What We’re Reading (Week Ending 4 April 2021) - 04 Apr 2021
Reading helps us learn about the world and it is a really important aspect of investing. The legendary Charlie Munger even goes so far as to say that “I don’t think you can get to be a really good investor over a broad range without doing a massive amount of reading.” We (the co-founders of Compounder Fund) read widely across a range of topics, including investing, business, technology, and the world in general. We want to regularly share the best articles we’ve come across recently. Here they are (for the week ending 04 April 2021):
1. How mRNA Technology Could Change the World – Derek Thompson
But mRNA’s story likely will not end with COVID-19: Its potential stretches far beyond this pandemic. This year, a team at Yale patented a similar RNA-based technology to vaccinate against malaria, perhaps the world’s most devastating disease. Because mRNA is so easy to edit, Pfizer says that it is planning to use it against seasonal flu, which mutates constantly and kills hundreds of thousands of people around the world every year. The company that partnered with Pfizer last year, BioNTech, is developing individualized therapies that would create on-demand proteins associated with specific tumors to teach the body to fight off advanced cancer. In mouse trials, synthetic-mRNA therapies have been shown to slow and reverse the effects of multiple sclerosis. “I’m fully convinced now even more than before that mRNA can be broadly transformational,” Özlem Türeci, BioNTech’s chief medical officer, told me. “In principle, everything you can do with protein can be substituted by mRNA.”
In principle is the billion-dollar asterisk. mRNA’s promise ranges from the expensive-yet-experimental to the glorious-yet-speculative. But the past year was a reminder that scientific progress may happen suddenly, after long periods of gestation. “This has been a coming-out party for mRNA, for sure,” says John Mascola, the director of the Vaccine Research Center at the National Institute of Allergy and Infectious Diseases. “In the world of science, RNA technology could be the biggest story of the year. We didn’t know if it worked. And now we do.”…
…“There was a lot of skepticism in the industry when we started, because this was a new technology with no approved products,” Türeci told me. “Drug development is highly regulated, so people don’t like to deviate from paths with which they have experience.” BioNTech and Moderna pressed on for years without approved products, thanks to the support of philanthropists, investors, and other companies. Moderna partnered with the NIH and received tens of millions of dollars from DARPA, the Defense Advanced Research Projects Agency, to develop vaccines against viruses, including Zika. In 2018, Pfizer signed a deal with BioNTech to develop mRNA vaccines for the flu.
“The technology initially appealed to us for the flu because of its great speed and flexibility,” Philip Dormitzer, who leads Pfizer’s viral-vaccines research and development programs, told me. “You can edit mRNA very quickly. That is quite useful for a virus like the flu, which requires two updated vaccines each year, for the Northern and Southern Hemisphere.”
By the time the coronavirus outbreak shut down the city of Wuhan, China, Moderna and BioNTech had spent years fine-tuning their technology. When the outbreak spread throughout the world, Pfizer and BioNTech were prepared to shift immediately and redirect their flu research toward SARS-CoV-2. “It was really a case of our researchers swapping the flu protein for the coronavirus spike protein,” Dormitzer said. “It turned out that it wasn’t that big a leap.”
Armed with years of mRNA clinical work that built on decades of basic research, scientists solved the mystery of SARS-CoV-2 with astonishing speed. On January 11, 2020, Chinese researchers published the genetic sequence of the virus. Moderna’s mRNA vaccine recipe was finalized in about 48 hours. By late February, batches of the vaccine had been shipped to Bethesda, Maryland, for clinical trials. Its development was accelerated by the Trump administration’s Operation Warp Speed, which invested billions of dollars in several vaccine candidates, including Moderna’s. With the perfect timing of a Hollywood epic, mRNA entered the promised land after about 40 wandering years of research. Scientific progress had proceeded at its typical two-speed pace—slowly, slowly, then all at once.
2. More accuracy – Robert Vinall
As I look back on the letter, it struck me that the importance I place on striving for an accurate picture of the future might seem so obvious as to be hardly worth mentioning. After all, if a company’s intrinsic value is the sum of discounted future cash flow, why on earth would you not want to form as accurate a view of the future as possible? I can imagine my insistence on this point is particularly puzzling to younger investors whose formative years have been dominated by the boom in Internet stocks. “Doh!” they might exhort, “Of course you have to skate to where the puck is going, not where it was.”
The reason it is not obvious to older generations of value investors is that in our formative years, investing based on the assumption that historical patterns of cashflow generation would reassert themselves – better known as “reversion to the mean” – seemed the better strategy. Many of the great investing track records were built by investing in stable, unchanging businesses when they went through a period of underperformance on the assumption that they would eventually recover. It was an approach to investing that was based on a good understanding of a company’s history and the assumption that the future would not look too different to the past. It worked far better than betting on companies with short histories and big plans for the future, and it seemed obvious that it would continue to.
These two contrasting approaches to investing – one placing more weight on the future; the other on the past – are a reminder that the optimal strategy is a function of the era you invest in. If you are in a market characterised by rapid and widespread change, it pays to be forward-looking despite the inherent difficulty of judging the future. If, on the other hand, you are in a market where the pace of change is slower and more localised, then it may simply be better to bet on reversion to the mean as the future is too uncertain and genuine change too infrequent.
The contrasting outcomes of different brands of value investing in different eras pose an intriguing question. If each era selects for the type of investor who is best adapted to it, does the younger investor have an edge over the older one? My strong sense is “yes”. I am fortunate to know several successful younger investors, and they seem perfectly adapted to the market they invest in. I, by contrast, have had to adapt, which in practice does not so much mean learning new tricks as unlearning old ones. The former is certainly easier than the latter as learning is fun, but parting ways with cherished ideas is painful. Reluctant though I am to acknowledge it, as grey hairs begin to colonise my scalp, experience is a disadvantage.
In one important respect though, there is an advantage to experience. When the nature of the market does change, it should, at least in theory, be easier for the investor that has lived through different types of market to adapt than for the investor who has only experienced one type. The younger investor suddenly finds themselves in the position of the older investor without the benefit of having experienced a change in the market before.
In practice, however, few investors have sustained multidecade success. This may not solely be down to how difficult it is. It could also be that the rewards to the stellar performer are so great in one era that they lose interest in competing in the next one when they realise that their skills are no longer as finely attuned to the market. For sure though, it is a monumental challenge.
To increase the chances of adapting to different markets, I see one big thing an investor should do and one big thing they should not. The single biggest thing they should do is commit to adapt. The single biggest thing an investor should not do is tie themselves to a particular investment style or geography or industry or any other categorisation. These two points may sound obvious but generally, fund managers do the complete opposite. Investors in funds tend to look for a specific niche expertise in fund managers, and fund managers respond to this by developing a personal brand for a particular style of investing or segment of the market. Their brand promise is that they will not adapt.
3. Moore’s Law for Everything – Sam Altman
In the next five years, computer programs that can think will read legal documents and give medical advice. In the next decade, they will do assembly-line work and maybe even become companions. And in the decades after that, they will do almost everything, including making new scientific discoveries that will expand our concept of “everything.”
This technological revolution is unstoppable. And a recursive loop of innovation, as these smart machines themselves help us make smarter machines, will accelerate the revolution’s pace. Three crucial consequences follow:
1. This revolution will create phenomenal wealth. The price of many kinds of labor (which drives the costs of goods and services) will fall toward zero once sufficiently powerful AI “joins the workforce.” 2. The world will change so rapidly and drastically that an equally drastic change in policy will be needed to distribute this wealth and enable more people to pursue the life they want. 3. If we get both of these right, we can improve the standard of living for people more than we ever have before.
Because we are at the beginning of this tectonic shift, we have a rare opportunity to pivot toward the future. That pivot can’t simply address current social and political problems; it must be designed for the radically different society of the near future. Policy plans that don’t account for this imminent transformation will fail for the same reason that the organizing principles of pre-agrarian or feudal societies would fail today…
…AI will lower the cost of goods and services, because labor is the driving cost at many levels of the supply chain. If robots can build a house on land you already own from natural resources mined and refined onsite, using solar power, the cost of building that house is close to the cost to rent the robots. And if those robots are made by other robots, the cost to rent them will be much less than it was when humans made them.
Similarly, we can imagine AI doctors that can diagnose health problems better than any human, and AI teachers that can diagnose and explain exactly what a student doesn’t understand…
…The traditional way to address inequality has been by progressively taxing income. For a variety of reasons, that hasn’t worked very well. It will work much, much worse in the future. While people will still have jobs, many of those jobs won’t be ones that create a lot of economic value in the way we think of value today. As AI produces most of the world’s basic goods and services, people will be freed up to spend more time with people they care about, care for people, appreciate art and nature, or work toward social good.
We should therefore focus on taxing capital rather than labor, and we should use these taxes as an opportunity to directly distribute ownership and wealth to citizens. In other words, the best way to improve capitalism is to enable everyone to benefit from it directly as an equity owner
This is not a new idea, but it will be newly feasible as AI grows more powerful, because there will be dramatically more wealth to go around. The two dominant sources of wealth will be 1) companies, particularly ones that make use of AI, and 2) land, which has a fixed supply…
…We could do something called the American Equity Fund. The American Equity Fund would be capitalized by taxing companies above a certain valuation 2.5% of their market value each year, payable in shares transferred to the fund, and by taxing 2.5% of the value of all privately-held land, payable in dollars.
All citizens over 18 would get an annual distribution, in dollars and company shares, into their accounts. People would be entrusted to use the money however they needed or wanted—for better education, healthcare, housing, starting a company, whatever. Rising costs in government-funded industries would face real pressure as more people chose their own services in a competitive marketplace.
As long as the country keeps doing better, every citizen would get more money from the Fund every year (on average; there will still be economic cycles). Every citizen would therefore increasingly partake of the freedoms, powers, autonomies, and opportunities that come with economic self-determination. Poverty would be greatly reduced and many more people would have a shot at the life they want.
4. The Robots Are Coming For Your Office – Nilay Patel and Kevin Roose
[Patel] You just said, “We’re journalists, it’s an industry that employs automation to do parts of our job.” I think that gets kinda right to the heart of the matter, which is the definition of automation, right?
I think when most people think of automation, they think of robots building cars and replacing factory workers in Detroit. You are talking about something much broader than that.
[Roose] Yeah. I mean, that’s sort of the classic model of automation. And still, every time there’s a story about automation — and I hate this, and it’s like my personal vendetta against newspaper and magazine editors — every time you see a story about automation, there’s always a picture of a physical robot. And I get it. Most robots that we think of from sci-fi are physical robots. But most robots that exist in the world today, by a vast majority, are software.
And so, what you’re seeing today in corporate environments, in journalism, in lots of places, is that automation is showing up as software, that does parts of the job that, frankly, I used to do. My first job in journalism was writing corporate earnings stories. And that’s a job that has been largely automated by these software products now…
…[Patel] How big is the total RPA market right now?
[Roose] It’s in the billions of dollars. I don’t know the exact figure, but the biggest companies in this are called UiPath and Automation Anywhere and there are other companies in this space, like Blue Prism. But just UiPath alone is valued at something like $35 billion and is expected to IPO later this year. So, these are large companies that are doing many billions of dollars in revenue a year, and they’re working with most of the Fortune 500 at this point.
[Patel] And the actual product they sell, is it basically software that uses other software?
[Roose] A lot of it is that. A lot of it is, this bot will convert between these two file formats or it’ll do sort of basic-level optical character recognition so that you can scan expense reports and import that data into Excel, or something like that. So, a lot of it is pretty simple. You know, a lot of AI researchers don’t even consider RPA AI, because so much of it is just like static, rule-based algorithms. But a lot of them are starting to layer on more AI and predictive capability and things like that…
…[Patel] That feels like I could map it to a pretty familiar consumer story. You’ve got a factory, it’s got some output. It’s almost like a video game, right? You’ve got a factory, it’s got some output, you need to make X, Y, and Z parts in various quantities and you need to deliver on a certain time. And to some extent, your job is to play tower defense and just fill all the bins at the right time. Or you could just play against the computer and the computer will beat you every time. That’s what that seems like. It seems very obvious that you should just let the computer do it.
[Roose] Totally. And that’s the logic that a lot of executives have. And I don’t even know that that’s the wrong logic. Like I don’t think we should be preserving jobs that can be automated just to preserve jobs. The concern, I think I, and some other folks who watch this industry have, is that this type of automation is purely substitutive.
So in the past we’ve had automation that carried positive consequences and negative consequences. So the factory machines put some people out of their jobs, but they created many more jobs and they lowered the cost of the factories’ goods and they made it more accessible to people and so people bought more of them. And it had this kind of offsetting effect where you had some workers losing their jobs, but more jobs being created elsewhere in the economy that those people could then go do.
And the concern that the economists that I’ve talked to had, was that this kind of RPA, like replacing people in the back office, like it’s not actually that good.
It’s not the good kind of automation that actually does move the economy forward. It’s kind of this crappy, patchwork automation that purely takes out people and doesn’t give them anything else to do. And so I think on a macroeconomic level, the problem with this kind of automation is not actually how advanced it is, it’s how simple it is. And if we are worried about the sort of future of the economy and jobs, we should actually want more sophisticated AI, more sophisticated automation that could actually create sort of dynamic, new jobs for these people who are displaced, to go into…
…[Patel] So you’ve called them boring bots. You say the technology is not so sophisticated. The industry calls it RPA. Like, there’s a lot of pressure on making this seem not the most technologically sophisticated or exciting thing. It comes with a lot of change, but I’m wondering, are there any stories of RPA going horribly wrong?
[Roose] I’m just imagining like, I think the most consumer-facing automation is, you call the customer support line and you go through the phone tree. It makes all the sense in the world on paper: if all I need is the balance of my credit card, I should just press 5 and a robot will read it to me, but like I just want to talk to a person every time. Because that phone tree never has the options I want or it’s always confused or something is wrong. There has to be a similar story in the back office where the accounting software went completely sideways and no one caught it, right?
Yeah, I mean, there’s several stories like that in the book. There’s a trading firm called Knight Capital that had an algorithm go haywire and it lost millions of dollars in milliseconds. There was actually just a story in the financial markets — I forget who it was, it was one of the big banks — accidentally wired hundreds of millions of dollars to someone else and couldn’t get it back. And so it was just like, they just lost that. I’m sure that automation had some role in that, but that might have been a human error.
But there are also lower-level instances of this going haywire. One of the examples I talk about in the book is this guy Mike Fowler, who is an Australian entrepreneur who came up with a way to automate T-shirt design. So, I don’t know if you remember like five or six years ago, but there were all these auto-generated T-shirts on Facebook that were advertised. So, you know, it’d be like, “Kiss me, I’m a tech blogger who loves punk rock.” You know, and those would just be like Mad Libs, you know?…
…And so he made a lot of money doing this, and then one day it went totally wrong because he hadn’t cleaned up the word bank that this algorithm drew from. So there were people noticing shirts for sale on Amazon that were saying things like “Keep calm and hit her,” or, “Keep calm and rape a lot.” Like just words that he had forgotten to clean out of the database, and so as a result, his store got taken down. He lost all his business. He had to change jobs, like it was a traumatic event for him. And that’s a colorful example but there are, I’m sure, lots of more mundane examples of this happening at places that have implemented RPA.
5. The Big Lessons of the Last Year – Morgan Housel
Jason Zweig explained years ago that part of the reason the same mistakes repeat isn’t because people don’t learn their lesson; it’s because people “are too good at learning lessons, and they learn overprecise lessons.”
A good lesson from the dot-com bust was the perils of overconfidence. But the lesson most people took away was “the stock market becomes overvalued when it trades at a P/E ratio over 30.” It was hyperspecific, so many of the same investors who lost their shirts in 2002 got up and walked straight into the housing bubble, where they lost again.
The most important lessons from a big event are usually the broad, 30,000-foot takeaways. They’re more likely to apply to the next iteration of crisis.
Covid-19 is far from over, but we’re now more than a year into this tragic mess. Enough has happened that we can start to ask “what lessons have we learned?” If you’re a doctor or a health regulator, some of those lessons are hyperspecific. But for most of us the biggest lessons are broad…
…A virus shutting down the global economy and killing millions of people seemed remote enough for most people to never contemplate. Before a year ago it sounded like the one-in-billions freak accident only seen in movies.
But break the last year into smaller pieces.
A virus transferred from animal to human (has happened forever) and those humans interacted with other people (of course). It was a mystery for a while (understandable) and bad news was likely suppressed (political incentives, don’t yell fire in a theater). Other countries thought it would be contained (exceptionalism, standard denial) and didn’t act fast enough (bureaucracy, lack of leadership). We weren’t prepared (common over-optimism) and the reaction to masks and lockdowns became heated (of course) so as to become sporadic (diversity, same as ever). Feelings turned tribal (standard during an election year) and a rush to move on led to premature reopenings (standard denial, the inevitability of different people experiencing different realities).
Each of those events on their own seems obvious, even common. But when you multiply them together you get something surprising, even unprecedented.
Big risks are always like that, which makes them too easy to underestimate. How starkly we have been reminded over the last year.
6. What Is Archegos and How Did It Rattle the Stock Market – Juliet Chung and Margot Patrick
Archegos is the family investment vehicle owned by Mr. Hwang, a former protégé of hedge-fund titan Julian Robertson. Mr. Hwang was a so-called Tiger cub, an offshoot of Mr. Robertson’s Tiger Management. Mr. Hwang founded Tiger Asia in 2001. Based in New York, it went on to become one of the biggest Asia-focused hedge funds, running more than $5 billion at its peak. In 2008, it was one of a swath of funds that suffered losses related to the soaring share price of Volkswagen AG of Germany
In 2012, Tiger Asia said it planned to hand money back to investors. Later that year, the firm pleaded guilty to a criminal fraud charge for using inside information from investment banks to profit on securities trades. Mr. Hwang and Tiger Asia paid $44 million to settle a related civil lawsuit, The Wall Street Journal reported at the time.
Mr. Hwang turned Tiger Asia into his family office and renamed it Archegos, according to its website…
…Archegos took big, concentrated positions in companies and held some positions via something called “total return swaps.” Those are contracts brokered by Wall Street banks that allow a user to take on the profits and losses of a portfolio of stocks or other assets in exchange for a fee.
Swaps allow investors to take huge positions while posting limited funds up front, in essence borrowing from the bank. The use of swaps allowed Mr. Hwang to maintain his anonymity, even as Archegos was estimated to have had exposure to the economics of more than 10% of multiple companies’ shares. Investors holding more than 10% of a company’s securities are deemed to be company insiders and are subject to additional regulations around disclosures and profits.
Swaps are common and have been around for a long time. They are also controversial. Long Term Capital Management, a hedge fund advised by two Nobel laureates that nearly brought down Wall Street in the late 1990s, used swaps. Warren Buffett wrote about the risks of swaps in his 2003 letter to investors.
7. Twitter thread on how a software entrepreneur burned US$10 million – Andrew Wilkinson
This is a story about how I lost $10,000,000 by doing something stupid. Ten. Million. Dollars. Literally up in smoke. Money bonfire. That’s enough to retire with $250,000+ in annual income. Here’s what happened…
In 2009, @metalab was a small but profitable agency. The business was making a couple hundred thousand dollars a year in annual profit and I was trying to figure out how to invest the profits. Agencies can be great businesses, but they are HARD.
You lose clients at random, your pipeline dries up on a dime. It’s feast or famine and unpredictable. I kept reading about what @dhh and @jasonfried were doing with Basecamp, building software for themselves then selling monthly access to it.
This was a relatively new concept back in those days, and it seemed like they were living the dream. I had a business crush. The model they used for Basecamp was:
1. Build great software that scratched their own itch (project management) 2. Assume others have this problem 3. Charge a monthly recurring amount to give them access (SaaS) 4. Focus on organic growth via product improvement and public writing 5. Spend less than they make 6. Profit…
…I loved Jason and David’s focus on building a business on your own terms, in a way that made you happy. I hated the idea of having some annoying VC involved, pressuring me to grow or move to San Francisco (believe it or not, that was almost 100% required at the time)…
…I was a huge to-do list junkie, but back then all of the task apps were either single-player or weird desktop apps with syncing issues. I decided to build a shared to-do list app for teams.
I grabbed a couple of devs from the agency and we started working on it. About 9 months later we were in beta. We called it Flow, and it was actually really cool. From day one, it was a huge hit. A lot of people had the same problem and there was nothing else like it…
…When we turned on billing for our beta users, we jumped to $20k MRR in the first month. We started growing at 10% per month and were the new hotness. I got reach outs from all the top VCs and tons of tech luminaries started using the product. We’d made it…or so I thought.
There was just one problem: I was consistently spending 2-3x our monthly revenue and losing money. And not venture capital. Out of my personal bank account.
Then I heard a name start popping up. Quietly at first, then a lot. Asana.
It turned out that Dustin Moskovitz (@moskov), the billionaire co-founder of Facebook, was a fellow to-do list junkie, and he was quietly working on his own product. A few months later it went live. And I breathed a big sigh of relief.
It was ugly! It was designed by engineers. Complicated and hard to use. Not a threat in the slightest. I felt validated: With a team a quarter of the size, and a fraction of the money, we had built what I felt was a superior product.
Around this time, Dustin invited me for a coffee in San Francisco. He implied—in the nicest possible terms—that they were going to crush us. (Emphasis on nice, he is a very nice, humble dude. Both Dustin and @christianreber, my two key competitors, turned out to be mensches)
Disclaimer: None of the information or analysis presented is intended to form the basis for any offer or recommendation. Of all the companies mentioned, we currently have a vested interest in Amazon and Facebook. Holdings are subject to change at any time.
