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 05 December 2021:
1. Assured Misery – Morgan Housel
Most things you envy look better from the outside, because everyone crafts a selected image of what they’re doing and they are. A lot of time you’re envious of someone specifically because that person has done a good job crafting the image of their life. But since they’re crafting the image, it’s not a complete view. There’s a filter. Skills are advertised, flaws are hidden.
Instagram is full of beach vacation photos, not flight delay photos. Resumes highlight career wins but are silent on doubt and worry. Investing gurus are easy to elevate to mythical status because you don’t know them well enough to witness times when their decision-making process was ordinary, if not awful.
The problem is that when you are keenly aware of your own struggles but blind to others’, it’s easy to assume you’re missing some skill or secret that others have. It’s a sure path to feeling inadequate.
Everyone’s dealing with problems they don’t advertise, at least until you get to know them well. Keep that in mind and you become less envious and more forgiving – to yourself and others.
2. Storm in the stratosphere: how the cloud will be reshuffled – Eric Bernhardsson
Here’s a theory I have about cloud vendors (AWS, Azure, GCP):
- Cloud vendors1 will increasingly focus on the lowest layers in the stack: basically leasing capacity in their data centers through an API.
- Other pure-software providers will build all the stuff on top of it. Databases, running code, you name it…
…There’s some sort of folk wisdom that the lowest layer of cloud services is a pure commodity service. So in order to make money you need to do at least one of:
- Make money higher up in the stack.
- Use services higher up in the stack to lock customers in. Then make money lower in the stack.
I think there’s some truth to these, at least looking historically, but there are some interesting trends blowing in the other way:
- The “software layer on top” is getting incredibly competitive. There’s so many startups going after it, fueled by cheap VC money and willing to burn billions of dollars on building software.
- Cloud vendors might be pretty happy making money just in the lowest layer. Margins aren’t so bad and vendor lock-in is still pretty high.
3. Will Marshall – Indexing the Earth – Patrick O’Shaughnessy and Will Marshall
[00:05:09] Patrick: I think one of the coolest trends in technology generally is making certain things legible to software through data sets, through image sets in your case, et cetera, and the wild things that can then be built on or learned on top of those emergent data sets. And so I’d love to tell that whole story through the Planet lens in as much detail as we can, maybe going all the way back to the beginning. You have the benefit of literally starting, I think, the first satellite, it was built in a garage. So the proverbial garage was a real one in your case. Take us back to the earliest days of Planet its origin story and why you started the business.
[00:05:43] Will: Yeah. I’m happy to do that. And by the way, the investors of dMY the group that we are merging with as part of our going public, his name’s Niccolo, and he says all the best software companies build their own hardware, Apple, you can think of like that. Tesla, he considers a software company, but he’s building his own hardware. There’s a way of viewing that. And I think certainly Apple’s, I think, a really fantastic example. We feel that the same. We’re not selling the satellite hardware, we are selling data that is produced from that. But by building our own satellite hardware, we create much efficiencies in that, back to the garage days, myself and a small team about seven of us left NASA to start Planet. And we literally started building the satellites in our garage. What we were thinking about at that time was how do we as space geeks help all these challenges of the world? From poverty, to climate change, to everything that was going on.
And we felt that there’s a huge business opportunity as well in one and the same mission, which was to image the whole earth every day, because we thought that data would spur economic development and it would help with these real huge problems. And no one had ever done that because it required at least about 100 satellites into orbit. Well, no one had ever launched 100 satellites and we’d have to do things very differently. We had been pioneering at NASA, this thing called the small spacecraft office, which was low cost, planetary missions and other things. And we were trying to take that a couple of steps further and say, okay, can we even leverage consumer electronics? Like the kind of electronics that’s in your laptop or in your phone in space and thereby instead of spending billions of dollars per satellite, spend a lot less, like 1000 times less than that, or 10,000 times less than that, like a $1 million or a $100,000 and do something like image the whole planet every day. We figured that we could and so we left NASA to do that. And six and a bit years later, we achieved that mission of having launched the largest constellation of satellites in history. We had just over 100 satellites in orbit. SpaceX, by the way, is just overtaken us recently in numbers of satellites.
[00:07:51] Patrick: The bastards.
[00:07:53] Will: Not that I’m too upset about that, by the way, if you count the number of satellites in orbit, they doubled roughly in the last five years and half of the satellites are now of two companies, that’s Planet and SpaceX. So although there’s a proliferation, it’s really quite concentrated at the present time. But anyway, yeah. So we started building our satellites in the garage and six or seven years later, we got to our daily cadence having lots of more of these satellites, it was much harder than I thought. I thought we could do it in three years or, and it took six, but we achieved something that was really cool.
And we have this brand new data set of an image of the entire earth every day. So it’s a bit like when you go onto Google and you see the satellite layer, that image is maybe three or five years old. We are doing that every day for the whole planet and we are keeping all of those images, so we have about 1700 images, for every point on the land of the earth, so a deep stack of images. So it’s like Google, but with a time access.
So it’s a recent data and a tall stack of information. And that’s how we figure out what’s changing on the planet, where all the resources are being moved, all the vehicles, agriculture, shipping, we figure all the changes on the planet over time. And we can train all our machine learning on top of that stack of data. You ask any machine learning expert, what’s the most exciting thing? It’s training data, well, we have gobs of it. We have 1700 images, as I said, of very place on the earth’s land mass on average, to train all your algorithms about what’s going on…
…[00:15:58] Patrick: Let’s talk now about how customers interface with you. I love the story of starting in the garage, building a very low-cost satellite, the Dove satellites, getting the constellation up there, mission accomplished. It’s going to keep compounding and getting higher and higher resolution. What was the first commercial interaction that you had? Did you plan to cater to the agriculture industry to start or did that emerge as sort of like an unpredicted or unpredictable use case?
[00:16:24] Will: We had thought about that use case as a very early one. One way to think about it is look at the areas of the earth and how are they used. About a quarter of the land mass of the earth, 25%, is agricultural land, so we went into that. A quarter of the land mass of the earth is forestry, so we went into that. About 10% is urban development, the suburb and/or urban, then there’s of course large areas of marine. Anyway, but agriculture was an obvious one just from a sheer area. We knew that we could do something useful in agriculture within the infrared band. That’s a known thing from land establishment been doing this for 40 years, except it’s just doing it in a slower cadence, which makes it less useful growing the crops and helping the crop development. It’s more for the annual survey of, “How did we do?” Which is useful at the end of the year, but it’s not helpful for the grower in the crop growing season.
Because what the farmer wants is intelligence about their field a few times a week during the crop growing season. And so by having daily information at that three-by-three meter area of every field of that farm, every one of their fields, we can help them do what’s called precision agriculture. That can improve their crop yield by 20 to 40%, at the same time decrease their use of things like fertilizer and other resources by similar amounts. That’s a big deal if you think of that. We can do that. Of course, a trillion dollar industry. This is, as I said, the general notion of big data and AI enabling the digital transformation of industries. Ag is a canonical example. We do that, for example, with Corteva. They use this to image about a million farmers’ fields every day. This is not a minor operation and the lot of those kind of companies that can use our data to help with precision ag.
The other areas, we help civil governments, normally things like disaster response. It’s also science. So we work with NASA on a lot of science, like climate science, but we also work with state and local governments and federal governments on things like floods and fires. We have been helping recently with the floods in Germany, where they had a lot of big floods, the biggest floods there for 60 years, with the fires here in California, helping the world wildfires. We can both detect where the fires are to help the firefighters, like where’s the wind blowing, where’s the edge of the fire, where is it compared with whatever hill? So we can help with it real time and we can help with preventative work. So we can actually, and we have, map every tree in California and where’s the fodder for growth for future mega fires? That can help them to then make clearing or do a fire lane in the future to stop future mega fires. So it’s preventative work.
Same with flooding, we can see the flood the day before, the day after the flood, and see which bridge is down to help the relief effort. But we can also then model where’s the floodplain going to be, which buildings are in the wrong place, where should they build and not build. Especially in developing countries, are often build in the wrong place, so we have a big project with Google where we’re doing this in Bangladesh and India. It’s because the floodplains change, they can then advise governments, “Don’t build here, do build here.” That’s important for getting assets out of the way of the flood. It’s also important for finance, by the way, because they also want to understand which assets are at risk from these various disasters or potential disasters. So civil government’s another area.
We also work with mapping. There’s a fascinating use case, and this is where it gets into what consumers might actually see, because we’re not actually selling the imagery to consumers, we’re selling to big businesses, but it does affect day to day people because the maps that you have online are kept up-to-date with our data. The news you see about world events, we’re sort of shedding light about everything going on on the planet. Pulitzer prize this year was won by some journalist who used our data to discover about 200 potential Uyghur, that’s Muslim detention camps in Western China that had previously not been known. And that sort of is really important for journalism. That happens using our data to uncover what’s going on around the world.
And also if you grew up in a disaster, our data might be helping the disaster responders to help you. So it actually affects people day to day. But with mapping that one case, we work with Google, for example, on updating the maps that you see online all the time, whenever they find any indication that map is going out date, they automatically task our stuff, it takes a picture of that location, extracts out that new road, that new train station, that new building, whatever it is, and then it updates the map you see online so that your directions and everything stay up to date. So there’s a lot of different applications. I’m just giving you a sample…
…[00:31:39] Patrick: If you stick in the earth-observation planet category, what do you think are the most far-fetched sci-fi potential futures for what the technology might enable, say, the rest of your career or something like that? On some timeline that’s long but reasonable.
[00:31:52] Will: Well, I do think that it’s possible in the long term that we would more or less be able to have a live image of the earth. I think that that’s possible. It’s not where we are now, and it won’t be for a long time, but it’s possible. And then the second thing is I imagine that you should be able to just query that. You should just be able to write… Just imagine a search query box on it and you can just say, “Hey, how many houses are there in Pakistan? Give me a plot of that versus time. Tell me where the trees were deforested, the latitude and longitudes of the trees that deforested in the Amazon in the last three weeks.”
It should be able to just tell you the answer to those questions without ever you looking at the images, or it may highlight that in the background or something, but you should be able to get answers, just like you can from Google. So I think a lot of what Planet’s doing in the long arm is a little bit similar to Google. Google figured out how to index the internet and make it searchable, and we are figuring out how to index the earth and making it searchable with the combination of the data and machine learning that sits on top…
…[00:34:02] Patrick: With this unique data set moving up the stack that you’ve done, what knowledge about the Earth has most surprised you that’s resulted from this data set so far?
[00:34:11] Will: Well, the degree of calamity of the destruction of ecosystems is just staggering. So we are wiping out forests, mainly to put cows on them so we can eat beef-burgers. We are destroying the fisheries with ocean trawling. We are seeing huge transitions. Because it’s not actually climate change. It’s mainly these things like deforestation and illegal fishing. So it’s been driven by humans deciding to change the use of those territories, and that is just really obvious and sad, but hopefully, our data can help companies and countries and individuals better manage the planet. We’ve, what, lost 70% of life on the planet in the last 40 years? It’s gobsmacking to think about. We are just whittling it away still. Our raison d’être, in many ways, is to help stop that, is to help the governments to see the deforestation. So we have a project to map all… Which we do. All the deforestation in the 64 tropical countries, with the Government of Norway paying for the data, helping that data be available to those forestry ministries to stop deforestation. It’s a huge project.
We have another one to map all the world’s coral reefs, and we just released that a few weeks ago, which is the first map of all the world’s corals, classification of different types, and showing early signs of bleaching. Or if there’s any illegal fishing going on, we can alert governments. In fact, there are already six countries have established marine-protected areas around coral reefs that we mapped for them. So those are the kind of things that can really help us protect and stop the eco side that’s happening on the planet. And so that is both a thing that’s super important for the planet, and it’s a massive business opportunity, because what is really happening is that global economy is going, “Ah, we cannot any longer presume that natural capital is free.” That the trees you cut down, and it’s free for the landowner; they can just cut down that tree and it’s cost nothing. Or you can just put gases into the atmosphere and it doesn’t matter, or pollutants into the rivers and it doesn’t matter.
There’s an externality of that cost. We’ve got to integrate the externality into our economic system. And that is the countries saying, “We’re going to measure these emission targets and set these limits,” and it’s companies doing their ESG targets and saying, “Hey, the environment piece I’m going to measure and make sure that my resources came from a sustainable source, or make sure I don’t build those assets in a flood-risk zone,” or these sort of things. And what does that mean? All of that means those countries and the companies have to measure all that stuff. So this is a massive business opportunity, because we have the data set that’s pretty foundational to the measurement of all that natural capital. It’s measuring all those things. It’s not just that we can see every tree and stop deforestation. We can count the carbon stock in all those trees. Our data is foundational to underpinning the transition to a sustainable economy, which is a multi-trillion dollar transition as well.
4. The Meaning of Decentralization – Vitalik Buterin
When people talk about software decentralization, there are actually three separate axes of centralization/decentralization that they may be talking about. While in some cases it is difficult to see how you can have one without the other, in general they are quite independent of each other. The axes are as follows:
- Architectural (de)centralization — how many physical computers is a system made up of? How many of those computers can it tolerate breaking down at any single time?
- Political (de)centralization — how many individuals or organizations ultimately control the computers that the system is made up of?
- Logical (de)centralization— does the interface and data structures that the system presents and maintains look more like a single monolithic object, or an amorphous swarm? One simple heuristic is: if you cut the system in half, including both providers and users, will both halves continue to fully operate as independent units?…
…Note that a lot of these placements are very rough and highly debatable. But let’s try going through any of them:
- Traditional corporations are politically centralized (one CEO), architecturally centralized (one head office) and logically centralized (can’t really split them in half)
- Civil law relies on a centralized law-making body, whereas common law is built up of precedent made by many individual judges. Civil law still has some architectural decentralization as there are many courts that nevertheless have large discretion, but common law have more of it. Both are logically centralized (“the law is the law”).
- Languages are logically decentralized; the English spoken between Alice and Bob and the English spoken between Charlie and David do not need to agree at all. There is no centralized infrastructure required for a language to exist, and the rules of English grammar are not created or controlled by any one single person (whereas Esperanto was originally invented by Ludwig Zamenhof, though now it functions more like a living language that evolves incrementally with no authority)
- BitTorrent is logically decentralized similarly to how English is. Content delivery networks are similar, but are controlled by one single company.
- Blockchains are politically decentralized (no one controls them) and architecturally decentralized (no infrastructural central point of failure) but they are logically centralized (there is one commonly agreed state and the system behaves like a single computer)
Many times when people talk about the virtues of a blockchain, they describe the convenience benefits of having “one central database”; that centralization is logical centralization, and it’s a kind of centralization that is arguably in many cases good (though Juan Benet from IPFS would also push for logical decentralization wherever possible, because logically decentralized systems tend to be good at surviving network partitions, work well in regions of the world that have poor connectivity, etc; see also this article from Scuttlebot explicitly advocating logical decentralization).
Architectural centralization often leads to political centralization, though not necessarily — in a formal democracy, politicians meet and hold votes in some physical governance chamber, but the maintainers of this chamber do not end up deriving any substantial amount of power over decision-making as a result. In computerized systems, architectural but not political decentralization might happen if there is an online community which uses a centralized forum for convenience, but where there is a widely agreed social contract that if the owners of the forum act maliciously then everyone will move to a different forum (communities that are formed around rebellion against what they see as censorship in another forum likely have this property in practice).
Logical centralization makes architectural decentralization harder, but not impossible — see how decentralized consensus networks have already been proven to work, but are more difficult than maintaining BitTorrent. And logical centralization makes political decentralization harder — in logically centralized systems, it’s harder to resolve contention by simply agreeing to “live and let live”.
5. Academic urban legends – Ole Bjørn Rekdal
Bauerlein et al. (2010) claim that we are currently experiencing an ‘avalanche of low-quality research’, and academia has become an environment where ‘[a]spiring researchers are turned into publish-or-perish entrepreneurs, often becoming more or less cynical about the higher ideals of the pursuit of knowledge’. Whether the current state of affairs is better or worse than before, it seems reasonable to assume that corner-cutting is an unfortunate side effect of publication pressure and competition for academic positions and scarce resources, especially in milieus where counting publications is more important than reading and evaluating them. In this article, I explore a particular set of corner-cutting techniques that reveal much about strategies of reading, writing, and citation, as well as the development of academic urban legends.
The digital revolution within academia
Twenty-five years ago, it could take weeks to obtain a specific source document needed in order to verify or explore a reference that for some reason had caught one’s attention. As a consequence of the digital revolution, it is possible today to obtain a wide spectrum of sources within minutes or seconds. Formidable databases, advanced scanners, optical character recognition (OCR) technology, and new features of reference management software such as Endnote have made it possible, with some experience, to read an academic text together with the sources it refers to.
For those of us who are old enough to know what a card catalog is, it is outright fascinating to sit in front of a computer with two monitors, reading an academic text on one of them and having the sources it refers to (or perhaps should have referred to) on the other. Having immediate access to most, if not all, of the sources behind an academic text opens up a number of exciting opportunities, but also exposes some unpleasant surprises. In the past few years, it has become dramatically easier to identify cases of plagiarism and scientific misconduct, and also to discover other types of academic shortcuts, and to see how shockingly frequently they are employed.
In this article, I will limit my focus to one specific type of situation that seems to cause problems for a large number of researchers and students and that provides a breeding ground for a wide variety of academic shortcuts. The situation of interest is one that all writing academics will encounter numerous times during their career: when we read a text and find a statement or specific point that we would like to use ourselves, and we discover that it is already accompanied by a reference.
6. China’s secret plan to become tech self-sufficient – Jeff Pao
A little-known group of experts is developing a roadmap to reduce China’s reliance on foreign technology and promote self-sufficiency, a high-stakes gambit that could change how China interacts and competes in the crucial and fast-moving global sector.
The advisory group, known as the National Science and Technology Advisory Committee (NSTAC), was only made public two years after its establishment in 2019. It recently submitted a secretive report focused on technology self-sufficiency that was deliberated by top Chinese officials, according to reports.
The committee’s creation was first raised by President Xi Jinping in February 2017, two weeks after the presidential inauguration of former-US president Donald Trump, who went on to ban Huawei and ZTE products in the US and restricted chip exports to China in the name of national security.
7. The Age of Funcertainty! – Joshua Brown
Can anyone deny that the last 18 months has been among the top stock market rollercoasters of all time? Existential panic to unbridled euphoria almost overnight. The worst, most sudden economic downturn in a century to the fastest stock market double in seventy years. But the speed of the recovery isn’t the real story here. It’s the giddiness with which we’re riding this recovery that is so remarkable, recognizable to anyone whose ever stumbled off a looping theme park coaster. Think about the last time you were at Six Flags or Disney or Universal. Getting off that first rollercoaster of the day. Even the people who were scared to climb on are climbing off with giant, goofy smiles plastered to their faces. First in line to see the picture of their car (or log, in the case of a flume) developed and for sale at the foot of the ride’s egress. There’s a glistening sweat on the foreheads of the riders, despite the fact they didn’t actually do anything but sit and grip. It’s chemical. It’s almost sexual. Again, again, again!
There’s a heightened and sustained state of arousal in today’s markets, borne of easy gains and a de facto risk-free environment. The current zeitgeist engendered by oceans of fiscal stimulus, a dearth of corporate bankruptcies, endless financing and equity capital on offer and the ironclad promises of the central banks to let the stimulus and interest rate bonanza run on “lower for longer” this time. The backdrop against which this is all taking place is a starry sky of daily IPOs, SPACs and record-setting venture capital superlatives – the largest fund, most money raised, highest amount of billion-dollar startups, most gigantic valuations, fattest first-day founder scores…You can’t look away or pretend for a moment you’re immune to the thirst. It’s practically pornographic and, well, you’re only human. We all have desires.
Where did this lust come from? So soon after a near-death experience for our entire way of life? In the first 15 months of the plague, nearly 1 in 500 Americans died. Millions got sick, around the world, and a not-insignificant percentage of them had to be hospitalized. Living through this, and surviving – not just surviving the illness but all of the fear and doubt – it can produce a very counterintuitive feeling of joy. Dare we call it a thrill? The opposite of survivor’s guilt. Survivor’s appreciation for life. A sort of bravery can set in. What doesn’t kill me makes me stronger. You’ve heard the term distress but its antonym may not be familiar to you: Eustress. The word eustress refers to the positive kind of the stress, which is actually beneficial to the person experiencing it. It comes from the Greek root eu, meaning good. Same root as Euphoria. Eustress is why the discos and nightclubs in Tel Aviv fill up after a terror attack hits the city. It’s why you walk off a rollercoaster grinning like a f***ing idiot.
Disclaimer: The Good Investors is the personal investing blog of two simple guys who are passionate about educating Singaporeans about stock market investing. By using this Site, you specifically agree that none of the information provided constitutes financial, investment, or other professional advice. It is only intended to provide education. Speak with a professional before making important decisions about your money, your professional life, or even your personal life. We currently do not have a vested interest in any company mentioned. Holdings are subject to change at any time.