do you hire your sales force, it’s pretty systematic. How do you create your org is systematic. But one of the things that’s just not systematic is pricing. Pricing is actually dictated by the shape of the market and the shape of the product. And it takes months to get it right. I’ll give you three mental landmarks, and I think the rest is just actual work.

So one of the mental landmarks is pricing is often fixed by the market. And so you should look at the ecosystem and the other types of companies and how they price and I think you should follow that model. For example, if you’re building on top of Snowflake, how Snowflake charges is going to be very similar to how the customer expects to buy. And if you’re building on top of that, you’re going to want to align with that. And I’ve been in many cases where the companies wanted to innovate on their own pricing model, but the ecosystem alignment just wasn’t there. And it was just painful until they had to change. I think another mental landmark is the market will tell you the price over time, but not initially. The less that you have public or the less that you force your opinion on, the better it is. I do think that a lot of early sales discussions is just to figure out pricing, that’s what it is. Your goal is to reverse engineer how they think about that. The good news is because the consumption is so much higher on these, and the unit of consumption is lower, it’s per API call, there’s kind of a lot of room to experiment…

…[00:40:14] Patrick: And as you think about the ways that all of this intersects with the real world now, which we really haven’t talked about. We basically talked about digital infrastructure that leads all the way up to applications at the top end, and the APIs in between and all this great stuff. But it seems also like as we mature, more of this technology will apply to the real world too, whether that’s new kinds of hardware, whether that’s intersection with physical goods like cars. How do you think about that side of things and maybe the hardware world of technology?

[00:40:45] Martin: People have a hard time grasping what, let’s say, AI and ML concretely provide, because it’s such a diluted buzzword. So for everybody that’s listening, the important thing to realize is that what modern AI and ML does, which we’ve never really been able to do before in systems, is take unstructured data, and digitize it, and add it to the typical logic of the program. And we’ve never been able to do this with vision, like objects out in the real world. We’ve never been able to do this with natural language in the level of ASCII we can. We’ve never been able to do this with voice or speech.

That technology just hasn’t existed and so we’ve never been able to build the big programs around them. And now we can, and it’s a sufficiently different workload that two things happen. First, it pushes software into the realm of the physical world. We can now see things and interact with things. And we’re talking quantum leaps of accuracy improvement. It also drives the type of hardware and software that we build, because the workload is so different, right? So we’re seeing tons of innovation all the way down to the ASIC level. Mosaic as a company is building a data center focused just on this type of stuff. So I think that this really is a massive impact on infrastructure at large, not just the infrastructure, but also what sorts of applications software can go after.

[00:42:10] Patrick: It’s very cool to consider what all that might mean. I mean like self-driving cars is like the obvious constant example of what computer vision might allow us to unlock. Obviously, cloud had this crazy impact on the services you consume. It’s unlocked innovation by reducing friction. As you think about what’s going on in the digital infrastructure world, period, what are you most excited about in terms of what it might unlock in the 2020s or over the next decade that maybe we’re just starting to think about?

[00:42:38] Martin: Any problem that human beings go after that’s been outside of the realm of software is currently in the realm of software. And this is farming, agriculture, oceanography, you name it. And so I am a tech optimist and tech maximalist. I think that part of our job is to solve problems. It has really been limited to IT, like information. And now I go from IT to just tech. You look at any industry, any industry at all, and I think that it’ll be touched by this. That’s, to me, just tremendously exciting. What’s interesting, I would just say very quickly is we’re still asking the question. Are these still software companies or something different? So you could say, this is just software going after agriculture. Now you still have a software business, or you could say this is still an agriculture business, or you could say it’s something totally different. That’s a question I’m personally very interested in…

…[00:45:34] Patrick: If you put your investor hat on, I guess your purely selfish investor hat, meaning you were just trying to maximize returns, and you could somehow have a crystal ball that would reveal some information about the future, which is currently uncertain, where if you knew what the future was going to hold, be super valuable to you as an investor, trying to earn a return. What would you ask of that crystal ball? Like what would you want to know about the future that you’re not sure which way it will go?

[00:45:58] Martin: I am very curious about where crypto lands, and I think there are three potential views, right? On one end, on the most negative and barren folks are like, “This is all fake. It’s just Ponzi scheme, yada yada, yada.” On the extreme other end, it’s a total reformation, not just of technology and companies, but an organization. This is like everything. You don’t just have routers. You’ve got crypto routers. You don’t have just storage. You have crypto storage. You don’t just have businesses. You’ve got dos. You don’t just have money. You’ve got DeFi, like everything changes. And then there’s a bit of a middle view somewhere. This is a continuum which says, “You know what? There’s something very innovative there on the ability to build networks. There’s a number of primitives that are very innovative on the ability to build applications. There’s a number of innovations on how you offer new services to consumers where you don’t know the endpoints. There’s a lot of great primitives, consumption to monetization layer, just like social was primarily a consumption monetization layer.”

In that future, that layer is on top of a lot of systems, but you still have traditional computer networking and storage. You still have traditional clouds. You still have to know all of those things. And it’s something that’s added to that. And I think the answer to that question given the amount of money that’s already involved is enormous. And I don’t think anybody knows the answer. I tend to be in the middle where I think that there’s a real innovation there. I think there’s real value. I think it’s a real unlock for a lot of new business applications and use cases. But I think that infrastructure itself, a lot of the traditional models still applies. You still have to build databases. You still have to use storage. You still have to understand the trade offs of asset. A lot of these things still apply.

[00:47:41] Patrick: Obviously distributed systems. Some of the smartest people in the world are working in distributed systems, not necessarily crypto networks, but just like the ability to distribute state or update state constantly faster, smoother, or whatever. As an infrastructure person, when you look at the current technology in crypto networks, maybe the dominant three or four, what are you watching or interested by or looking at, the consensus mechanisms, the scaling ability? What are the dimensions that you as an infrastructure person are keyed in on today?

[00:48:10] Martin: The crypto origin solves a very important problem. That’s traditionally not been solved practically. And that is allowing basically an anonymous set of people with no prior trust relationship to have strong guarantees on something, right? Originally it was a ledger, and then it’s become more to generalized compute. That’s a very, very real innovation thing. And that unlocks very, very interesting business use cases like we’ve mentioned. But distributed systems is one of those things that you just can’t paper over with a thin software layer. You can’t hide it under an API. You’ve never been able to. There’s entire languages that just help programmers manage distributed systems. What’s important is what developers end up using, or what distributed paradigms they end up using, because that will drive the capabilities of the system. So if everybody says, “This is a purely distributed world and everything I write must be purely distributed,” that will have some implications of the type of systems that you can build.

So the thing that I’m most interested in as kind of an old distributed systems guy is what are the nature of the applications? Is this going to land in the realm of purely distributed stuff? Is it only embarrassingly parallel applications, like DeFi is an embarrassingly parallel application? There’s other things that are embarrasingly parallel. Or, is this going to go more to the model of general compute? Is that something people are going to do? Are people going to build like the AWS in crypto? The answer to that is actually very, very significant, right? You could say, “Well, listen, traditional distributor systems are great for building AWS, and this is going to just be the consumption monetization layer, or it actually is going to cause innovation in the way that we do distributed programming in the future.” I don’t think that’s clear yet where that’s going to land.

7. Watch: How Does a Dead Fish Swim Upstream? – American Physical Society  

Take a quick look at this trout swimming upstream. Notice anything unusual?

[Video of trout]

You’ve probably seen something similar countless times; the fish wriggles against the currents that push it backwards, slowly making headway until it turns and ducks out of the influence of the stream. Nothing special in that.

The only thing is, this particular fish is dead.

Yes, you read that right. No matter how lifelike it looks as it undulates across the tank, that same trout would just go belly-up if the current were switched off. So how can it possibly swim upstream?

A team of researchers from MIT and Harvard were equally surprised when they happened upon this phenomenon by accident. They’d been studying the way live trout conserve energy by swimming behind obstacles that block the current*, and unintentionally placed a dead fish in the experimental setup. When they took a closer look, they were stunned.

“It was incredible, very counterintuitive,” MIT researcher Michael Triantafyllou says, describing the shock he felt upon seeing the fish swimming upstream. He explains that while he knew trout were good at conserving and even extracting energy, he had no idea that they’d be able to extract enough energy from the surrounding fluid to swim upstream without expending any of their own energy. Immediately, the team started to investigate this new, seemingly impossible phenomenon.

As it turns out, objects that block the natural flow of water, like a rock or a boat, create a series of complex vortices in the current as the water navigates the obstacle. As anyone who’s tried to grab a fish knows, fish are quite flexible all down their spines, which allows the head and the tail to move independently of one another. In certain situations, the array of vortices forming behind an obstacle cause the body and tail to flap in resonance. This tilts the body in such a way that the vortices, which cause a pressure drop, apply a suction force that propels the fish forward.

As Triantafyllou explains, “You have a flow behind the obstacle, which creates a continuous stream of eddies. Each eddy contains energy and also causes the pressure in the fluid to drop… the eddy causes the body to flap back and forth, and the fish manages to extract energy.” Since all of the energy is supplied by the vortices, it doesn’t matter at all whether the fish is alive or dead, if the timing happens to be right.

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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, Meta Platforms (formerly known as Facebook), and Zoom Video Communications. 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 22 May 2022:

1. Flexport: How to Move the World – Mario Gabriele

The name “freight forwarder” is strange. It’s the kind of term whose meaning seems literally evident but is blurred by a sort of tedium-cloud. It is the cousin of descriptors like “insurance agent” and “data analyst.” 

A freight forwarder is a tour guide for objects. Or, at least, that is how I have come to think of it after having it explained to me (and re-explaining it to myself after I have forgotten) by a series of patient people over the years. For, say, a shipment of pillows to make its way from Taiwan to France, it must move between ships, planes, trains, and trucks. As many as twenty different companies may be involved in a single shipment, each handling one stint of the multi-modal journey. Critically, each party is incentivized to care narrowly about their leg rather than the entire trip.

The freight forwarder sees this salmagundi of boats and warehouses and flight maps and says, Don’t worry, I’ll take care of it. With the savvy of a good tour guide, it helps the customer navigate the mess, keeping the itinerary, forewarning chancy routes, bum ports (the “bad neighborhood” of logistics), and charting an optimal path. They are the concierge of conveyance, consiglieri of transit.

As it turns out, this is a big business. The global freight forwarding market is pegged at around $182 billion with projections to reach $221 billion by 2025. That amounts to a modest compound annual growth rate (CAGR) of a tick below 5%.

This is also an extremely fragmented space. As of 2020, DHL Global Express led the market with 6% of annual revenue. Kuehne+Nagel and DSV+GIL followed with 4% each, succeeded by DB Schlenker and Nippon Express at 3%. Fully 60% of the market is composed of “others” – smaller providers that hold less than 2%, and perhaps not more than a few deciles…

…Chen’s primary mission is to guide Flexport into a phase of automation that lays the groundwork for its global trade platform ambitions. When I asked Chen which initiatives best showcased the company’s burgeoning abilities on this front, he shared two examples.

First, Flexport is devoting serious resources to digitizing global trade documents. Ingesting data from different languages and formats is the first step in building a library of “facts” around a shipment. Instead of “optical character recognition,” or OCR, Flexport relies on machine learning models developed with Scale AI. Whereas Flexport used to transform accurate data from documentation in two days or less, the recent partnership has helped the company do it in minutes while maintaining 95% accuracy. Chen noted that the reason accuracy sat short of 100% was not a technical issue but a result of human error at the outset. 

Second, Flexport is developing models to predict when a shipment will arrive to be unloaded at a given port. Getting seemingly simple things right can have meaningful downstream effects. Knowing when a ship is ready to be unloaded influences when a truck should arrive, for example. This, in turn, impacts how a warehouse might organize its space. Just as snafus in one part of the supply chain can lead to misery elsewhere, improvements can create secondary and tertiary efficiencies. According to Chen, Flexport is well-positioned to produce reliable models to this end thanks to its existing freight forwarding business, saying, “We believe we probably have the highest accuracy.”  

2. #028 – PM Lessons by Meituan Co-Founder – Pt 22: Demand and Supply – Tao

Understanding demand and supply is hard. Although you can only be in one of two situations: 1. demand outstrips supply or 2. supply outstrips demand, it’s hard to know at any point in time, which situation you’re in.

In our day-to-day work, these are the common problems that we’d encounter that have to do with demand and supply:

1. The team doesn’t proactively determine the situation of demand and supply. As a result, the operation lacks focus, and the approach is basically “throw it against the wall and see what sticks“.
2. The team knows that demand and supply each affects the other, but can’t make a clear judgment about which one is more important.
3. The judgment about demand and supply is correct, but the operation is not guided by the prevailing demand and supply conditions.

If any of the above three situations occurs, then the team would usually work in the opposite direction of the demand-supply condition. In fact, they may even have a strong incentive to get it wrong!

For example, if a company is in a situation where supply outstrips demand, it should be doing more work on the demand side. In reality though, more often than not, it’s still pushing hard on the supply side.

Why?

If supply outstrips demand, then the demand is very important. The demand side buyers know they’re important, and they’d be very hard to entertain. In comparison, the supply side sellers would be a breeze to spend time with because they’re the ones who are in a hurry and have something to ask for. As such, the team would have a strong incentive to continue working on the supply side and pretend the harder-to-deal-with side is less important. This was a frequent occurrence in Meituan…

…Here’s another example from the retail industry to demonstrate how frequently people get the demand and supply relationship wrong.

I asked the bosses of convenience store and supermarket chains, “In the retail industry, which one is more important, demand or supply?“

Without exception, all of them answered that supply is more important.

It doesn’t gel with my business common sense – with the industrialized manufacturing that we have today, most goods should have more supply than there is demand.

Therefore, the next question to figure out is – is this how they think, or is this how they act?

So I asked them another question, “At your company, what work do you absolutely have to do yourself?“

They all answered, “Choosing the location“. One of them even said he personally chose the location for close to a thousand stores they have all over the country.

For retail, location is the aggregation of demand. They say supply is more important, but their actions are pretty revealing.

3. Arena Show Part I: Idea Dinner + YC Continuity – Benjamin Gilbert, David Rosenthal, Packy McCormick, Mario Gabriele, Shu Nyatta, and Anu Hariharan

Anu: It is so true. I also think it’s really hard to understand and appreciate an organization like YC from the outside. You really deeply understand YC in only two ways, if you’re a YC founder and if you work within YC.

When I was at Andreessen Horowitz, I actually did not understand the depth and the cultural nuance with which YC was built. It’s really hard to grasp that.

David: Can we talk about that for a minute? I put this in the notes. My current mental model of YC is like a university, a top Ivy League University. It’s very hard to get into. You take classes every year or every six months. There’s an endowment attached to it, which is Continuity now.

Ben: Wait, David. What do you mean by endowment? Are you saying that all of the proceeds from YC exits go into a big pool of capital that then funds Continuity? Is that what you’re suggesting by endowment?

David: No, but I’m curious if that’s the case. I meant more just like, it’s really weird that a large part of the private capital markets and the venture capital markets in America, those dollars come from educational institutions, mostly private educational institutions. That’s just very bizarre. Anyway, that’s kind of what I meant. Is that a good mental model of YC? What is it like?

Anu: Yes. In fact, we say that. We say YC is university for startups. Think of the accelerator as the undergraduate program and Continuity is the graduate school.

We are modeled after university in the sense of we have applications you don’t need to know anyone to apply to YC. Second, we were the first to do mass production of investments in a batch of startups. No one had ever done that. Everyone usually does, I met a set of companies, we have a Monday partner meeting, and you pick one or two.

YC from day one was a batch. They always received investments together. That, I think, goes to the insight that the founders of YC had at the time, which was entrepreneurship is lonely. Being in a group is how you motivate each other to learn from each other. And that’s your peer group.

Fundamentally, it came from the approach of a university. Continuity is graduate school. As I talked about, Series A is just one of the programs we run. We have two others, Post-A and Growth. Post-A focuses on two months within you raise the Series A. That’s a six-week program. We rebatch you, so now you have a new set of peers.

Our scale founders come teach how to form a recruiting team, how to hire engineers, because your job changes as a CEO. No one is writing a book about how your job changes and how to learn. Remember, the median age of a YC founder is 27, which means they have probably managed the sum total of three people in their life before these founders.

David: They really are like undergrads.

Anu: Yeah. You cannot expect them to know. How are you going to provide resources so that they can learn from others and they do as few mistakes as possible and as quickly as possible? Because when you’re scaling, you just go on a rocket ship, but the amount you demand out of these founders is a lot. The bar you’re setting is really high.

In our community, that’s why Brian Chesky comes to speak every batch. He’s the opening speaker of every batch. Right now, for all these programs that we run, the group program is how to scale as a CEO. That’s literally the program. It’s an eight-week session. It talks about hiring execs, performance, management, culture, and so on.

We have scaled founders and scaled exec. Tony Xu comes for that. His execs, the CFO of DoorDash, the head of engineering of DoorDash, come for the respective session. It’s really good to see the entire community working to transfer their learnings to the next batch of companies…

…Anu: At YC, I would say, if I had to pick one thing YC is really good at across both early and Continuity is we go by based on founders. I know it sounds cliche, but I think we also have an incredible advantage in assessing what makes a founder a really good founder.

We have incredible amounts of data, pattern recognition, and learning that we have honed it to a point that we know how to spot them. You all have heard of the famous 10-minute YC interview and everyone asks, how do you know in 10 minutes? The fact is we probably know in the first two minutes.

We actually don’t need the full 10 minutes. But sometimes, one or two people will surprise us with the end of the interview. I think the three things that can articulate what it is on the founder we look for.

One is the continuity stitch. Often in the growth stage, people pay attention to the founder, but they don’t. If you’re at a venture fund or a growth fund, you probably hang out with the founder for a week or two weeks before investment, some a total of three hours. By the time Continuity invest, I probably know them for years, or months, and I’ve had those interactions.

Ben: You’re saying that you’re paying attention more to the qualitative founder properties—even at the growth stage—than you are to their specific growth rate, or what their margins look like, or anything like that?

Anu: Yes, but if the three qualities hold, the metrics will show. I can either look at metrics, but sometimes metrics don’t tell you how good the internal sausage making is. Many people can package the metrics in a fundraise deck. It’s very well done. We teach you to do it.

We’re really experts at it. Therefore, we know it’s going to look great. We also teach them what points to emphasize on. We actually do practice runs. In Demo Day, we actually even write the script sometimes if they don’t understand what it is.

David: That’s a how-can-I-help moment.

Anu: Yeah. What we look for is, how fast does the founder move? What is how fast do they move mean? How fast do they ship? How fast do they iterate? Is it single biggest indicator and correlation to how successful they’re going to be and how soon?

You won’t be right about members’ many decisions early on, but at least, are you learning from them fast? And are you making changes? That’s one we measure. Second of the growth stage is how well are you hiring. If you’re sloppy in hiring, it always hits a wall.

One of the things we look for is how well are they hiring engineers, how well are they hiring execs. Will they be able to convince an incredible exec to come join them? That’s second. Third is clarity of thought. Clarity of thought in the growth stage for us is, can they write out two pages what makes this a $5 billion or a $10 billion company really well?

If you’re doing those three things, you’re going to be on top of your metrics, your product-market fit, your attention. There will be rough edges. I think because of YC, we’ve had the benefit of watching everyone from day one.

We know how Tony scaled. We know deeply well how Josh had Gusto scale. We know a lot of those founders. We then know, okay, these were rough edges, these are okay. These other founders had and this is how you and I know.

David: We’ve told a lot of these stories on Acquired. If you’re a growth investor looking at these companies new, you’re like, I know this is all going great, but you know those companies don’t always all go great. Tony had some serious near death moments. Airbnb was not up into the right journey the whole time.

Ben: If I had to summarize, I know we’re interviewing you. Not me here, but it seems like you invest based on the inputs rather than the outputs or maybe the leading indicators rather than the trailing indicators, where if somebody’s operating with those three principles, the business probably won’t consistently produce the results that someone would like to look for in the growth stage investment. They have a much higher probability at any given time of producing high quality results because those are the inputs that matter.

Anu: Absolutely. That’s why we feel strongly that inputs can be influenced. If you’re learning best practices and those are your inputs, then you can actually influence company building. When Tony comes and teaches our Growth Program and says these were my darkest moments, these are my mistakes I made, and I sure hope you don’t make the same mistakes, but these are two things I did really well, that’s incredibly valuable. That color is very hard to get outside of YC.

4. 2022 SaaS Crash – Alex Clayton

The rapid decline in value of public SaaS companies over the past 6 months has undoubtedly already had a huge impact on private market valuations. That downward trajectory may continue even if the public markets stay flat at today’s levels. If public market returns cannot fuel venture capital fundraising from their limited partners, the flywheel will slow down. Investors will have fewer dollars to invest, companies will have less cash to hire and invest in growth, and outcomes are likely to be much smaller than previously thought. This reset has been swift and will soon be painful for many businesses that are burning too much money and/or those that will have to slow top-line growth. Moreover, there will be wide-ranging implications for employees and investors not only in the SaaS community but for all private technology markets.

And while much of the focus has been on the decline in valuations, there is another huge factor that can’t be overlooked – how could a recession or broader economic slowdown affect your financial profile? This could have an even bigger impact on valuations if the fundamentals of businesses change for the worse. While a large part of the sell-off has consisted of a move away from riskier asset classes in sectors such as high-growth SaaS to cash and value stocks, recent earnings results have been strong and business fundamentals have not changed broadly. But what if you traded at 50x forward revenue and are now trading at 10x, and your associated forward revenue also dips by 30-40-50% from your prior plan? The outcome is not pretty and one we have not yet seen, but could soon if the 2008-2009 Great Recession is any indicator.

The following charts look at Salesforce* and NetSuite*, two publicly traded SaaS companies during the 2008 Great Recession, and what happened to their respective value and financial profiles. Unfortunately, while this is a small sample size, these are the best precedents as almost all other SaaS companies went public after the Great Recession…

…Salesforce was almost a $1B implied ARR (annualized revenue run-rate) business growing over 50% year-over-year at the start of 2008. During the Great Recession, revenue growth slowed to 20%. Non-GAAP operating margins did hold fairly steady, though…

…NetSuite was over $160M in implied ARR growing ~45% YoY at the end of 2008 before slowing dramatically. The company did not grow for 3 quarters in a row before accelerating back to growth. Similar to Salesforce, they also held non-GAAP operating margins constant but slowed investment significantly. It would be hard to imagine a ~$150M ARR business today that’s growing fast grinding to a halt, but this happened for NetSuite. The company also sold to SMBs and the mid-market, a segment that was hit particularly hard during the Great Recession.

5. TIP447: How To Build A Human Bias Defense System w/ Gary Mishuris – Trey Lockerbie and Gary Mishuris

Trey Lockerbie (16:25):

Fascinating stuff. So I want to move on to the next one, which is base-rate neglect. So there’s this phrase that’s come up, I don’t know, maybe over the last decade, maybe longer, but it’s don’t fight the Fed. And we’ve seen a lot of help from the Fed when markets have declined in the past and we’ve seen the Fed reverse course on say, raising interest rates quickly due to recessions and other liquidation problems around the world. So from this, we may have misconceived notions on how either the Fed will react to markets if they continue to decline from here, for example, which would thus enact this base-rate neglect human bias. So walk us through what the base-rate neglect bias is and how we might be able to avoid it.

Gary Mishuris (17:05):

Yeah. So I think it’s fascinating that… And I think sometimes people talk about inside view versus outside view. So base-rate neglect refers to ignoring the experience of others in similar situations and just making an assumption based on what we think we can do in this situation. So let’s say a very simplistic example of someone flips coins 1,000 times, they get 50% heads, 50% tails for a fair coin. And somehow we convince ourself that we can take a fair coin and flip tails 70% of the time. And that sounds ridiculous when they phrase it that way, but sometimes essentially that’s what is happening.

Gary Mishuris (17:42):

So, for example, if you study great investment records, which I’m sure you do, you realize that there’s a certain range of access returns over decades that the best investors have been capable of. And if you take Warren Buffett out of the picture and if you take people who use leverage out of the picture, unlevered returns, there’s almost nobody over decades has exceeded 5% per year access returns with no leverage and so forth. Obviously, Buffett has done close to 10, but I don’t think there’s going to be another Buffett necessarily.

Gary Mishuris (18:11):

So when someone shows up and they think they can do 10, what they’re doing is they’re exhibiting example of base-rate neglect. They’re looking at their own strategy and they’re saying, I have these clever mental models, I have this process, I have this special sauce. So they start to believe their own marketing deck a little bit too much, and they forget that the people who tried and failed to achieve the 10% for years, as an example, have also had their special sauce and their analyst teams and this and that, and yet they were only able to do a certain…

Gary Mishuris (18:42):

Think about someone like John Neff who record is public or who had three decades of returns. He beat the market by 3% per year in arguably less efficient markets than they are today. So when someone shows up and says, “Oh, I’m going to beat the market by 10%,” that’s a little bit crazy, it’s a little bit arrogant. And again, I think we’re all overconfident, but come back to the Fed. So look at the last 10 years, we had almost a perfect confluence of events. We had interest rates coming down. We had unrivaled Fed manipulation of markets far beyond just the short term end of the curve. We had maybe as a result there or maybe as a coincidence, huge amount of speculation, both by retail investors and by a number of “institutional” investors, institutional in quotes, not naming any names, don’t ask. And you basically had over the last five years, you had 25% CAGR for large growth stocks or all cap growth stocks.

Gary Mishuris (19:33):

So if you are investing in the universe, it’s pretty easy to start believing your own BS and start saying, well, gee, yeah, no, I can crush the… I can do 20, 25% per year, but like really? Let’s zoom out over the long term US equities return inflation plus six to seven, depending on the time period. So if you think you can do 20% plus, you think you’re going to beat the market by double digit percent per year. And I know everyone thinks they’re very special, but that’s just a perfect example of the inside view. The inside view is all these specific details for why the past experience of others doesn’t matter. And the base rate is the past experience of others in a similar situation. And I think the best thing you can do is zoom out and say, “Well, whatever I think about my own capabilities, let me put a heavy weight on the experience of others and a small weight on why I think I’m going to do so much better.” And that’s probably the best you can do.

Trey Lockerbie (20:25):

That’s interesting, because I was wondering the distinction here between say the base-rate neglect effect versus say the recency bias effect, because what I was describing, I don’t know, it could maybe fall into both categories depending on how you look at it. So recency bias is when you’re essentially taking events from the past and extrapolating them into the future. So how exactly is that different or what are maybe some other distinctions between that and the base-rate neglect effect?

Gary Mishuris (20:49):

So I think a recency bias is almost a special case of base-rate neglect. So what are some examples of recency bias? Let’s say you have a company over the last couple of years, it’s been growing 30% per year and you assume it’s going to grow at 30% per year for the next one year. I’m obviously using extreme example. So that’s recency bias. You take a near term past and assume that’s going to be the same in the long term future. On the other side, let’s say you have a company that over the cycle has barely earned its cost of capital and averaged a dollar per share. But now the last couple of years been earning $2 per share and averaging 20% return on capital. So you are going to extrapolate that $2 and assume that’s the new normalized earnings for the business and say the new long term average earnings is $2. And this now all of a sudden, the 20% return on capital business or something like that.

Gary Mishuris (21:40):

In each case, you’re ignoring the base rate, the base rate in this case being the history of the company or the history of similar companies. So in the first case, the history of companies growing 30% for two years is mean version in the growth rate towards the growth rate in all companies. So just to level set everything that the average company’s profits over long periods of time grow in line with nominal GDP. But, by the way, ironically, if you look at Wall Street estimates, hey, now they assume the average company grow is going to grow earnings in double digits. Well, it hasn’t, it’s been growing five to 6%. And that’s an example of base-rate neglect because they forget that a fifth of the market is going to have negative earnings growth, but that’s a separate thing.

Gary Mishuris (22:20):

And then the base rate for a company that’s been earning its cost of capital and had a couple of good years is that the long term history is much more likely to be the best predictor than the last couple of years, which could be a cyclical high or something like that. So I think ignoring the base rate leads to the recency bias, where we put a disproportionate weight on what just happened and assume that’s a proxy for what’s going to happen as opposed to zooming out and looking at a much longer data series…

…Gary Mishuris (54:36):

And like you said, if you have areas where you don’t invest, that squeezes those 10 to 15 investments into the rest of the opportunity set, meaning that you might be correlated. But it’s not about gig sectors, which is a common misconception. So I’ll give you an example. So prior to starting Silver Ring, I managed a fund at my prior employer and I had two investments. One was SABMiller, which was a beer company, and the second one was Qualcomm. If you are running some bar risk model, and you’re looking at overlap, they’re completely different gig sectors. One is technology, the other is consumer. So no relationship, you’re good, you’re diversified. But the thesis for each one was predicated on rising middle class in emerging markets, meaning people were going to trade up and buy more expensive beer in China and other emerging economies and people were going to trade up to fancier smartphones, which was going to drive demand for Qualcomm’s products.

Gary Mishuris (55:28):

So here are two completely different industries where the same macro force, which is a tailwind, if it doesn’t play out would hurt the thesis. So looking for those correlations as systematically as possible, and thinking about what do I have to be right about each business five plus years out as opposed to what do I have to be right about each stock five quarters out, that’s the mindset you want to have. And also frankly, you have a set of risk reward trade-offs. Too many people make the mistake of sizing their largest investments based on upside. But again, going back to the safety first mentality, I size my positions based on downside, meaning my largest investments have the smallest downside. I have an investment, which maybe it’s a 30% of my base case value as to 30 cents in a dollar, but if that has 100% downside, that might not be my biggest position. So again, you want to have multiple layers of defense.

6. The Transcript Q1 2022 Letter – Scott Krisiloff and Erick Mokaya

Investors are asking whether this is the end of an era. For nearly 15 years global policymakers have battled a deflationary mindset with near-zero interest rates and quantitative easing. However, a series of supply chain shocks and monetary policy errors have sparked rising long-term inflation expectations. If we have exited the deflation era and entered into an inflationary one, it will mean structural changes in monetary policy, interest rates, and stock multiples. By the Fed’s own account, despite raising interest rates by 0.75% so far this year, it is still only on pace to get to a neutral interest rate by the end of the year. It has not yet entered the restrictive territory, which would usually be justified by >8% inflation.

“We’ve been accustomed to 40 years, basically, of one cycle, the whole cycle that we covered in the last quarterly review. Declining interest rates, declining tax rates, all these trends – – it’s all come to an end. Not just an end, it’s actually changing. But people haven’t wrapped their heads around that yet…There’s going to be a new cycle.” – Horizon Kinetics (INFL) Co-Founder Steven Bregman

“An entire generation of entrepreneurs & tech investors built their entire perspectives on valuation during the second half of a 13-year amazing bull market run. The “unlearning” process could be painful, surprising, & unsettling to many. I anticipate denial.” – Benchmark Capital General Partner Bill Gurley

While the Fed has talked about getting to “neutral” throughout this quarter, it hasn’t yet set an expectation for what neutral means. There seems to be some consensus that this neutral rate would be a short-term interest rate in the 2.5% – 3.5% range. Equity markets may not yet reflect this new cost of capital.

“I think I’m in the same areas as my colleagues philosophically. I think it’s really important that we get to neutral and do that in an expeditious way. “ – Atlanta Fed President Raphael Bostic

“I like to think of it as expeditiously marching towards neutral. It’s clear the economy doesn’t need the accommodation we’re providing. And so in order not to tip the economy over by reacting abruptly, we need to take a measured pace. But that measured pace still gets us up to the neutral rate, which I put at about 2.5% by the end of the year.” – San Francisco President Mary Daly…

…The Transcript is also closely watching continued lockdowns in China. The Chinese government’s zero-Covid policy has left hundreds of millions of people in lockdown even though the rest of the world has returned to normal. The effects of this supply chain shock have still not entirely made their way into the economic discussion.

“I think a separate risk is kind of the impact of logistics and supply chains as we deliver product to China and from a more macro perspective just the port closures and the broader impact that we could see in China given the degree of exports they have just generally across the economy. With respect to the China quota difficult to predict.” – Intuitive Surgical (ISRG) CFO Jamie Samath

“...the situation in China is unprecedented. Shanghai, a city 4x the size of New York City, is completely locked down…China continues to battle COVID resurgences and navigate through prolonged lockdowns.” – Starbucks (SBUX) CEO Howard Schultz…

…Surprisingly, consumer spending has still only been moderately affected by surging inflation and falling financial markets. The covid-era stimulus has left consumer bank accounts with lots of reserves and consumers still have a significant amount of pent-up demand for travel, restaurants, and other entertainment. We are expecting to see some slowing of consumer spending and the real economy going forward in sympathy with the dynamics of capital markets.

“March was the eighth straight month in which inflation outpaced income with lower-income consumers being most impacted by rising energy and food prices.” – Wells Fargo (WFC) CEO Charlie Scharf

“Consumers are trying to ration their money a little bit more carefully because they’re trying to smooth out their cash flow.” – Affirm (AFRM) CEO Max Levchin

“when we think about where inflation is, there’s absolutely pressure on that low and middle income consumer.” – Macy’s (M) CEO Adrian V. Mitchell…

…No one really knows whether this is truly the end of an era and the start of an inflationary epoch, but this period is not without historical analogue. The transition from the deflationary 1930s to the inflationary 1940s was caused by World War II, which was also a time of intense supply chain disruptions coupled with huge economic stimulus. The Covid period has some similarities. Immediately following the war there was sharp and severe inflation for several years, which was ultimately brought under control by changes in monetary policy. In the longer term, huge investment in industrial capacity and human capital led to a consumer renaissance and low inflation in the 1950s.

The inflationary period of 1966-1980 is also worth studying if we are entering a new inflationary phase. An important takeaway from that period is that a surge in interest rates does not necessarily happen overnight. Instead, it happened in fits and starts over the course of several bear markets and recessions. Stock multiples ended that period in single digits, but the nominal value of the Dow hovered around 1,000 for more than a decade.

We may be entering a period in which the Fed raises interest rates more frequently than it lowers them, but the Fed is still very reluctant to cause a recession. If it looks like higher interest rates are putting employment at risk, the Fed is likely to abruptly change course despite inflation. The result would probably be positive for capital market valuations.

“You can’t think of a worse environment than where we are right now for financial assets..I think we’re in one of those very difficult periods where simply capital preservation is I think the most important thing we can strive for. I don’t know if it’s going to be one of those periods where you’re actually trying to make money.” – Tudor Investment Corporation Co-Founder Paul Tudor Jones

7. Trying Too Hard – Morgan Housel

Thomas McCrae was a young 19th Century doctor still unsure of his skills. One day he diagnosed a patient with a common, insignificant stomach ailment. McCrae’s medical school professor watched the diagnosis and interrupted with every student’s nightmare: In fact, the patient had a rare and serious disease. McCrae had never heard of it.

The diagnosis required immediate surgery. After opening the patient up, the professor realized that McCrae’s initial diagnosis was correct. The patient was fine.

McCrae later wrote that he actually felt fortunate for having never heard of the rare disease.

It allowed his mind settle on the most likely diagnosis, rather than be burdened by searching for rare diseases, like his more-educated professor. He wrote: “The moral of this is not that ignorance is an advantage. But some of us are too much attracted by the thought of rare things and forget the law of averages in diagnosis.”

A truth that applies to almost every field is that it’s possible to try too hard, and when doing so you can get worse results than those who knew less, cared less, and put in less effort than you did…

…But there are mistakes that only an expert can make. Errors – often catastrophic – that novices aren’t smart enough to make because they lack the information and experience needed to try to exploit an opportunity that doesn’t exist…

…Marc Andreessen explained how this has worked in tech: “All of the ideas that people had in the 1990s were basically all correct. They were just early.” The infrastructure necessary to make most tech businesses work didn’t exist in the 1990s. But it does exist today. So almost every business plan that was mocked for being a ridiculous idea that failed is now, 20 years later, a viable industry. Pets.com was ridiculed – how could that ever work? – but Chewy is now worth more than $10 billion.

Experiencing what didn’t work in 1995 may have left you incapable of realizing what could work in 2015. The experts of one era were disadvantaged over the new crop of thinkers who weren’t burdened with old wisdom…

…Doctors have their own version, as one article highlights:

“Almost all medical professionals have seen what we call “futile care” being performed on people. That’s when doctors bring the cutting edge of technology to bear on a grievously ill person near the end of life. The patient will get cut open, perforated with tubes, hooked up to machines, and assaulted with drugs. All of this occurs in the Intensive Care Unit at a cost of tens of thousands of dollars a day.

What it buys is misery we would not inflict on a terrorist. I cannot count the number of times fellow physicians have told me, in words that vary only slightly, “Promise me if you find me like this that you’ll kill me.” They mean it. Some medical personnel wear medallions stamped “NO CODE” to tell physicians not to perform CPR on them. I have even seen it as a tattoo.

The trouble is that even doctors who hate to administer futile care must find a way to address the wishes of patients and families. Imagine, once again, the emergency room with those grieving, possibly hysterical, family members. They do not know the doctor. Establishing trust and confidence under such circumstances is a very delicate thing. People are prepared to think the doctor is acting out of base motives, trying to save time, or money, or effort, especially if the doctor is advising against further treatment.”

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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 Intuitive Surgical, Meituan, Salesforce, and Starbucks. 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 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.