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 November 2023:

1. 11 Signs to Avoid Management Meltdowns – Todd Wenning

Pressure to maintain those numbers

Anyone who’s made it to the C-suite understands that missing Wall Street estimates can result in a stock price drop. There’s natural pressure to satisfy investors, particularly when the stock price drives a big part of employee compensation.

Some of that pressure can be good, but it can also lead to unethical decisions when a company can’t achieve those numbers in the ordinary course of business. A company may, for example, stuff a channel with inventory to pull forward demand. That can work for a while, but eventually, all the customers’ warehouses are full.

Companies might also make an acquisition, alter segment reporting, or take some type of restructuring initiative to reset investor expectations. These moves should be viewed with skepticism…

…Young’uns and bigger-than-life CEOs

An iconic CEO who surrounds themselves with young, ambitious employees can be a warning sign. Jennings argues that’s because:

“These young’uns don’t have enough experience or wisdom to challenge the CEO, and the CEO has roped them in with executive success. They are hooked on the cash and its trappings and cannot speak up about obvious ethical and legal issues because they might lose the homes, the boats, the cars, and, yes, the prestige that comes with astronomical financial success at a young age.”

In contrast, when a CEO has an experienced team who – critically – have financial and professional options other than working at the company, it’s far less likely (though not impossible) for misbehavior to persist for long…

…Innovation like no other

As technological advances accelerate, we’re more frequently dazzled by their potential impacts. Jennings warns that companies behind these technologies may consider themselves “as being above the fray, below the radar, and generally not subject to the laws of either economics or gravity.”

Founders and executives of tremendously successful companies often receive accolades from the business and financial media, as well as their local communities. In turn, this feedback can create an inflated sense of self-importance.

To illustrate, here’s a clip from a December 2000 press release announcing that Fortune magazine named Enron one of the 100 best companies to work for in America.

Enron adds the “100 Best Companies to Work For in America” distinction to its “Most Innovative Company in America” accolade, which it has received from Fortune magazine for the past five years. The magazine also has named Enron the top company for “Quality of Management and the second best company for “Employee Talent.”

When a company gets this type of public reinforcement, it can provide mental cover for justifying other actions.

As an antidote to this red flag, Jennings suggests being on the lookout for how management responds to external questions about the company, its performance, or its tremendous growth. If rather than thoughtfully respond to a tough question, management launches an ad hominem attack against the questioner, be on your guard…

…Obsession with short sellers: If the company has been the target of a well-distributed short thesis, there are two appropriate responses for the types of companies we want to own. One is ignore it and focus on the business. In 1992, when Fastenal founder Bob Kierlin was asked about the huge short interest in his stock, he replied: “I’ve got nothing against short sellers…They have a role in the market place, too. My own portfolio has a couple of short positions. In the long run, the truth will always come out.” The second is to calmly and thoughtfully respond to short seller concerns like Netflix’s Reed Hastings did in reply to Whitney Tilson. Any other type of response – particularly when it’s driven by emotion – is a warning sign.

2. Waking up science’s sleeping beauties – Ulkar Aghayeva

Some scientific papers receive very little attention after their publication – some, indeed, receive no attention whatsoever. Others, though, can languish with few citations for years or decades, but are eventually rediscovered and become highly cited. These are the so-called ‘sleeping beauties’ of science.

The reasons for their hibernation vary. Sometimes it is because contemporaneous scientists lack the tools or practical technology to test the idea. Other times, the scientific community does not understand or appreciate what has been discovered, perhaps because of a lack of theory. Yet other times it’s a more sublunary reason: the paper is simply published somewhere obscure and it never makes its way to the right readers…

…The term sleeping beauties was coined by Anthony van Raan, a researcher in quantitative studies of science, in 2004. In his study, he identified sleeping beauties between 1980 and 2000 based on three criteria: first, the length of their ‘sleep’ during which they received few if any citations. Second, the depth of that sleep – the average number of citations during the sleeping period. And third, the intensity of their awakening – the number of citations that came in the four years after the sleeping period ended. Equipped with (somewhat arbitrarily chosen) thresholds for these criteria, van Raan identified sleeping beauties at a rate of about 0.01 percent of all published papers in a given year.

Later studies hinted that sleeping beauties are even more common than that. A systematic study in 2015, using data from 384,649 papers published in American Physical Society journals, along with 22,379,244 papers from the search engine Web of Science, found a wide, continuous range of delayed recognition of papers in all scientific fields. This increases the estimate of the percentage of sleeping beauties at least 100-fold compared to van Raan’s.

Many of those papers became highly influential many decades after their publication – far longer than the typical time windows for measuring citation impact. For example, Herbert Freundlich’s paper ‘Concerning Adsorption in Solutions’ (though its original title is in German) was published in 1907, but began being regularly cited in the early 2000s due to its relevance to new water purification technologies. William Hummers and Richard Offeman’s ‘Preparation of Graphitic Oxide’, published in 1958, also didn’t ‘awaken’ until the 2000s: in this case because it was very relevant to the creation of the soon-to-be Nobel Prize–winning material graphene.

Both of these examples are from ‘hard’ sciences – and interestingly, in physics, chemistry, and mathematics, sleeping beauties seem to occur at higher rates than in other scientific fields.

Indeed, one of the most famous physics papers, Albert Einstein, Boris Podolsky, and Nathan Rosen (EPR)’s ‘Can Quantum-Mechanical Description of Physical Reality Be Considered Complete?’ (1935) is a classic example of a sleeping beauty. It’s number 14 on one list that quantifies sleeping beauties by how long they slept and how many citations they suddenly accrued…

…The EPR paper wasn’t hidden in a third-tier journal, unread by the scientific community. Indeed, it generated intense debate, even a New York Times headline. But in terms of its citations, it was a sleeper: it received many fewer citations than one would expect because it needed testing, but that testing wasn’t feasible for a long time afterward…

…In some cases, a sleeping beauty comes without the kind of great mystery attached to the EPR paper. In some cases, scientists understand something well enough – but just don’t know what to do with it.

The first report of the green fluorescent protein (GFP) – a crucial ingredient in many modern biological experiments because of its ability glow brightly under ultraviolet light, and thus act as a clear indicator of cellular processes like gene expression and protein dynamics – was published in 1962 in the Journal of Cellular and Comparative Physiology. GFP had been discovered in the jellyfish Aequorea victoria in research led by the marine biologist Osamu Shimomura.

Over the summers of the following 19 years, 85,000 A. victoria jellyfish were caught off Friday Harbor in Washington state in attempts to isolate sufficient amounts of GFP that allowed for a more thorough characterization. This resulted in a series of papers between 1974 and 1979. But as Shimomura admitted in one of the interviews many years later, ‘I didn’t know any use of . . . that fluorescent protein, at that time.’

In 1992, things changed. The protein was cloned, and the relevant genetic information was passed on to the biologist Martin Chalfie. Chalfie was first to come up with the idea of expressing GFP transgenically in E. coli bacteria and C. elegans worms. He demonstrated that GFP could be used as a fluorescent marker in living organisms, opening up new worlds of experimentation. GFP is now a routinely used tool across swathes of cell biology…

…With that caveat on the record, we can look at a final example of a true sleeping beauty – one that perhaps has the most to teach us about how to awaken dormant knowledge in science.

In 1911, the pathologist Francis Peyton Rous published a paper in which he reported that when he injected a healthy chicken with a filtered tumor extract from a cancerous chicken, the healthy chicken developed a sarcoma (a type of cancer affecting connective tissue). The extract had been carefully filtered to remove any host cells and bacteria, which might be expected to cause cancer, so another factor must have been at play to explain the contagious cancer.

It turned out that the cause of the tumor in the injected chicken was a virus – but Rous wasn’t able to isolate it at the time.

The importance of his study, and the paper reporting it, wasn’t recognized until after 1951, when a murine leukemia virus was isolated. This opened the door to the era of tumor virology – and to many citations for Rous’s initial paper. The virus Rous had unknowingly discovered in his 1911 paper became known as the Rous sarcoma virus (RSV), and Rous was awarded the Nobel Prize in Medicine in 1966, 55 years after publishing…

…Another lesson is related to collaboration. It could be that the techniques and knowledge required to fully exploit a discovery in one field lie, partly or wholly, in an entirely different one. A study from 2022 showed empirically how the ‘distance’ between biomedical findings – whether they were from similar subfields or ones that generally never cite each other – determines whether they tend to be combined to form new knowledge.

‘Biomedical scientists’, as the paper’s author, data scientist Raul Rodriguez-Esteban, put it, ‘appear to have a wide set of facts available, from which they only end up publishing discoveries about a small subset’. Perhaps understandably, they tend to ‘reach more often for facts that are closer’. Encouraging interdisciplinary collaboration, and encouraging scientists to keep an open mind about who they might work with, could help extend that reach.

That, of course, is easier said than done. Perhaps the most modern tools we have available – namely, powerful AI systems – could help us. It is possible to train an AI to escape the disciplinary lines of universities, instead generating ‘alien’, yet scientifically plausible, hypotheses from across the entire scientific literature.

These might be based, for example, on the identification of unstudied pairs of scientific concepts, unlikely to be imagined by human scientists in the near future. It’s already been shown in research on natural language processing that a purely textual analysis of published studies could potentially glean gene-disease associations or drug targets years before a human, or a human-led analysis, would discover them.

3. An Interview with Intel CEO Pat Gelsinger About Intel’s Progress Towards Process Leadership – Ben Thompson and Pat Gelsinger

Well, tell me more about that. Why is advanced packaging the future? I know this has been a big focus for Intel, it’s something you want to talk about, and from everything I know you have good reason to want talk about it, your technology is leading the way. Why is that so important in addition to the traditional Moore’s Law shrinking transistors, et cetera? Why do we need to start stacking these chiplets?

PG: Well, there’s about ten good reasons here, Ben.

Give me the top ones.

PG: I’ll give you the top few. One is, obviously, one of your last pieces talked about the economics of Moore’s Law on the leading edge node, well now you’re able to take the performance-sensitive transistors and move them to the leading edge node, but leverage some other technologies for other things, power delivery, graphics, IP sensitive, I/O sensitive, so you get to mix-and-match technologies more effectively this way.

Second, we can actually compose the chiplets to more appropriate die sizes to maximize defect density as well, and particularly we get to some of the bigger server chips, if you have a monster server die, well, you’re going to be dictated to be n-2, n-3, just because of the monster server die size. Now I get to carve up that server chip and leverage it more effectively on a 3D construct. So I get to move the advanced nodes for computing more rapidly and not be subject to some of the issues, defect density, early in the life of a new process technology. Additionally, we’re starting to run into some different scaling aspects of Moore’s Law as well.

Right.

PG: SRAMs in particular, SRAM scaling will become a bigger and bigger issue going forward. So I actually don’t get benefit by moving a lot of my cache to the next generation node like I do for logic, power, and performance. I actually want to have a 3D construct where I have lots of cache in a base die, and put the advanced computing on top of it into a 3D sandwich, and now you get the best of a cache architecture and the best of the next generation of Moore’s law so it actually creates a much more effective architectural model in the future. Additionally, generally, you’re struggling with the power performance and speed of light between chips.

Right. So how do you solve that with the chiplet when they’re no longer on the same die?

PG: Well, all of a sudden, in the chiplet construct, we’re going to be able to put tens of thousands of bond connections between different chiplets inside of an advanced package. So you’re going to be able to have very high bandwidth, low latency, low power consumption interfaces between chiplets. Racks become systems, systems become chips in this architecture, so it becomes actually a very natural scaling element as we look forward, and it also becomes very economic for design cycles. Hey, I can design a chiplet with this I/O…

…The yield advantages of doing smaller dedicated chiplets instead of these huge chips is super obvious, but are there increased yield challenges from putting in all these tens of thousands of bonds between the chips, or is it just a much simpler manufacturing problem that makes up for whatever challenges there might be otherwise?

PG: Clearly, there are challenges here, and that’s another area that Intel actually has some quite unique advantages. One of these, we can do singulated die testing. Literally, we can carve up and do testing at the individual chiplet level before you actually get to a package, so you’re able to take very high yielding chiplets into the rest of the manufacturing process. If you couldn’t do that, now you’re subject to the order of effects of being able to have defects across individual dies, so you need to be able to have very high yielding individual chiplets, you need to be able to test those at temperature as well, so you really can produce a good, and then you need a high yielding manufacturing process as you bring them into an advanced substrate.

Why is Intel differentiated in this? What is your advantage that you think is sustainable? You’ve talked about it already being the initial driver of your Foundry Service.

PG: Yeah, it’s a variety of things. We’ve been building big dies and servers for quite a while, so we have a lot of big die expertise. Our advanced packaging with Foveros and EMIB (Embedded Multi-Die Interconnect Bridge) and now hybrid bonding and direct copper-to-copper interface, we see ourself as years ahead of other technology providers in the industry. Also, as an integrated IDM, we were developing many of these testing techniques already. So we have our unique testers that we have that allow us to do many of these things in high yield production today at scale, and we’re now building factories that allow us to do wafer level assembly, multi-die environment.

So it really brings together many of the things that Intel is doing as an IDM, now bringing it together in a heterogeneous environment where we’re taking TSMC dies. We’re going to be using other foundries in the industry, we’re standardizing that with UCIe. So I really see ourself as the front end of this multi-chip chiplet world doing so in the Intel way, standardizing it for the industry’s participation with UCIE, and then just winning a better technology…

…And then RibbonFET and number one, explain them to me and to my readers. And number two, why are they so important? And number three, which one is more important? Are they inextricably linked?

PG: Yeah, PowerVia, let’s start with the easier one first. Basically, when you look at a metal stack up and a modern process, leading edge technology might have fifteen to twenty metal layers. Metal one, metal two…

And the transistors all the way at the bottom.

PG: Right, and transistors down here. So it’s just an incredible skyscraper design. Well, the top level of metals is almost entirely used for power delivery, so now you have to take signals and weave them up through this lattice. And then you want big fat metals and why do you want them fat? So you get good RC characteristics, you don’t get inductance, you’re able to have low IR drop right across these big dies.

But then you get lots of interference.

PG: Yeah and then they’re screwing up your metal routing that you want for all of your signals. So the idea of taking them from the top and using wafer-level assembly and moving them to the bottom is magic, right? It really is one of those things, where the first time I saw this laid out, as a former chip designer, I was like, “Hallelujah!”, because now you’re not struggling with a lot of the overall topology, die planning considerations, and you’re going to get better metal characterization because now I can make them really fat, really big and right where I want them at the transistor, so this is really pretty powerful. And as we’ve done a lot of layout designs now we get both layout efficiency because all of my signal routing get better, I’m able to make my power delivery and clock networks far more effectively this way, and get better IR characteristics. So less voltage drops, less guard banding requirements, so it ends up being performance and area and design efficiency because the EDA tools —

Right. It just becomes much simpler.

PG: Everybody loves this. That’s PowerVia, and this is really an Intel innovation. The industry looked at this and said, “Wow, these guys are years ahead of anything else”, and now everybody else is racing to catch up, and so this is one where I say, “Man, we are ahead by years over the industry for backside power or PowerVia, and everybody’s racing to get their version up and running, and we’re already well underway and in our second and third generation of innovation here”.

On the transistor, that’s the gate-all-around (GAA) or we call it RibbonFET, our particular formulation for that. Samsung and TSMC have their variation of that, so I’ll say on PowerVia, well ahead, while everybody’s working on GAA and you can say, “Why is Intel better?”, well hey, when you’ve done every major transistor innovation for the last twenty-five years…

…Just to step back and look back over your two to three years, in our previous interview we talked about the importance of the Foundry business being separate from the product business. This is something that I was very anchored on looking at your announcement, and it’s why I was excited about Meteor Lake for example, because to me that was a forcing function for Intel to separate the manufacturing and design parts. At the same time, you are not actually unveiling a separate P&L for it until early next year. What took so long? Was that the area where maybe you actually were moving too slowly?

PG: Well, when you say something like separate the P&L, it’s sort of like Intel hasn’t done this in almost our 60-year history. The idea that we’re going to run fully separate operations with fully separate financials, with fully separate allocation systems at ERP and financial levels, I joke internally, Ben, that the ERP and finance systems of Intel were old when I left, that was thirteen years ago and we are rebuilding all of the corporate systems that we sedimented into IDM 1.0 over a five-decade period.

Tearing all of that apart into truly separate operational disciplines as a fabless company and as a foundry company, that’s a lot of work. Do I wish it could have gone faster? Of course I do, but I wasn’t naive to say, “Wow, I can go make this happen really fast.” It was five decades of operational processes, and internally we’ll be by the time we publish the financials in Q1 of next year, we’ll have gone through multiple quarters of trial running those internally, and now that’ll be the first time that we present it to the Street that way.

As we talk to Foundry customers we’re saying, “Come on in, let’s show you what we’re doing, test us.” And MediaTek, one of our early Foundry customers, “Hey, give us the feedback, give me the scorecard. How am I doing? What else do you need to see?”, start giving us the NPS scores for us as a Foundry customer, there’s a lot of work here. Yeah, I wish it would go faster but no, I’m not disappointed that it’s taken this long…

…I mean, you’ve pushed vigorously, I would say, for the CHIPS Act and there was actually just a story in the Wall Street Journal, I saw it as I was driving in, that said Intel is the leading candidate for money for a national defense focused foundry, a secure enclave I think they called it, potentially in Arizona. But you mentioned the money aspect of being a foundry, and you have to be the first customer, but you’re the first customer with an also-threatened business that has— you talked about your earnings, you’re not filling your fabs currently as it is, and you don’t have trailing edge fabs spinning off cash to do this, you don’t have a customer base. Is this a situation where, “Look, if the US wants process leadership, we admit we screwed up, but we need help”?

PG: There are two things here. One is, hey, yeah, we realize that our business and our balance sheet, cash flows are not where they need to be. At the same time, there’s a fundamental economic disadvantage to build in US or Europe and the ecosystem that has emerged here (Taiwan), it’s lower cost.

Right. Which TSMC could tell you.

PG: Right. And hey, you look at some of the press that’s come out around their choice of building in the US, there’s grave concerns on their part of some of those cost gaps. The CHIPS Act is designed to close those cost gaps and I’m not asking for handouts by any means, but I’m saying for me to economically build major manufacturing in US and Europe, those cost gaps must be closed, because if I’m going to plunk down $30 billion for a major new manufacturing facility and out of the gate, I’m at a 30%, 40% cost disadvantage —

Even without the customer acquisition challenges or whatever it might be.

PG: At that point, no shareholders should look at me and say, “Please build more in the US or Europe.” They should say, “Well, move to Asia where the ecosystem is more mature and it’s more cost-effective to build.” That’s what the CHIPS Act was about: if we want balanced, resilient supply chains, we must close that economic gap so that we can build in the US and Europe as we have been. And trust me, I am fixing our issues but otherwise, I should go build in Asia as well, and I don’t think that’s the right thing for the world. We need balanced supply chains that are resilient for the Americas and for Europe and in Asia to have this most important resource delivered through supply chains around the world. That’s what the CHIPS Act was about.

I am concerned though. My big concern, just to put my cards on the table, is the trailing edge, where it’s basically Taiwan and China, and obviously China has its own issues, but if Taiwan were taken off the map, suddenly, part of what motivated the CHIPS Act was we couldn’t get chips for cars. Those are not 18A chips, maybe those will go into self-driving cars, I don’t want to muddy the waters, but that’s an issue where there’s no economic case to build a trailing edge fab today. Isn’t that a better use of government resources?

PG: Well, I disagree with that being a better use of resource, but I also don’t think it’s a singular use of resource on leading edge. And let me tease that apart a little bit. The first thing would be how many 28 nanometer fabs should I be building new today?

Economically, zero.

PG: Right, yeah, and I should be building zero economically in Asia as well.

Right. But China is going to because at least they can.

PG: Exactly. The economics are being contorted by export policy, not because it’s a good economic investment as well.

Right. And that’s my big concern about this policy, which is if China actually approaches this problem rationally, they should flood the market like the Japanese did in memory 40 years ago.

PG: For older nodes.

For older nodes, that’s right.

PG: Yeah because that’s what they’re able to go do and that does concern me as well. At the same time, as we go forward, how many people are going to be designing major new designs on 28 nanometers? Well, no. They’re going to be looking at 12 nanometers and then they’re going to be looking at 7 nanometers and eventually they will be moving their designs forward, and since it takes seven years for one of these new facilities to both be built, come online, become fully operational in that scale, let’s not shoot behind the duck.

And so your sense is that you are going to keep all these 12, 14 nanometer fabs online, they’re going to be fully depreciated. Even if there was a time period where it felt like 20 nanometer was a tipping point as far as economics, a fully depreciated 14 nanometer fab—

PG: And I’m going to be capturing more of that because even our fab network, I have a whole lot of 10 nanometer capacity. I’m going to fill that with something, I promise you, and it’s going to be deals like we just did with Tower. We’re going to do other things to fill in those as well because the depreciated assets will be filled. I’m going to run those factories forever from my perspective, and I’ll find good technologies to fill them in.

Let’s talk about AI. I know we’re running short on time, but there’s the question. I feel like AI is a great thing for Intel, despite the fact everyone is thinking about it being GPU-centric. On one hand, Nvidia is supply constrained and so you’re getting wins. I mean, you said Gaudi is supply constrained, which is not necessarily as fast as an Nvidia chip, I think, is safe to say. But I think the bull case, and you articulated this in your earnings call, is AI moving to the edge. Tell me this case and why it’s a good thing for Intel.

PG: Well, first I do think AI moves to the edge and there are two reasons for that. One is how many people build weather models? How many people use weather models? That’s training versus inference, the game will be in inference. How do we use AI models over time? And that’ll be the case in the cloud, that’ll be the case in the data center, but we see the AI uses versus the AI training becoming the dominant workload as we go into next year and beyond. The excitement of building your own model versus, “Okay, now we build it. Now what do we do with it?”

And why does Intel win that as opposed to GPUs?

PG: For that then you say, in the data center, you say, “Hey, we’re going to add AI capabilities.” And now gen four, Sapphire Rapids is a really pretty good inferencing machine, you just saw that announced by Naver in Korea. The economics there, I don’t now have to port my application, you get good AI performance on the portion of the workload where you’re inferencing, but you have all the benefits of the software ecosystem for the whole application.

But importantly, I think edge and client AI is governed by the three laws. The laws of economics: it is cheaper to do it on the client versus in the cloud. The laws of physics: it is faster to do it on the client versus round tripping your data to the cloud. And the third is the laws of the land: do I have data privacy? So for those three reasons, I believe there’s all going to be this push to inferencing to the edge and to the client and that’s where I think the action comes. That’s why Meteor Lake and the AIPC is something— 

4. Slicing and Dicing: How Apollo is Creating a Deconstructed Bank – Marc Rubinstein

Securitisation as a technology changed finance. By allowing loans to be repackaged for resale, it paved the way for the disintegration of the traditional value chain that cleaved loan origination to funding sources.

The basic form of an asset-backed security goes back a long time, but the modern-day version was born 40 years ago when First Boston, Salomon Brothers and Freddie Mac divided up residential mortgage pools into different tranches that allowed bondholders to be paid at different rates. Investors could choose between buying more expensive, higher rated bonds backed by tranches with first claim on payment flows, or purchasing subordinated bonds that were less expensive, lower rated and riskier. This technique helped the mortgage-backed securities market grow from $30 billion in 1982 to $265 billion in 1986.

The market soon spread, moving beyond mortgages in the 1980s to include student loans, auto loans and credit card receivables. Eventually, issuers securitised more exotic revenue streams, creating, for example, Bowie bonds securitised by revenues from David Bowie’s back catalogue and even Bond bonds securitised by revenues from James Bond movies. Market growth was aided by a friendly regulatory environment, improvements in computing power and new information technologies. With increasing precision, the risks and revenues associated with debts could be identified, catalogued, isolated and sold.

The securitisation process involves a chain of participants. In a stylised version, a borrower sits at one end and takes out a loan from an originator. The originator then sells the loan into a special purpose entity which issues bonds against it, with the help of an underwriter. To provide originators with liquidity, banks offer warehouse facilities which act as a kind of institutional credit card, allowing them to finance pre-agreed eligible assets. The underwriter manages the sale of bonds to investors. To ensure payment flows continue uninterrupted, a servicer sits underneath the process, collecting cash from the borrower and passing it through to the investor…

…In its 13 years of experience investing in asset-backed securities, Apollo has deployed over $200 billion of capital. Its annualised loss rate: just 1.3 basis points. 

But Apollo reckons that it could deploy more if only it had access to more origination. “There is no shortage of capital,” said CEO Marc Rowan at an investor day two years ago. “What there is, is a shortage of assets.”

Hence, the firm has reversed back down the value chain into direct origination. And because it’s not necessarily able to invest in everything its origination platforms throw off, it has built up a capital solutions group as well, to distribute asset-backed loans to other market participants. Apollo also recently lifted a business from Credit Suisse which it has renamed Atlas SP that offers warehouse facilities, securitisation and syndication to other originators. So, like a deconstructed bank, it now operates right across the value chain in a fairly unique way.

Apollo currently operates 16 different origination engines. They operate as stand-alone companies focused on their particular niche, independently capitalised and with their own management and board of directors. In total, the firm has invested around $8 billion of equity capital into these businesses; they collectively manage $130 billion of assets and employ 3,900 staff. The companies are at different stages of maturity: Seven manage less than $2 billion of assets, including two that Apollo launched de-novo; six manage between $2 billion and $10 billion of assets; and three manage in excess of $20 billion…

…The problem with operating a range of origination platforms is that their track record – at least as public businesses – is not very good. Origination businesses need to manage two risks: liquidity risk and credit risk.

Historically, liquidity risk has brought many down. Their reliance on market funding sources entwines their fortunes with market sentiment, and markets can be skittish. Following the Russian debt crisis in 1998, market disruption led to a steep fall in demand among investors for risky assets, including subprime securitizations, even before a recession took hold three years later. Subprime originators saw their own borrowing costs skyrocket. In the two years following the crisis, eight of the top 10 subprime lenders declared bankruptcy, ceased operations or sold out to stronger firms…

…Apollo argues that its long-term insurance liabilities are a better match for asset financing than commercial paper, money markets or even a bank’s deposits. The firm may have a point. Deposit outflows at Silicon Valley Bank, Signature Bank and First Republic highlight that bank funding isn’t what it was and that its realised duration may be lower than anticipated.

The second risk is credit risk. Apollo reckons its diversification helps – across originators and across asset types. Its platforms operate over 30 product lines and each deploys a large funnel. Since being founded in 2008, MidCap has closed only 2,000 deals out of around 29,000 identified opportunities, on which it issued 6,800 term sheets. Overall, the group’s platforms target a conversion of between 5% and 10% of opportunities. Such a large funnel avoids adverse selection.

5. Everything You Can’t Predict – Jack Raines

You would be hard-pressed to find a technological development from the last 20 years that is more important than “the cloud.”…

…Interestingly, the first company to launch an enterprise cloud solution wasn’t Amazon, Microsoft, or Google.

It was IBM.

Yes, IBM, whose stock price appreciated by a whopping 2.39% between August 2000 and April 2023, was the first entrant to the cloud space.

So, what went wrong?

IBM was the face of the computer industry for most of the 20th century, and in July 2002, they unveiled a service called Linux Virtual Services, which offered customers a radical new payment structure.

Historically, IBM had locked customers into long-term, fixed-price contracts in exchange for access to different hardware and software products. In contrast, Linux Virtual Services would allow customers to run their own software applications through IBM mainframes and pay based on their usage.

According to IBM, this usage-based payment model would result in savings of 20% – 55%.

Linux Virtual Services should have kickstarted a proliferation of cloud-based services, but instead, it was shut down a few years later…

…In 2002, IBM, a $130B computing giant with unlimited resources and a multi-decade head start, launched an enterprise cloud offering aimed at commoditizing computing power.

In 2002, Amazon, a $10B e-commerce store, was solving an internal engineering bottleneck.

In 2006, IBM shut down its cloud storage service.

In 2006, Amazon launched its cloud storage service.

In 2023, IBM is still worth $130B.

In 2023, Amazon is worth 10 IBMs, largely due to the success of AWS.

What went wrong at IBM? No one really knows, but Corry Wang, a former equity researcher at Bernstein, speculates that IBM’s sales team may have had misaligned incentives. In 2002, sales teams would have earned larger commissions on higher-priced, fixed contracts than on cheaper, usage-based contracts, and the new offerings would have cannibalized current customers as well. Why, as a salesperson, would you sell a service that made you less money?

Meanwhile, Amazon realized, almost by accident, that their internal solutions to infrastructure bottlenecks could be exported and sold as services. And Amazon didn’t have current SaaS customers to worry about cannibalizing, so their salespeople were free to sell the service to anyone.

16 years later, Amazon is the market leader in cloud, and IBM is stuck in 2006…

…Because it shows that predicting the future is easy, but predicting who wins in that future is much, much more difficult. By 2001, plenty of tech experts could have told you that cloud computing was going to emerge as an important technological development in a decade.

But how many of those experts would have predicted that an online bookstore would dominate the cloud market?

Picking trends is easy. Picking winners is hard.

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 have a vested interest in Alphabet (parent of Google), Amazon, Microsoft, Netflix, and TSMC. 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 November 2023:

1. Strange Ways AI Disrupts Business Models, What’s Next For Creativity & Marketing, Some Provocative Data – Scott Belsky

Increasing perversion of certain business models that are liable to be gamed or constrained by AI: We’re shifting from a world where data analysis required long cycles (analysts need lots of time to run queries, analyze, and then present findings in a way that people understand) to a new world of real-time optimization and insights (AI will mine the data to surface insights and make optimization decisions in real-time). But when businesses start optimizing themselves, all sorts of crazy things might start happening (or at least be suggested by the AI). What wild examples can we think of here? For dating apps, where the perfect match of two people increases churn, will Tinder or Bumble constrain the efficiency of AI so the product doesn’t become too “unsustainably effective”? Or in the world of music streaming: Since Spotify pays artists per song, will Spotify automatically optimize its algorithms to favor longer songs, taking into account the number of minutes each customer listens per day? As AI gets really good at optimization, some industries and business models will need to change…

…AI will threaten subjectivity in purchase decisions, and with it the sway of brand and marketing. As we gain trust in the guidance of agent-assisted experiences, will the impact of brand, referral, and relationships in purchase decisions be diminished? Whether we’re buying batteries, sneakers, potato chips, or kitchen appliances, we are often influenced more than we care to admit by brand perceptions as opposed to factual comparisons. However, as your “AI Agent” gets to know you better – infused by every personal preference and previous purchase as well as every online review and consumer reports determination – you may start trusting the guidance of your agent more than any other signal. Perhaps the stakes are even more pronounced in the enterprise, where a procurement process tainted by human emotions, laziness, and previous relationships is the persistent fear of any CFO. How many purchase decisions are made for the wrong reasons – like relationships strengthened by football games and steak dinners with salespeople as opposed to the value and quality of a solution? Companies like Globality (in my portfolio, tackling enterprise procurement) and many others are leveraging AI to radically transform every function of a company. And if you look at this wave of companies overall, they are tackling the tremendous costs of subjectivity in decision making and are designed to yield better and more cost-effective solutions. Ultimately, elevating product meritocracy solves problems in both the worlds of consumer and enterprise purchasing. AI threatens subjective decision making tainted by human error and bias and will usher in an era where the best product at the best value may in fact win. This is a win for buyers, but may be quite disruptive to sellers who fail to innovate and endlessly optimize.

2. 15-Year Anniversary of the Business Owner Fund – Robert Vinall

On Monday the Biden administration released an Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence. This Executive Order goes far beyond setting up a commission or study about AI, a field that is obviously still under rapid development; instead it goes straight to proscription…

If the wrong mental model or a lack of intellectual acumen is not why people fail to beat the market, what is? In my view, it is the sheer difficulty of remaining rational – i.e., buying businesses for less than they are worth and selling them for more than they are worth – when being constantly bombarded with market gyrations, news flow, social media, expert opinions, and any number of other influences. This is not something that I or, likely, anyone is immune to. Over the last five years, I likely became too optimistic in some of my assumptions as the bull market approached its peak in 2021.

That the emotions interfere with rational thought is not an original idea. The definitive book on psychological biases has already been written – “Thinking, Fast and Slow,” by Israeli-American Nobel Prize winner Daniel Kahneman…

…In practice, there are two main failings resulting from emotional biases that an investor needs to eliminate – turning overly pessimistic when markets or investments are down and turning overly optimistic when they are up. Perhaps it could even be argued that turning overly pessimistic when markets are down is the key risk to be cognizant of. Assuming markets trend upwards over time – albeit in a lumpy fashion – the single most important attribute in an investor is the ability to remain steadfast when the outlook temporarily looks bleak. However, given how damaging to wealth it can be to be caught up in a bubble, I will leave the list at two…

…I cannot overstate the importance of being independent. It is far easier to reach a rational conclusion about any topic if your thought process is unclouded by the opinions of others.

In the 1950s, psychologist Solomon Asch conducted a series of psychological experiments known as the Asch conformity experiments. A group of eight participants engaged in a simple perceptual task, whereby all but one of the participants were actors. Each participant was shown a card with one line on it followed by another card with three lines of differing lengths on it… The task was to state which line was of similar length, a simple task that everyone got right when left to their own devices. The catch is that in some trials, the actors gave the wrong response. When this happened, the study’s subject was far more likely to give the wrong response as well. The research suggests people are more likely to conform than they might expect.

Furthermore, the study found that people are more likely to conform when a) more people are present; b) the task is more difficult; and c) the other members of the group are of higher social status. The study found they are less likely to conform when able to respond privately.

This suggests to me four ways to increase your chances of thinking independently: avoid large groups, stick to simple investment opportunities, avoid experts and gurus, and make investment decisions in private, i.e., not in a committee. It is fascinating to me that three of these four measures suggest working alone is better than in a team, and the fourth (keeping it simple) is independent of team size. All the evidence suggests that the smaller the team size, the better the decision-making…

…Given that a good investment idea should be obvious, but the analysis before investing needs to be detailed, it stands to reason that the optimal approach is to analyse many ideas superficially and a handful (the ones considered for investment) in depth. In other words, it is necessary to kiss lots of frogs until you find a prince.

The investment blog, value and opportunity, occasionally takes an entire stock market and analyses every company in it. I am a big fan of the idea. The blogger’s reasons for rejecting an investment sometimes amount to just a single sentence, so these entries will not win any prizes for their intricacy. However, to the end of creating as many free options as possible, it is great…

…If the name of the game is to keep an even keel despite what is happening around you, I cannot for the life of me imagine how having a prominent social media presence can improve investment returns. Social media tends to amplify whatever emotions are prevalent at the time. When returns are good, you are likely to be celebrated as the next Warren Buffett. When they are bad, you will be pummelled mercilessly. An ideal environment is the exact opposite – one where you receive gentle encouragement when things are going badly and a reality check when things are going well. I realise that building a prominent social media presence is an effective way to raise capital fast. But if you live by the sword, expect to die by the sword…

…The single best way I have found to evaluate whether something is important or not is to consider whether I will care about it or even remember it in 10 years’ time. Changes in interest rates, disappointing quarters and even recessions are things that people will likely not care about in ten years’ time. A customer exodus, the breach of debt covenants, and a disruptive new entrant potentially are. Looking through the lens of how things will appear in the future helps to separate signal from the noise. Note though, it is only possible to practice long-term thinking if you have capital providers who take a similarly long-term perspective.

The final point is the importance of humility. Markets occasionally do unexpected things and the best investment strategies ultimately fail as competitors imitate them. Anyone who thinks they have everything figured out is riding for a fall. It is important to be humble.

You have read on two occasions in this memo that it is not clear to me whether I will beat the market in the long-term. This is not false humility. I believe it – not just because of the sobering experience of the last five years, but for the simple reason that so few investors do beat the market over the long-term. Why should I be the chosen one? I hope this is not too disconcerting to my investors. It should not be. Even Buffett is circumspect about Berkshire’s ability to continue to beat the market given its enormous scale, though he would no doubt fancy his chances with a smaller capital base. Anyone who is certain they are going to beat the market belongs in the marketing department, not the investment department.

3. Attenuating Innovation (AI) – Ben Thompson

On Monday the Biden administration released an Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence. This Executive Order goes far beyond setting up a commission or study about AI, a field that is obviously still under rapid development; instead it goes straight to proscription…

…To rewind just a bit, last January I wrote AI and the Big Five, which posited that the initial wave of generative AI would largely benefit the dominant tech companies. Apple’s strategy was unclear, but it controlled the devices via which AI would be accessed, and had the potential to benefit even more if AI could be run locally. Amazon had AWS, which held much of the data over which companies might wish to apply AI, but also lacked its own foundational models. Google likely had the greatest capabilities, but also the greatest business model challenges. Meta controlled the apps through which consumers might be most likely to encounter AI generated content. Microsoft, meanwhile, thanks to its partnership with OpenAI, was the best placed to ride the initial wave generated by ChatGPT.

Nine months later and the Article holds up well: Apple is releasing ever more powerful devices, but still lacks a clear strategy; Amazon spent its last earnings call trying to convince investors that AI applications would come to their data, and talking up its partnership with Anthropic, OpenAI’s biggest competitor; Google has demonstrated great technology but has been slow to ship; Meta is pushing ahead with generative AI in its apps; and Microsoft is actually registering meaningful financial impact from its OpenAI partnership.

With this as context, it’s interesting to consider who signed that letter Allen referred to, which stated:

Mitigating the risk of extinction from AI should be a global priority alongside other societal-scale risks such as pandemics and nuclear war.

There are 30 signatories from OpenAI, including the aforementioned CEO Sam Altman. There are 15 signatories from Anthropic, including CEO Dario Amodei. There are seven signatories from Microsoft, including CTO Kevin Scott. There are 81 signatories from Google, including Google DeepMind CEO Demis Hassabis. There are none from Apple or Amazon, and two low-level employees from Meta.

What is striking about this tally is the extent to which the totals and prominence align to the relative companies’ current position in the market. OpenAI has the lead, at least in terms of consumer and developer mindshare, and the company is deriving real revenue from ChatGPT; Anthropic is second, and has signed deals with both Google and Amazon. Google has great products and an internal paralysis around shipping them for business model reasons; urging caution is very much in their interest. Microsoft is in the middle: it is making money from AI, but it doesn’t control its own models; Apple and Amazon are both waiting for the market to come to them.

In this ultra-cynical analysis the biggest surprise is probably Meta: the company has its own models, but no one of prominence has signed. These models, though, have been gradually open-sourced: Meta is betting on distributed innovation to generate value that will best be captured via the consumer touchpoints the the company controls.

The point is this: if you accept the premise that regulation locks in incumbents, then it sure is notable that the early AI winners seem the most invested in generating alarm in Washington, D.C. about AI. This despite the fact that their concern is apparently not sufficiently high to, you know, stop their work. No, they are the responsible ones, the ones who care enough to call for regulation; all the better if concerns about imagined harms kneecap inevitable competitors…

…I wrote at the time in an Update:

In 1991 — assuming that the “dawn of the Internet” was the launch of the World Wide Web — the following were the biggest companies by market cap:

$88 billion — General Electric
$80 billion — Exxon Mobil
$62 billion — Walmart
$54 billion — Coca-Cola
$42 billion — Merck

The only tech company in the top 10 was IBM, with a $31 billion market cap. Imagine proposing a bill then targeting companies with greater than $550 billion market caps, knowing that it is nothing but tech companies!

What doesn’t occur to Senator Klobuchar is the possibility that the relationship between the massive increase in wealth, and even greater gain in consumer welfare, produced by tech companies since the “dawn of the Internet” may in fact be related to the fact that there hasn’t been any major regulation (the most important piece of regulation, Section 230, protected the Internet from lawsuits; this legislation invites them). I’m not saying that the lack of regulation is causal, but I am exceptionally skeptical that we would have had more growth with more regulation.

More broadly, tech sure seems like the only area where innovation and building is happening anywhere in the West. This isn’t to deny that the big tech companies aren’t sometimes bad actors, and that platforms in particular do, at least in theory, need regulation. But given the sclerosis present everywhere but tech it sure seems like it would be prudent to be exceptionally skeptical about the prospect of new regulation; I definitely wouldn’t be celebrating it as if it were some sort of overdue accomplishment.

Unfortunately this week’s Executive Order takes the exact opposite approach to AI that we took to technology previously…

…I fully endorse Sinofsky’s conclusion:

This approach to regulation is not about innovation despite all the verbiage proclaiming it to be. This Order is about stifling innovation and turning the next platform over to incumbents in the US and far more likely new companies in other countries that did not see it as a priority to halt innovation before it even happens.

I am by no means certain if AI is the next technology platform the likes of which will make the smartphone revolution that has literally benefitted every human on earth look small. I don’t know sitting here today if the AI products just in market less than a year are the next biggest thing ever. They may turn out to be a way stop on the trajectory of innovation. They may turn out to be ingredients that everyone incorporates into existing products. There are so many things that we do not yet know.

What we do know is that we are at the very earliest stages. We simply have no in-market products, and that means no in-market problems, upon which to base such concerns of fear and need to “govern” regulation. Alarmists or “existentialists” say they have enough evidence. If that’s the case then then so be it, but then the only way to truly make that case is to embark on the legislative process and use democracy to validate those concerns. I just know that we have plenty of past evidence that every technology has come with its alarmists and concerns and somehow optimism prevailed. Why should the pessimists prevail now?

They should not. We should accelerate innovation, not attenuate it. Innovation — technology, broadly speaking — is the only way to grow the pie, and to solve the problems we face that actually exist in any sort of knowable way, from climate change to China, from pandemics to poverty, and from diseases to demographics. To attack the solution is denialism at best, outright sabotage at worst. Indeed, the shoggoth to fear is our societal sclerosis seeking to drag the most exciting new technology in years into an innovation anti-pattern. 

4. Bad Office – Marc Rubinstein

On the very same avenue, there have been multiple examples of borrowers handing back keys to lenders on office properties:

• Between 55th and 56th Streets, Blackstone gave up on a $308 million loan on 26-storey 1740 Broadway after tenants L Brands and law firm Davis & Gilbert relocated. 
• Between 49th and 50th, Brookfield surrendered the deeds to the 11-storey Brill Building at 1619 Broadway in a transfer valued at $216 million, six years after buying it.
• At Times Square, CIM Group and Australian pension fund QSuper handed back the keys on 1440 Broadway after being unable to pay a $399 million loan. They had bought the building in 2017 and had it valued at $540 million when they took the loan out two years ago.

Such stress in the office market stems from a combination of lower occupancy and higher interest rates. Over three years on from the start of the pandemic, occupancy in major US office markets remains depressed. Latest turnstile swipe data from Kastle indicates that office occupancy stands at 49.8% of pre-pandemic levels, a figure which has remained stable for eighteen months. And data from XY Sense, which uses sensor data to measure physical office presence, points to office utilisation of around 30% compared with around 60% pre-pandemic, consistent with a roughly 50% drop in occupancy. Whatever incentives companies deploy to get employees back into the office, they’re not working.

For a team of New York-based academics, this doesn’t bode well. In a recent paper, they established a clear connection between companies’ remote work policies and their actual reductions in leased office space. Because only a third of pre-pandemic office leases have come up for renewal, the impact has yet to fully flow through even with vacancy rates at 30-year highs (22.1% in Manhattan). They estimate that on the basis of current working behaviours, New York office stock is worth 42% less than it was in December 2019. At that level, even 60% loan-to-value financing deals are at risk.

In the meantime, higher interest rates are also squeezing borrowers. A lot of debt in the commercial real estate market is floating rate. When times were good, floating rate loans gave borrowers the flexibility to prepay early and sell assets. A typical structure is a five year loan, with the rate hedged for the first two years. With rates up, borrowers have to decide whether to buy new rate protection or alternatively, what to do with the asset. To finance its downtown Los Angeles portfolio, Brookfield took on a lot of floating-rate debt and when an interest-rate hedge expired on one of its properties last year, it opted not to get a new one, leading to it defaulting on a $319 million loan.

Not all of the office debt is with banks. For commercial real estate overall – encompassing hotels, retail, industrial and multifamily as well as office – banks sit on around 50% of debt outstanding. The rest is in commercial mortgage-backed securities structures (16%), government and government sponsored enterprise pools (13%), insurance companies (13%) and Real Estate Investment Trusts (5%).

In some of these segments, stress has been evident for some time. Office REIT stock prices  are down 60% since the end of 2019 and commercial mortgage-backed securities spreads have widened. For banks, though, the pressures have been slower to build.

This week, Bawag, another Europe-based commercial real estate lender, took a €20 million provision against a specific US office exposure as part of its third quarter earnings. The bank underwrote the loan in 2019 based on a rent roll that failed to materialise and has now written down the value of the collateral (to “an 8.5% cap rate, which I think is quite conservative, if you kind of benchmark that to where other people are valuing assets,” said the CEO on his earnings call). The write-down reflects a concern the European Central Bank recently expressed, that bank property valuers may be slow to update estimates. “Despite its importance, collateral valuation is a blind spot for many of the banks reviewed,” ECB supervisors warned last year.

Other banks are trying to get ahead of the curve. Among the largest US banks, PNC has the highest exposure to office (2.7% of total loans). Although it has barely seen any losses in its portfolio, it has begun to classify many of the loans as non-performing. In total, it views 23% of its office portfolio as “criticised” and in the third-quarter, it shifted $373 million of those onto non-performing status. “I think they’re actually all still accruing. We just kind of get there because we don’t think they’re refinanceable in the current market,” explained CEO Bill Demchak on his earnings call. “The move to non-performing from already being criticised comes about as you just watch cap rates creeping higher and adjust the underlying value of the properties accordingly.”

PNC has now set aside reserves to cover 8.5% of its total office loans and within that 12.5% of multi-tenant office loans. Other banks have provisioned at similar levels. Wells Fargo is at 10.8% in the large corporate office segment (although only at 2.2% for small offices); US Bancorp is at 10%.

For many, the crunch will come when borrowers look to refinance. Bank of America has disclosed that around a half of its office loans mature before the end of 2024. As long as they are still paying, many banks may be tempted to roll out the maturity rather than force a loss. Willy Walker, CEO of commercial real estate finance company Walker & Dunlop, calls it “extend and pretend” (emphasis added):

“What the regulator is allowing the banks to do is to take provisions for loan losses and then go and renegotiate those loans and extend and pretend. And given that the banking system is so overcapitalised right now, very much unlike the great financial crisis, a lot of this paper is just going to get rolled. Because the banks are sitting there going: I don’t want to foreclose on this; there’s no sale market for me to get rid of it. Do I want 60 cents on the dollar today or hope that maybe I get 80 or 90 cents on the dollar if I allow them to hold the asset, or I get worked out? The only place where this obviously causes some problems is in the CMBS world where you don’t have the flexibility because you have a special servicer who has a fiduciary responsibility to the bondholders to seize the asset.”

5. RWH034: The High Road To Riches w/ Peter Keefe – William Green and Peter Keefe

[00:17:43] Peter Keefe: And I think if you, opened up the minds of a lot of people in this business. You discover that their motivations may not be exactly what they think they are. And I think money is an incredibly powerful motivator and people may not be willing to admit just how powerful of a motivator it is. And I think it was Henry Kissinger who said money’s the ultimate aphrodisiac and it just can accomplish all kinds of things.

[00:18:11] Peter Keefe: And I think we all know that subconsciously. And so, and of course, am I interested in the rewards, the financial rewards of this business? Absolutely. I don’t know anybody in this business who isn’t, and I’d worry about you a little bit if you said that you weren’t, but having said that, This business is a calling and I think that when I’m talking to people about why they want to be in this business or when I’m mentoring younger investors, I do, this is sort of, it’s sort of an ominous statement says we’re all think that we’re in service to others.

[00:18:50] Peter Keefe: But sometimes you’re serving yourself. So, I sort of ask this question, it’s gently, you’re serving someone, but who are you serving? Make sure you understand who you’re serving. So, you know, we’re all in service to others, but. Make sure you understand who you’re serving.

[00:19:07] William Green: I wonder if it changes as we get older, because I often find when I interview great investors, it seems like early in their lives, there’s a sort of, I have no factual basis for this, it’s more impressionistic, but I have this sense that there’s a real hunger, often, for money, a kind of ill-defined hunger for money, whether it’s to get out of straightened circumstances, if you’re someone like Bill Miller or Mario Gabelli who grew up with nothing or desire to sort of impress people and get, you know, be noticed, you know, which I think, you know, if you were someone like Bill Ackman, who came from a very successful family, you know, you needed to make your mark. And then at a certain point, it shifts, maybe one, at least for a lot of people. I don’t know.

[00:19:54] William Green: And then also there’s a sense of just loving the game, right? I remember you, one thing that I heard you would ask the people you were interviewing for jobs was you would say to them, would you do this on a teacher’s salary for five years? And I think that’s a really important issue as well. Like, you know, actually having to enjoy the game enough the actual craft. Sorry, I’m going on. Well, do you have any thoughts?

[00:20:17] Peter Keefe: You’ve got to enjoy the game, but you’ve really got to appreciate the craft, and you do have to, the reason I asked that question, would you do this on a teacher’s salary it’s serious, but it’s also a trick question, because anybody who’s good at this is not going to have to live on a teacher’s salary for very long.

[00:20:34] Peter Keefe: I don’t want to be involved professionally with people who are doing this solely for the money. You are serving someone, and you should be serving those who need your skills. If you are good at this business, then you have an obligation to give those skills to those who need it. And they’re desperately needed.

[00:20:57] Peter Keefe: They’re desperately needed by hospitals, schools, retirees, poor people. Wealthy people who simply don’t care about investing, so the need is enormous, so I think it’s important to approach this business from a standpoint of service, and if you’re any good at it, you know, the money is going to rain down upon you more money than you ever imagined and more money than you’re ever going to need.

[00:21:23] Peter Keefe: So, you need to take the money out of the equation. Cause if you’re any good, you’re going to make a lot of it. If you’re not, you still might make a lot of it, but I think the principal motivation has to be to serve. Now, when you’re a young man, young woman, you know, we’ve all been there. You just want to go out and slay the world.

[00:21:41] Peter Keefe: I think that’s just part of the natural deal of you know, being young and moving to a new city like I did and wanting to do something. I mean, I tell people I had no idea what I wanted to do, but I wanted to do something, and I wanted to make an impact on people’s lives, a favorable impact on people’s lives.

[00:21:59] Peter Keefe: I’m not sure I even cared about making a favorable impact. I think I wanted to be noticed and I wanted my life to amount to something. You know, there was just a sort of ill-defined desire, this yearning to make some kind of mark. It was very ego filled, definitely. I think we’re saying exactly the same thing.

[00:22:18] Peter Keefe: I mean, I think that over time, my objectives evolved. Yeah. You know, on day one, you’ve got to pay the rent. You know, on day two, you know, you’re thinking about building a family. Day three, you’re thinking about the legacy. So, your way you approach your life evolves over time. And but yeah, I mean, I just, like I said, I got here in DC and I thought I was going to be a lawyer and then decided that was a really bad idea and I just wanted to do something, you know, I had a lot of energy and I was curious about everything, you know, which can be a problem because if you’re curious about everything, it’s hard to focus on one thing…

…[00:31:49] William Green: Yeah, I wanted to dwell for a moment on that idea of the biggest mistakes you’ve made, because you said recently that my biggest mistakes have been early sales of great businesses, and you described that as the silent killer, where you sell compounders too soon, and while we’re dwelling on your mistakes, can I cause you heart palpitations by asking you about Pool Corp, which is a pretty good example of this.

[00:32:14] Peter Keefe: Sure. Yeah. I think Pool Corporation’s the biggest mistake I’ve ever made in my investment career. And like you said, you know, selling early is the high blood pressure of the investment business. It’s a silent killer. And you know, people will always talk about the business they bought that went to zero, or the one that went down 50% or 75%.

[00:32:32] Peter Keefe: Yes, that’s bad. You want to avoid that but the business that you sold too early, that went on the compound tenfold, or 20-fold after that in my career, has been a real killer. I bought Pool Corp at the right price. Interesting story that I tell you about going down to visit management in Covington, Louisiana, which is not exactly a business mecca, but in any event, you know, it checked all our boxes.

[00:32:58] Peter Keefe: Manny Perez de la Mesa, who was still the chairman of the board, I think, was the CEO then, he’d been in GE, he had a great background, he was, understood capital allocation beautifully, and Pool Corp was a terrific business, and for those of your listeners who aren’t familiar with it, I’ll tell them in a breath.

[00:33:18] Peter Keefe: It’s a distributor of pool supplies and equipment, and it gets margins in the mid-single digits, most distributors, margins in the low single digits, but Pool Corp has this unique franchise that permits it to get these ridiculously high returns for a distributor. And those ridiculously high returns multiplied by frequent inventory turnover mean they get huge returns on capital and do everything that you want a compounder to do.

[00:33:50] Peter Keefe: Well, I bought it right and sold it after depreciating four or five times. In our portfolio, because I allowed some thinking about erroneous thinking about evaluation and probably about the economy didn’t creep into my thinking and full construction is somewhat like to the construction cycle. And so, I probably let all these things influence my thinking and went up selling the position in its entirety and having made, like I said, four or five times our capital on the business. Well, I think it’s appreciated about tenfold since then. So, what that taught me was that you either have to be an investor or an economist. Not many people can be both, and I don’t know any wealthy economists, so I’d rather be an investor.

[00:34:38] Peter Keefe: And I, so I just try to tune out some of this economic stuff that can infect your thinking unusually negatively.

[00:34:46] William Green: Yeah, I was reading your letters to shareholders yesterday, and there was one from, I guess, August 2020, so in the middle of the COVID pandemic, and you wrote, We have no predictions about the direction of the economy or markets, and certainly not the virus.

[00:35:01] William Green: The trajectory of the virus and its ultimate duration and impact on the economy are unknowable. What is knowable? is that on occasion the unthinkable happens, unforeseen acts of terrorism occur, real estate bubbles burst, or a pandemic emerges. This means we must own businesses with both bulletproof balance sheets and outstanding and durable business models that can withstand unthinkable economic hardship, which are run by ethical managers whom we can trust to act in our best interests.

[00:35:26] William Green: And that strikes me as such an important insight that you, I mean, in a way it gets back to stoicism, right? It’s like controlling what we can control and letting go of what we can’t control and just recognizing the fact that the direction of the economy and the market and viruses and stuff is just not really knowable unless maybe you’re Soros or Druckenmiller or someone like that, I don’t know. And so just that recognition, having the humility to recognize that’s not knowable strikes me as a really important first step-in long-term investment success.

[00:35:58] Peter Keefe: Yeah, so I, one of the things I tell young investors when I’m mentoring them is, make a choice, you’re either an economist or an investor, unless you’re one of those five people you mentioned.

[00:36:10] Peter Keefe: And I just rhetorically say there’s probably five people on the planet who can consistently tell you what the dollar is going to do, what the price of gypsum is going to be, what oil might be, or the price of money. I know none of those things, and I simply don’t have enough mental bandwidth to be able to allocate any room at all to these things.

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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 05 November 2023:

1. Lessons from Charlie Munger’s podcast interview – Thomas Chua

2. Why algo driven trading firms like Renaissance technology are taking excessive risk

“The easiest trade is to front run what you know, what the average is, what the index funds have to buy, and you know what it is. Exactly. They all know that. And the way they get their returns year after year is taking the leverage, the midday leverage, up higher and higher and higher and higher.

So they’re making smaller and smaller profits on more and more volume, which gives them this big peak leverage risk, which I would not run myself. And that’s the only way they make these big returns, is to have this huge leverage that would make you crazy if you were already rich.”

3. How Warren and Charlie changed their mind quickly with Diversified Retailing after they realized it was too competitive

(and how they made a ton of money after changing their mind)

Some context: On January 30, 1966, Buffett, Munger, and Gottesman formed a holding company, Diversified Retailing Company, Inc., to “acquire diversified businesses, especially in the retail field.”

Buffett and Munger then went to the Maryland National Bank and asked for a loan to make the purchase. The lending officer looked at them goggle-eyed and exclaimed, “Six million dollars for little old Hochschild-Kohn?”  Even after hearing this, Buffett and Munger—characteristically—did not question their own judgment and run screaming out the door.

“We thought we were buying a second-class department store at a third-class price” is how Buffett describes little old Hochschild-Kohn.

“We made nothing but money at Diversified. We didn’t exactly make it in retailing, but we made a lot of money.

What happened was very simple. We bought this little department store chain in Baltimore. Big mistake. Too competitive.

As the ink dried on the closing papers we realized we’d made a terrible mistake. So we decided just to reverse it and take the hits to look foolish rather than go broke. You just told us how to get us out of this. By that time we’d already financed half of it on covenant free debt and so forth. And they had all this extra cash and our own stocks got down to selling an enormous (discounts).

In the middle of one of those recessions, we just bought, bought and bought and bought and all that money went right into those stocks and of course we tripled it.”…

...11. Why Warren’s investment in Japan was a no-brainer

“If you’re as smart as Warren Buffett, maybe two, three times a century, you get an idea like that. The interest rates in Japan were half a percent per year for ten years. And these trading companies were really entrenched old companies, and they had all these cheap copper mines and rubber foundations, and so you could borrow for ten years ahead, all the money, and you could buy the stocks, and the stocks paid 5% dividends.

So there’s a huge flow of cash with no investment, no thought, no anything. How often do you do that? You’ll be lucky if you get one or two a century. We could do that [because of Berkshire credit]. Nobody else could.”…

…14. His view on China

“Well, my position in China has been that the Chinese economy has better future prospects over the next 20 years than almost any other big economy.

That’s number one. Number two, the leading companies of China are stronger and better than practically any other leading companies anywhere, and they’re available at a much cheaper price. So naturally I’m willing to have some China risk in the Munger portfolio.

How much China risk? Well, that’s not a scientific subject. But I don’t mind. Whatever it is, 18% or something.”…

…15. What about BYD that captivated Munger?

“Guy (Wang Chuanfu) was a genius. He was at a PhD in engineering and he could look at somebody part, he could make that part, look at the morning and look at it in the afternoon. He could make it. I’d never seen anybody like that. He could do anything. He is a natural engineer and get it done type production executive.

And that’s a big thing. It’s a big lot of talent to have in one place. It’s very useful. They’ve solved all these problems on these electric cars and the motors and the acceleration, braking, and so on.”

Comparing Elon with Wang Chuanfu

“Well, he’s a fanatic that knows how to actually make things with his hands, so he has to he’s closer to ground zero. In other words, the guy at BYD is better at actually making things than Elon is.”

2. The Crash Callers Won’t Save You – Ben Carlson

Here’s something Henry Blodget wrote about notorious stock market bear John Hussman:

Every historical indicator Hussman is looking at is suggesting that the stock market is wildly overvalued and headed for a period of lousy returns. How lousy? John Hussman thinks there’s a good chance the stock market will soon crash 40-50 percent.

And even if the market doesn’t crash, Hussman thinks stocks are priced to produce returns of only a couple of percentage points per year over the next decade–far below the 7 percent inflation-adjusted long-term return that everyone is used to and the double-digit returns of the last few years. If you want to feel comfortable and happy, go ahead and ridicule John Hussman with everyone else. If you want to prepare yourself for what seems like a likely possible stock-market future, however, read on.

Sounds scary, right?…

…Here’s the problem with Blodget and Hussman’s predictions — this piece was written in the summer of 2013!… In the 10 years following Hussman’s prediction of a 40-50% crash or lousy returns for a decade, the S&P 500 was up more than 230% in total or 12.8% on an annual basis:…

…Did Hussman relent from his crash-calling ways? No. He’s still out there calling for a crash, only this time it’s going to be even bigger!…

…When Hussman called for a 40-50% crash in August 2013, he said the Dow could fall somewhere in the 7,500-8,500 range. From current levels at around 32,800 the Dow would need to fall 55% just to get back to the point where Hussman made his initial prediction in 2013 and then another 50% from there to hit that target range…

…I looked at the daily returns on the S&P 500 going back to 1950 to see how often the market was in a state of drawdown at different levels of losses… We’ve had 40% and 50% crashes but it’s pretty rare. You don’t spend all that much time there as an investor. Sometimes you’re going to get your face ripped off in the markets and learn to live with it but you can’t shouldn’t expect it to happen all the time…

…I looked at the rolling 10 year returns for the S&P 500 going back to 1950 to find the distribution of annual returns at various levels… More than 3% of the time returns have been negative over 10 year time frames. Annual returns have been 5% or worse 14.1% of the time. That’s not great. However, annual returns have been 10% or higher 55% of the time. Annual returns of 8% or more have occurred in nearly 70% of all rolling 10 year windows since 1950…

…The people who predict a crash every single year will be “right” eventually. The same is true for those who are constantly forecasting a recession. But they will be wrong the majority of the time. The stock market has been up roughly 75% of the time over one year periods and nearly 97% of the time over 10 year time frames over the past 70+ years.

3. The Long, Long View of Interest Rates – Byrne Hobart

The single most important variable in economics is the risk-free interest rate, i.e. the price of money. Over time, the available data indicate that money has gotten much, much cheaper…

…First things first: a loan to the Duke of Burgundy in the 15th century and a loan to the US treasury in 2023 are completely different things. In the latter case, it’s a loan to a global hegemon that issues the world’s most-accepted reserve currency, a currency in which that loan will be repaid. The Duke, by contrast, is a person, not a country. He collects taxes, but perhaps intermittently, and some fraction of Burgundy consists of his personal property. His big source of uncertain expenses is military campaigns, which are also one of the few legible investments that can produce some theoretical return. But they’re usually a bad deal…

…So for the tiny set of people (like the Duke) who regularly borrowed large sums, their big lumpy expenses were probably ransoms, which means ransoms were potentially a big lumpy source of income as well. In other words, they took part in a physical contest which would lead to an unpredictable positive or negative payoff. 12% is expensive for a sovereign credit, but pretty cheap for a loan to an athlete whose primary source of income is wagering on the outcome of their own matches.

The process of replacing personal relationships with institutional ones has been gradual. Over time, though, it’s created more low-risk or even essentially risk-free lending opportunities. It’s hard to draw a dividing line here, especially because some countries move in and out of risk-free status depending on the market’s general fears and the specifics of their political situation (the spread between German and Italian rates, for example, is a good proxy for the market’s view of how stable Europe is). In the case of the US, we really do have a stable, low-risk borrower—but even the US likes to periodically stress-test the market with debt ceiling fights.

There were lower-risk bonds in the early period of the chart; the paper mentions people earning 5% lending to the governments of Florence and Venice, for example. But in practice, the only risk-free investment at the time was taking hard currency and literally burying it; a risk-free asset with a positive yield is a 19th-century innovation.

Adjust for that, and you’d end up with a new chart: the rate of return on a risk-free investment was on average slightly negative (the inconvenience of hiding it, the risk of losing it, and the possibility, for some currencies, of devaluation). Then it jumped some time in the 19th century despite the fact that governments still needed to borrow, they were now larger and better at tax collection than their predecessors, but their bonds now competed with high-return private sector opportunities). And then we see a decline, or a reversion to the mean, starting in the 1980s: growth declined, rates declined, and the real rate of return on risk-free assets dropped. That’s recently been disrupted again, with the resurgence in inflation since 2021.

But the other big driver of that secular-decline chart is still in place, and it pushes the equilibrium interest rate relentlessly lower. The biggest factor is the existence of retirement…

…The key difference in modernity is that we took a luxury previously available only to the elite, i.e. the ability to live well entirely off the labor of others, and made it an option for anyone who chose to sock away enough money in their 401(k). (The “labor of others” is now some miniscule share of the profits from every company in whatever index the retiree in question has invested in, and their capital comes from forgone consumption when they themselves had a job, but the fundamentals are unchanged.) Longer lifespans that mostly lead to longer retirement rather than more working years will necessarily increase global savings; whether this old-age saving is mediated through private sector investments or through public pensions like social security, it creates an implicit asset on the retiree’s (or future retiree’s) economic balance sheet, a corresponding liability on the part of whoever is offering that income, and thus a demand for income-producing assets that match that liability…

…Technology is also a driver of rates. But the direction is noisy. The more sophisticated the financial system, the more likely it is that deploying new technology will be inflationary. There are two forces at work: in the long run, new technology is deflationary over time, since we’re getting more from less—the number of labor-hours required for illuminating a room for an hour, traveling across the country, or getting a nutritious meal has continuously declined. But when technology is being deployed, it’s inflationary, because there’s more demand for investment and labor. So asking whether the impact of a given technological development is net inflationary or deflationary over, say, the next decade, amounts to asking: how quickly is it getting deployed? If we developed some radically transformative new technology, like a way to generate low-cost, low-emissions energy from trivial amounts of a fairly abundant natural resource, taking advantage of this would require spending money on construction labor, equipment, and raw materials, but would lead to energy abundance over time…

…Last big feature in the real rates model is the existence of a reserve currency. Early in the time series, there were reserve-like currencies; some kinds of money were good for transacting or paying taxes in a specific place, but a ducat or florin was useful just about anywhere, because Venetian and Florentine merchants were almost everywhere, and people who did business with them were everywhere else. But these were small, open economies, of the sort that can’t absorb significant inflows. They’re closer to the Swiss franc than to the dollar: everyone knew they were safe, but it wasn’t possible for everyone in the world to denominate savings in the same currency.

What the dollar as a reserve currency does is to create demand for dollar-denominated savings from exporters, who a) want to keep their currency from appreciating too quickly, and b) want to have local dollar liquidity to ensure that they don’t have a ruinous financial crisis if their exports slow down. The relevant exporters, and the policy consequences, have varied over time; sometimes the petrodollar is the dominant form, and sometimes it’s manufacturing economies. But the direction persists, and as long as the dollar has such strong network effects, there will be foreign demand for dollar-denominated savings with minimal interest rate sensitivity.

Extremely long-term trends are important, because they’re the closest thing we have to true economic fundamentals. If something was true under feudalism and democracy, in wartime and peacetime, in an agrarian economy, a manufacturing economy, and a services-based one, it’s probably just a fact of economic life. The decline in real rates is noisy in the chart and noisier still in reality, but it’s something we should accustom ourselves to: if people live longer than they work, and provide for their old age by saving money; if technological advances are deflationary over time and haven’t been happening as often as they did at the peak; and if countries still grudgingly rely on the dollar; then the long-term set point for rates will decline over time. 

4. How Does the World’s Largest Hedge Fund Really Make Its Money? – Rob Copeland

Since founding Bridgewater in his Manhattan apartment in 1975, Mr. Dalio has been said to have developed prodigious skill at spotting, and making money from, big-picture global economic or political changes, such as when a country raises its interest rates or cuts taxes. That made both a lot of sense and none at all; what was it about Bridgewater that made it so much better at predictions than any other investor in the world trying to do the exact same thing?

Bridgewater earned worldwide fame for navigating the 2008 financial crisis, when the firm’s main fund rose 9 percent while stocks dropped 37 percent, making Mr. Dalio a sought-after adviser for the White House and Federal Reserve and attracting new deep-pocketed clients to his fund. Yet the hedge fund’s overall descriptions of its investment approach could be maddeningly vague. Mr. Dalio often said he relied on Bridgewater’s “investment engine,” a collection of hundreds of “signals,” or quantitative indicators that a market was due to rise or fall. Bridgewater rarely revealed any details of these signals, citing competitive pressure, but if they pointed to trouble ahead or even to uncertainty, Bridgewater said it would buy or sell assets accordingly — even if Mr. Dalio’s own gut might have told him otherwise…

…What confused rivals, investors and onlookers alike was that the world’s biggest hedge fund didn’t seem to be much of a Wall Street player at all. Much smaller hedge funds could move the markets just by rumors of one trade or another. Bridgewater’s heft should have made it the ultimate whale, sending waves rolling every time it adjusted a position. Instead, the firm’s footprint was more like that of a minnow.

What if the secret was that there was no secret?…

…In early 2015, Bill Ackman, the endlessly opinionated hedge fund manager, took the first whack. The billionaire founder of Pershing Square Capital had long found Mr. Dalio’s public pronouncements about his quantitative investment style to be generic and even nonsensical. At a charity event in February that year, Mr. Ackman grilled Mr. Dalio during an onstage interview about how Bridgewater handled the assets it managed.

Mr. Dalio responded: “Well, first of all, I think it’s because I could be long and short anything in the world. I’m basically long in liquid stuff. And I can be short or long anything in the world, and I’m short or long practically everything.” He also noted that some 99 percent of Bridgewater trading was automated, based on longtime, unspecified rules. “They’re my criteria, so I’m very comfortable,” Mr. Dalio said.

Mr. Ackman tried another tack. He gave Mr. Dalio a layup, the sort of question asked six times an hour on business television. “Let’s say you were to buy one asset, or one stock, or one market, or one currency. Where would you put your money?” There was a pause, then Mr. Dalio said, “I don’t do that.” He went on to lay out how Bridgewater’s hundreds of investment staff members spent their days, describing a data-driven approach.

Onstage, Mr. Ackman would remark that it was “one of the most interesting conversations I’ve ever had.” But he walked away shaking his head.

“What was he even talking about?” he vented afterward…

…This all piqued the interest of a Boston financial investigator, Harry Markopolos, who had been a no-name analyst in the late 1990s when his boss asked him to reproduce a rival’s trading strategy that seemed to pay off handsomely. Mr. Markopolos couldn’t, but he figured out enough that he began chatting with the Securities and Exchange Commission. Six years later, when his warnings about Bernie Madoff proved right, Mr. Markopolos earned national fame.

To Mr. Markopolos, what was happening in Westport, Conn., where Bridgewater has its headquarters, raised serious questions, according to people who worked with him. Here lay another giant hedge fund famed for an investment approach that no competitors seemed to understand. He got his hands on Bridgewater’s marketing documents, including a summary of the firm’s investment strategy and a detailed chart of fund performance. Bridgewater described itself as a global asset manager, yet these documents didn’t name a single specific asset that had made or lost the firm money. An investment-performance chart indicated the firm seldom had a down year — even when Mr. Dalio’s public predictions proved off, Bridgewater’s main fund, Pure Alpha, consistently seemed to end the year around flat.

As he looked over the documents, Mr. Markopolos felt a familiar flutter in his heart…

…Mr. Markopolos also went to see David Einhorn of Greenlight Capital, the hedge fund billionaire famed for spotting frauds. Mr. Einhorn welcomed Mr. Markopolos into his Manhattan office, and they sat down with a team of Greenlight analysts who Mr. Einhorn said were interested in investigating Bridgewater themselves, two people present recalled.

After hearing Mr. Markopolos’s talk, Mr. Einhorn said it tracked with his suspicions, too. That was all the encouragement Mr. Markopolos needed. Bridgewater, he wrote to the S.E.C., was a Ponzi scheme.

Bridgewater was not a Ponzi scheme. Which is not to say that all was as Mr. Dalio so often described it.

The S.E.C. and other regulators dutifully took meetings with Mr. Markopolos and his team. The whistle-blowers’ report was passed through the organization, and a team at the agency looked into it. (The S.E.C. declined to comment.)

According to a person briefed on the investigation, what they concluded, in part, was that the world’s biggest hedge fund used a complicated sequence of financial machinations — including relatively hard-to-track trading instruments — to make otherwise straightforward-seeming investments. It made sense to the S.E.C. that rivals couldn’t track them…

…As it turned out, by the time the S.E.C. received Mr. Markopolos’s submission, the regulators had already looked into Bridgewater. In the wake of the Madoff fraud, and never having really dug into the world’s biggest hedge fund, S.E.C. staff spent a stretch in Westport, deeply studying the firm’s operations. The S.E.C. did not much bother with how Bridgewater made money, just that it did indeed invest its clients’ accounts…

…Of Bridgewater’s roughly 2,000 employees at its peak — and hundreds more temporary contractors — fewer than 20 percent were assigned to investments or related research. (The rest worked on operations tasks, including the expansion of Mr. Dalio’s “Principles.”) And of those investment staff members, many held responsibilities no more complicated than those of the average college student. They worked on economic history research projects and produced papers that Mr. Dalio would review and edit. As for whether those insights made it into Bridgewater’s trading, most research employees knew not to ask, current and former investment employees said.

Only a tiny group at Bridgewater, no more than about 10 people, enjoyed a different view. Mr. Dalio and his longtime deputy, Greg Jensen, plucked the members from the crew of Bridgewater investment associates and offered them entry to the inner sanctum. In exchange for signing a lifetime contract — and swearing never to work at another trading firm — they would see Bridgewater’s inner secrets…

…There were two versions of how Bridgewater invested hundreds of billions of dollars in the markets. One version, Mr. Dalio told the public and clients about. The other version, current and former investment employees said, happened behind closed doors.

In the first version, Bridgewater’s hedge funds were a meritocracy of ideas. Every investment staff member or researcher could suggest an investment notion, and the Bridgewater team would debate the merits of the thesis dispassionately, incorporating a broad study of history. Ideas from investment employees with a record of accurate predictions would over time carry more weight and earn backing with more client money. Investors flocked to the approach, assured that Bridgewater — unlike other hedge funds — would not rise or fall off a single trade or prediction from the firm founder. It was the Wall Street equivalent of Darwinism, with a thick wallet…

…The bottom line: Mr. Dalio was Bridgewater and Mr. Dalio decided Bridgewater’s investments. True, there was the so-called Circle of Trust. But though more than one person may have weighed in, functionally only one investment opinion mattered at the firm’s flagship fund, employees said. There was no grand system, no artificial intelligence of any substance, no holy grail. There was just Mr. Dalio, in person, over the phone, from his yacht, or for a few weeks many summers from his villa in Spain, calling the shots.

Lawyers for Mr. Dalio and Bridgewater said the hedge fund “is not a place where one man rules because the system makes the decision 98 percent of the time.” They said that “the notion that Mr. Dalio ‘call[ed] the shots’ on Bridgewater’s investments is false.”…

…On Wall Street, the phrase “information advantage” often carries an unseemly implication, suggesting that one is engaged in insider trading. Mr. Dalio’s information advantage, however, was as legal as it was vast.

Bridgewater’s target was information about entire nations. According to employees involved with the effort, Mr. Dalio heavily courted well-connected government officials from whom he might divine how they planned to intervene in their economies — and Bridgewater used these insights to make money in its funds.

Anywhere seemed fair game, even Kazakhstan. The Central Asian nation was not on the first page in any Wall Street manual. Ruled by an authoritarian government, it is the globe’s largest landlocked country yet sparsely populated. In 2013, Kazakhstan began developing what was then the most expensive oil project — a giant field in the Caspian Sea — helping it grow a $77 billion sovereign wealth fund. That money would have to be invested somewhere, and Bridgewater’s client services squad put a meeting on Mr. Dalio’s calendar with Berik Otemurat, the fund’s chief, a bureaucrat who had begun his career barely 10 years earlier…

…Inside Bridgewater, a relationship meant access. The country’s new oil field had taken more than a decade to develop, with near-constant delays. Anyone who knew how the project was proceeding could adjust bets on oil accordingly. Bridgewater’s representatives told the delegation that their firm would be happy to offer free investing advice, and Bridgewater’s team would likewise appreciate the opportunity to ask questions about industries of local expertise…

…The longest-term project for Mr. Dalio was in China, where he made frequent trips. Mr. Dalio hired China Investment Corporation’s former chairman to a cushy job as head of a Dalio charity in China, and he became close with Wang Qishan, who would later become China’s vice premier and widely considered the second most powerful person in the country. Mr. Dalio would occasionally tell Chinese government representatives that when they invested with Bridgewater, their fees were not merely being sent back to America. “Whatever fees you pay, I will donate back to China personally,” he said in one meeting, according to a person present.

In media interviews, Mr. Dalio stuck to a fixed, laudatory line about the country’s leadership. It was “very capable,” he said, over and again, sometimes repeating the phrase more than once in an interview. Those same leaders, he would also say inside Bridgewater, were quick to ask him for advice.

To any reasonable observer — and even to the Chinese themselves — Mr. Dalio was the paradigm of a China booster. But there was also an advantage that could be played. He asked the Circle of Trust to help create a way for Bridgewater’s funds to place bets against Chinese assets, in an offshore way that China’s government couldn’t track. That way, when Bridgewater took the wrong side of China, no one would know…

…With the hope of turning around the firm’s investment performance, members of the Circle of Trust put together a study of Mr. Dalio’s trades. They trawled deep into the Bridgewater archives for a history of Mr. Dalio’s individual investment ideas. The team ran the numbers once, then again, and again. The data had to be perfect. Then they sat down with Mr. Dalio, according to current and former employees who were present. (Lawyers for Mr. Dalio and Bridgewater said that no study was commissioned of Mr. Dalio’s trades and that no meeting took place to discuss them.)

One young employee, hands shaking, handed over the results: The study showed that Mr. Dalio had been wrong as much as he had been right. Trading on his ideas lately was often akin to a coin flip.

5. Palmer Luckey – Inventing The Future Of Defense – Patrick O’Shaughnessy and Palmer Luckey

Patrick: [00:01:42] Palmer, I always like starting somewhere of recent passion, you started to give me some amazing materials, so we stopped and we restarted the recording here. And maybe we’ll just begin with this idea that you were telling me about I’m always interested by major changes that might happen in the world that nobody is really talking about.

And until you said the word synthetic long chain hydrocarbon fuel to me 5 minutes ago, I’d never heard that combination of words before. So maybe you can start there and explain why that topic is of interest to you today.

Palmer: [00:02:16] Well, it’s of interest because there’s a lot of money being bet by companies, but also governments on a handful of specific technological pads for electrifying vehicles, battery electric vehicles, hydrogen electric vehicles.

If you can make synthetic long chain hydrocarbon fuels, in other words, synthetic gasoline, synthetic diesel synthetic jet fuel using carbon from the atmosphere in particular, there’s a lot of ways to do it. Boiling it down, one of the ways to do it. You take water, you crack it into hydrogen and oxygen using some kind of energy source like a nuclear power plant and then you bond it with carbon to make hydrocarbons and then you’ve got artificial gasoline coming out the other end.

If someone can figure out how to do that, cheaply enough. First of all, it’s an incredible carbon capture mechanism. Two, if you can do it cheaply enough, let’s say, $1 per gallon, then all of these trillions of dollars in investment into battery electric vehicles and hydrogen electric vehicles become really a waste of money and a waste of time. There are, of course, some advantages to battery electrical vehicles, hydrogen electric vehicles that wouldn’t apply.

But for the most part, especially on the aviation side, the ability to make fuels to just plug into existing fully known, fully optimized, fully understood and even fully certified systems that are better than the ones that cost hundreds of billions of dollars to develop. That also aren’t as good electric planes spend most of their energy hauling around their energy storage, not people or payload, which, of course, means you need to put more energy into them in the first place than even synthetic fuels with a pretty low conversion efficiency.

The reason it’s so interesting to me is that the bet seems so mis-apportioned, you have so much money going into battery electric vehicles and electrification of electrical infrastructure that’s not moving. And almost nobody betting that you can build systems that make dollar per gallon hydrocarbon fuels using either biological processes like algae farms or mechanical processes, however you’re making the synthetic fuels.

And of course, if someone figures it out, they’re going to really knock a whole bunch of stuff sideways. And we talked about this before, but it’s especially interesting because lots of companies make poor technical decisions and they decide to go down a product path, it doesn’t make sense.

I personally feel like this is a case where you have dozens of governments around the world have decided to commit to a particular product path that isn’t optimal. It’s not the optimal end state or the optimal near term, and they’re dumping hundreds of billions, maybe trillions of dollars into that bet. It’s something that I’m worried about.

Patrick: [00:04:38] When you find an idea like this. First of all, I’m curious how you found this one in particular, but the world has just gone through the superconductor craze, which for a week there it was like, well, if this is real, it changes everything. And then very quickly realize, oh, it’s not real, and it seems like, again, a remote possibility and not terribly likely. So maybe there won’t be loads of dollars trying to create superconductors. With something like this, how do you weigh the potential against the odds of us being able to do it with your own time and investigation?

Palmer: [00:05:05] Well, in this case, it’s 1950s era Department of Energy documents regarding potential energy futures for the United States, accounting for what they assumed would be a nuclear future. I always find it interesting when I go into an area that I don’t understand to try and understand it better.

Even — if I want to understand what’s going on in the modern day, you want to go back to the future and say, what were people saying, back then, what are the ideas that people aren’t even discussing right now? Because I don’t want to be too pessimistic on present, but if you look through a lot of the academic literature and government literature today on energy solutions for the United States, they’re really, really narrow minded.

They are really, really politically driven, it’s all about what is aligned with the current debates going on between political parties. The people in these agencies are largely tied to the things that have already been deemed important. And if you go back on the other hand, to let’s say, post-World War II America, where we were really thinking from first principles, what do we want the world to look like? What do we want the United States to look like?

And what are all of the ways we could get there? They were thinking very expensively. And so this idea of extremely cheap synthetically manufactured biofuels that would get rid of strategic dependence on limited oil supply or allow us to sell off our oil supply to make money in the near term while still having a robust renewable base of energy to power our industrial machine, our war machine, you name it

That was an idea that was of interest to people in the ’40s, the ’50s, the ’60s. I think mostly all of this fell apart when it became clear that we were not going to be a nuclear economy mostly in for political reasons, not practical or technological reasons. So this was the case, right?

I didn’t actually have to be a big thinker. I just had to go say, what were people thinking when they were allowed to think whatever they wanted and when they could think really, really big things? And it’s not even just on fuel. There’s other interesting things they were thinking about back then, like today, if you say, what’s the best way to help the environment in the United States? It’s actually very calcified.

There’s very little consideration for things that are better than what currently exists. You kind of preserving the status quo as the ultimate good. There’s very little consideration for what is better for people, what would be better for more animals. And if you look back again the earlier parts of the United States history, there were serious proposals by the Department of Interior to say, what should the United States ecosystem look like if we could make it whatever we wanted?

What animals would we have? Would we have hippos? Would we have rhinos? Why not? Why don’t we put hippos in Louisiana? There was just this endless possibility, big thinking. But what’s crazy is it’s not even big thinking in the way that we would think of today. When people think a big thinking, they immediately jump to really hard ideas, fusion power and what if we could bio-engineer ourselves? The ideas they were having, they would have a big impact, but they’re actually easy ideas.

Just what if we brought some hippos, put them over there on that swamp? The big idea is what could we do economically? Would that be a good meat source? Could we use that as a better protein source that is less damaging to environments that we’re trying to preserve than what we’re currently doing with cows? And those types of ideas, they’re not taken seriously today. People treat you like a crank if you step outside the orthodoxy…

…Patrick: [00:22:27] When you approach those problems, I have heard you talk about those other two, which is totally fascinating how you approach things. Maybe even before I ask this question, I’m just curious what your method of invention is. So you find an interesting problem either that you want to work in this case, don’t want to work on necessarily. Is your method iterative? Is it more theoretical? Describe the way that you start to invent in a field when you approach something for the first time.

Palmer: [00:22:50] Well, it depends on if it’s a field that I know a lot about or don’t know a lot about. If you know a lot about something, it’s easier to get right into the iterative side of things and know that you’re probably on a pretty reasonable path. In that case, iteration is a valuable tool to move very quickly, find out what works, find out what doesn’t and then continuously make it better.

The risk with going with a strongly iterative approach in areas that you maybe don’t understand, and you might even think you do, but let’s say you truly don’t is that there might be much better approaches that you should have started iterating on. Or that you should have examined before you committed to one particular path. I talked about this earlier, but it’s really about going back to the future. I love to go and see what everyone else who solved this problem thinks about it, not in the recent times.

I don’t want to know what my competition looks like because when I started Oculus, I wasn’t looking at what existing companies were doing in VR because clearly, they were all doing it wrong. Whatever they were doing was not working. I was not going to look around at the handful of VR companies that existed in that time and learn anything except how to fail. So I wanted to look into the past, what were people thinking when they were thinking bigger, when they were willing to look at wackier paths.

When they were willing to consider things that have been eliminated often because technologies just weren’t ready. There’s a lot of technologies that have been discarded because they weren’t practical at the time, and nobody ever revisited them and said, “Hey, I actually think the time has come.” A good example is with the Rift. Doing real-time distortion correction is not a new idea. It existed in the 1980s and 1990s in the virtual reality community. They have been discarded even by NASA.

There’s a fascinating NASA paper where they talk about doing real-time geometry distortion correction on a virtual reality headset that made the optics lower distortion and allow them to, therefore, use wider field of view. Lighter weight optics than would have otherwise been required to have an optically perfect image. And the conclusion was, yes, this is a really good way to save money and to save weight, but it’s too computationally expensive. We’re using most of our processing power to warp the image in real time rather than render this wire frame image.

And so this is not a good approach. You should just do it optically, and then you don’t have to have a more expensive computer. But back then, compute was the expensive part and the optic transform used up a lot of your compute. Nobody reexamined this idea even as computers got better. Nobody went back until me and said, “Wait a second, you can do real-time transform on a modern graphics card for like 1% or 2% of your render horsepower.” And also your graphics card doesn’t cost $100,000 anymore. It costs a few hundred dollars.

And so if you’re worried about that 1% or 2% impact, just buy a graphics card that costs a few dollars more, so you can save hundreds or thousands of dollars on the VR headset itself by using optics that have geometric distortion or in chromatic aberration, for example. That was an idea that had been discarded and nobody ever came back to it.

And most of the things that made the Rift successful were ideas like that, there’s a few others where I was just going back to the future and realizing, “Wait a sec, these ideas, they were actually pretty good. They were just a little too early.”…

…Patrick: [00:35:55] Just trying to think about a framework to discuss the state of weapons technology or the history of weapons technology with you. And the cleanest I could come up with a stupid consultant 2×2, where on one axis, you have offensive versus defensive technologies dominating. And on another, you have democratic versus very non-democratic. Musket’s on one end, everyone has the same amount of power. And on nuclear weapons on the other, one person has a gazillion times as much as the Musket guy or something. Is that a good way to think about where we might pop through history and weapons technology? Is there some other way that you would approach thinking about an era and weapons technology?

Palmer: [00:36:29] Offensive and defensive is definitely the right scale. Distributed or not distributed, I’m not sure. That I actually think matters less by weapon system and more by the power dynamic of the nation. There’s a question here, are the people aligned with the government or are they opposed to the government and to what degree? If you have them where there are neutral parties that accept each other’s existence, that’s one thing.

Let’s say you have a country like Ukraine where implausibly to the Russians, they had formed a strong national identity, and there were people who were willing to die in large numbers for their country. That’s a case where there’s people who are very much aligned with the broader goals of their nation. On the other hand, if you look at a lot of African nations, even a lot of Middle Eastern nations, you have a huge mismatch between what the political class wants and what the every man wants.

And so I think a better access is actually more like democratic versus autocratic technologies. In that, there are a lot of technologies that are much more useful for controlling your own population than for preserving their rights against hostile actors. There’s a lot of countries whose military effectively is an internal peacekeeping force to crush dissent. That’s actually what it’s for.

And there’s a lot of tools made by companies like SenseTime in China that are fundamentally — they are not useful for going to other countries and preserving our rights. They’re not useful for defending yourself from an invader. They are only useful for controlling people in your nation. This is one of the reasons that China is exporting these technologies in the same way that the Soviet Union exported AK-47s, which you could say are on this distributed and offensive side, if you were to look at it that way.

But the reason they were actually doing that is they wanted to arm nations with the tools that they needed to keep their civilian population in check and keep them in power and they wanted to threaten them and say, “Hey, if you ever get out of line, we’re going to stop providing you with these arms and systems you need, and then you’re going to immediately get a violent revolution and you’re probably going to get killed.” It was a great motivation for people to stay stuck to the Soviet Union. This idea that, “We are the thing that allows you to keep your people in check, and without us, that is over.”

China is pursuing a similar strategy. They’re going to African nations and saying, “Hey, we’re going to help build out this infrastructure in your ports, on your roads, in your police force, in your military. We’re going to build AI camera systems that track dissidents for you. They track where they’re shopping, where they’re going, where they’re riding trains. We’re going to allow you to monitor all their communications on the telecommunications side. So let us sell you telecommunications gear, and you get all these back doors that allow you to control people who’re trying to come after you.”

But a bargain with the devil that they’re making is, “Oh, and by the way, if you ever do anything that’s counter to Chinese interests, we’re going to pull all of this and you’re going to lose all your tools for controlling your population and you’re going to be dead inside of a week.” And say SenseTime is on the autocratic, authoritarian side of that scale because it has almost no application in preserving freedom or in deterring an invasion. It’s only for controlling your own people…

…Patrick: [00:48:53] So you built Lattice. Now the world is catching up. Everyone wants to build an AI platform system. What have you learned about building one? What are the components of it? What do you think about AI at large, the cost associated with compute and AI? All of these big things you’ve been working in for a while, and now it matters to everybody. So what would you contribute as the lessons that you’ve learned there so far?

Palmer: [00:49:11] It’s been a double-edged sword, I think. We’ve been working on AI for defense since literally day one. That was the whole pitch. The second page of our pitch deck was a quote from Vladimir Putin. He was talking about artificial intelligence, and he said, “The country that wins in this sphere will become the ruler of the entire world,” which I love. It’s a very James Bond villain quote.

On the one hand, it sounded crazier at that time because AI wasn’t hot seven years ago. There were people who were interested in it, but it’s obviously not even a 0.01 of the attention that’s being dedicated to it today. The flip side of that is now that everyone is saying they’re AI, all of a sudden our message is getting diluted or, “Hey, we’ve actually been doing AI for defense for almost seven years now.” And now everyone is changing to say, “Yes, our systems are all powered by AI. It’s all AI-driven.” Some of that’s true, some of it’s not.

Now it actually is less differentiated than we were. You have to now get very clear about what the difference between a real usable, fieldable AI system looks like versus strapping together ChatGPT with whatever your quadcopter thing is and saying that it’s going to change the world. I do think what’s been helpful to us, though, with members of Congress, people in The Pentagon, even investors has been the explosion of firsthand understanding of how powerful AI can be that’s been driven by these large language models.

Obviously, the things that we are building that fuse data from thermal vision and radar and signals intelligence processors, that then calculate optimal weapons pairing against that target, very different use of AI than a thing that you tell to write a poem about your car.

But the fact that every member of Congress has been able to use ChatGPT, the fact that all these people in The Pentagon have seen and used systems, doing things that they never believed a computer could do has, I think, expanded people’s minds in general towards the possibility that maybe people can be replaced by AI in certain use cases, maybe there are areas where computers really can do a job as well or better than a person. A lot of skeptics, I think, have changed their minds because they type something into ChatGPT, it did something for them, and they said, “Wow, computers sure are amazing these days.

Patrick: [00:51:20] What has AI most unlocked?

Palmer: [00:51:22] The most important thing that it has unlocked for us is ability to scale. People focus on use cases where AI can do better than a person or even better than a team of 100 or 1,000 people at some one specific task. I think a lot of the more interesting use cases are where you can do as good of a person but without a person having to do it.

Let’s use an example. Let’s say that I’m going to deploy 1,000 autonomous cruise missiles. Those are going to be a lot more impactful than they would be if I had to have 1,000 people trying to remotely pilot 1,000 systems and tell them what to do, how to do it, have all those data links active. I guess it’s using AI to do things that would be impossible to do otherwise, either for real technical reasons like bandwidth or just for practical reasons. We don’t have thousands of pilots that we could dedicate to such a task. For me, I think that’s the biggest thing AI enables.

I’m less focused on the superhuman, super intelligent side of things and more, “Hey, this AI that’s running my autonomous helicopter, is it about as good as a pretty good helicopter pilot? Okay, that means that I can have one soldier managing a fleet of 25 airframes himself and just telling them, “Hey, I need you to clear this area. I need you to find this target I’m looking for. I need you to fly ahead of my convoy and watch for anything.” Now he doesn’t need 25 pilots and 25 helicopters to do that. That’s what I’m most excited about.

And it’s really important in a world where I think quantity is going to have a quality all of its own in these types of weapon systems. The best way to defeat a lot of our adversaries’ defenses is not through building a small number of exquisite systems. But quantities that are so large, they can’t possibly stop them.

And it’s especially important in a world where militaries are struggling to recruit. They’re trying to be more cost effective. They’re trying to put less money into salaries and disability payments and more into systems that are going to be fighting the adversary directly, robotic systems.

For example, the United Kingdom has said that they want to, over the next few years, reduce the size of their Navy by 30%, 30% personnel reduction. And because of that, they are doing things like dedicating one of their two aircraft carriers to being an autonomous aircraft launch system.

In other words, they want one of their carriers to only launch autonomous systems. You don’t have to have huge numbers of people to run and maintain these traditional manned systems. If that’s the world we’re going to live in where we need to ramp up the number of systems but also ramp down the number of people, the only thing that can fill the gap is automation.

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 companies mentioned. 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 29 October 2023:

1. CEO/CIO’s Final Investment Note: The Best of Times and The Worst of Times – Chuin Ting Weber

While there is sadness in the hearts of all of us at MoneyOwl, we know that the ups and downs of our journey are but a faint reflection of our larger condition – as a human race, as countries, as societies and as individuals. We all face circumstances that we cannot control, try as we may to do so. The shock of the Israel-Hamas conflict and the accompanying humanitarian disaster, the ongoing Russia-Ukraine war, and the gyrations in big economies both East and West, threatens to shake us and tempt us to despair. On an individual level, some of us may face unexpected shocks, tough times, or just unsettling uncertainties as we move from one season of life to another.

Yet, there must always be some beliefs in our lives that anchor us, so that our core will not be shaken. And as it is with our lives, so it is with investing. Whatever is happening around you and in the world, please remember that the human spirit for recovery and progress, has never been quenched by wars, pandemics, natural disasters or man-made crises. COVID-19 was the most recent example, but it was neither the first crisis we have overcome, nor would it be the last. As J.S. Mill put it, writing in a period that Charles Dickens described as both the best and the worst of times:

“What has so often excited wonder, is the great rapidity with which countries recover from a state of devastation…… An enemy lays waste a country by fire and sword, and destroys or carries away nearly all the moveable wealth existing in it: all the inhabitants are ruined, and yet in a few years after, everything is much as it was before.”

John Stuart Mill, “Principles of Political Economy”, 1848

When you invest in a globally diversified portfolio of stocks and bonds – instruments that companies and countries issue to finance their economic activities – what you are really investing in, is the future of human enterprise. It is a vote of confidence in the human race. In the long run, stock prices are driven by earnings, and earnings, by the increase in global aggregate demand, which is in turn driven by a combination of global population growth and the quest for increase in standards of living. That is why no matter how bad the crisis, the stock market always recovers and goes up in the long run. This is the reason the stock market has a positive expected return. It is backed by logic and evidence.

The principle, however, does not apply to individual companies, sectors or even countries. It is also not so easy to read the tea-leaves to try to catch the short-term turns of ups and downs, to do better than the market’s long-term return. The best of times often follows the worst of times. We just don’t know when it turns. While being in a bad season is temporary, being out of the market because you timed it wrong, is the one sure way of missing out on the recovery.

2. Higher For Longer vs. the Stock Market – Ben Carlson

I don’t know what the bond market is thinking but it’s worth considering the potential for rates to remain higher than we’ve been accustomed to since the Great Financial Crisis. So I used various interest rate and inflation levels to see how the stock market has performed in the past.

Are returns better when rates are lower or higher? Is high inflation good or bad for the stock market?…

…Surprisingly, the best future returns have come from both periods of very high and very low starting interest rates while the worst returns have come during average interest rate regimes.

The average 10 year yield since 1926 is 4.8% meaning we are at that long-term average right now. Twenty years ago the 10 year treasury was yielding around 4.3%. Yields have moved a lot since then…

…In that 20 year period the S&P 500 is up nearly 540% or 9.7% per year. Not bad…

…The average inflation rate since 1926 was right around 3%.

These results might look surprising as well. The best forward long-term returns came from very high starting inflation levels. At 6% or higher inflation, forward returns were great. At 6% or lower, it’s still pretty good but more like average.

So what’s going on here? Why are forward returns better from higher interest rates and inflation levels?

The simplest explanation is we’ve only had one regime of high interest rates over the past 100 years or so and two highly inflationary environments. And each of these scenarios was followed by rip-roaring bull markets. The annual inflation rate reached nearly 20% in the late-1940s following World War II. That period was followed by the best decade ever for U.S. stocks in the 1950s (up more than 19% per year). And the 1970s period of high inflation and rising interest rates was followed by the longest bull market we’ve ever experienced in the 1980s and 1990s.

A simple yet often overlooked aspect of investing is a crisis can lead to terrible returns in the short-term but wonderful returns in the long-term. Times of deflation and high inflation are scary while you’re living through them but also tend to produce excellent entry points into the market…

…It’s also important to remember that while volatility in rates and inflation can negatively impact the markets in the short-run, a long enough time horizon can help smooth things out.

Regardless of what’s going on with the economy, you’ll fare better in the stock market if your time horizon is measured in decades rather than days.

3. Drawdowns – Chris Mayer

A drawdown is how much a stock price declines from its peak before it recovers.

Drawdowns are part of the life of every investor. Invariably, if you own a stock for a long time, you are going to have to sit through several…

…A few examples from the book, which was published in 2015:

• Apple from its IPO in 1980 through 2012 was a 225-bagger. But you had to sit through a peak-to-trough loss of 80% — twice! And there were several 40% drops.
• Netflix, which has been a 60-bagger since 2002, lost 25% of its value in a single day — four times! And there was a four-month stretch where it dropped 80 percent.
• And Berkshire Hathaway, the best performing stock in the study, was cut in half four times.

What I found affirmed what Peter Lynch once said: “The real key to making money in stocks is not to get scared out of them.” …

…Not only do the best stocks suffer frequent (and lengthy) drawdowns, but the best investors also suffer drawdowns that would surprise most.

The aforementioned Peter Lynch, for example, had four severe drawdowns during his Hall of Fame run at Fidelity. Even though he returned a mind-boggling 29% annually, he had many drawdowns during those years, including three of more than 20% (one of which was a hair-raising 42% drop in 1987).

In summary: There is no defense against drawdowns if you are committed to a long-term, ownership approach to stocks. (Peter Lynch, by the way, was highly diversified and had a high turnover rate in his career – but still). In fact, I would go so far as to say that the ability to sit through drawdowns with equanimity is a source of outperformance. It is a competitive advantage over those that can’t. 

4. NVIDIA CEO Jensen Huang – Ben Gilbert, David Rosenthal, Jensen Huang

David: I love this tee-up of learning but not imitating, and learning from a wide array of sources. There’s this unbelievable third element, I think, to what Nvidia has become today. That’s the data center.

It’s certainly not obvious. I can’t reason from AlexNet and your engagement with the research community, and social media feed […]. You deciding and the company deciding we’re going to go on a five-year all-in journey on the data center. How did that happen?

Jensen: Our journey to the data center happened, I would say almost 17 years ago. I’m always being asked, what are the challenges that the company could see someday?

I’ve always felt that the fact that Nvidia’s technology is plugged into a computer and that computer has to sit next to you because it has to be connected to a monitor, that will limit our opportunity someday, because there are only so many desktop PCs that plug a GPU into. There are only so many CRTs and (at the time) LCDs that we could possibly drive.

The question is, wouldn’t it be amazing if our computer doesn’t have to be connected to the viewing device? That the separation of it made it possible for us to compute somewhere else.

One of our engineers came and showed it to me one day. It was really capturing the frame buffer, encoding it into video, and streaming it to a receiver device, separating computing from the viewing.

Ben: In many ways, that’s cloud gaming.

Jensen: In fact, that was when we started GFN. We knew that GFN was going to be a journey that would take a long time because you’re fighting all kinds of problems, including the speed of light and—

Ben: Latency everywhere you look.

Jensen: That’s right.

David: To our listeners, GFN GeForce NOW.

Jensen: Yeah. GeForce NOW.

David: It all makes sense. Your first cloud product.

Jensen: That’s right. Look at GeForce NOW. It was Nvidia’s first data center product.

Our second data center product was remote graphics, putting our GPUs in the world’s enterprise data centers. Which then led us to our third product, which combined CUDA plus our GPU, which became a supercomputer. Which then worked towards more and more and more.

The reason why it’s so important is because the disconnection between where Nvidia’s computing is done versus where it’s enjoyed, if you can separate that, your market opportunity explodes.

And it was completely true, so we’re no longer limited by the physical constraints of the desktop PC sitting by your desk. We’re not limited by one GPU per person. It doesn’t matter where it is anymore. That was really the great observation.

Ben: It’s a good reminder. The data center segment of Nvidia’s business (to me) has become synonymous with how is AI going. And that’s a false equivalence. It’s interesting that you were only this ready to explode in AI in the data center because you had three-plus previous products where you learned how to build data center computers. Even though those markets weren’t these gigantic world-changing technology shifts the way that AI is. That’s how you learned.

Jensen: That’s right. You want to pave the way to future opportunities. You can’t wait until the opportunity is sitting in front of you for you to reach out for it, so you have to anticipate.

Our job as CEO is to look around corners and to anticipate where will opportunities be someday. Even if I’m not exactly sure what and when, how do I position the company to be near it, to be just standing near under the tree, and we can do a diving catch when the apple falls. You guys know what I’m saying? But you’ve got to be close enough to do the diving catch.

David: Rewind to 2015 and OpenAI. If you hadn’t been laying this groundwork in the data center, you wouldn’t be powering OpenAI right now.

Jensen: Yeah. But the idea that computing will be mostly done away from the viewing device, that the vast majority of computing will be done away from the computer itself, that insight was good.

In fact, cloud computing, everything about today’s computing is about separation of that. By putting it in a data center, we can overcome this latency problem. You’re not going to overcome the speed of light. Speed of light end-to-end is only 120 milliseconds or something like that. It’s not that long.

Ben: From a data center to—

Jensen: Anywhere on the planet.

Ben: Oh, I see. Literally across the planet.

Jensen: Right. If you could solve that problem, approximately something like—I forget the number—70 milliseconds, 100 milliseconds, but it’s not that long.

My point is, if you could remove the obstacles everywhere else, then the speed of light should be perfectly fine. You could build data centers as large as you like, and you could do amazing things. This little, tiny device that we use as a computer, or your TV as a computer, whatever computer, they can all instantly become amazing. That insight 15 years ago was a good one.

Ben: Speaking of the speed of light—David’s begging me to go here—you totally saw that InfiniBand would be way more useful way sooner than anyone else realized. Acquiring Mellanox, I think you uniquely saw that this was required to train large language models, and you were super aggressive in acquiring that company. Why did you see that when no one else saw that?

Jensen: There were several reasons for that. First, if you want to be a data center company, building the processing chip isn’t the way to do it. A data center is distinguished from a desktop computer versus a cell phone, not by the processor in it.

A desktop computer in a data center uses the same CPUs, uses the same GPUs, apparently. Very close. It’s not the processing chip that describes it, but it’s the networking of it, it’s the infrastructure of it. It’s how the computing is distributed, how security is provided, how networking is done, and so on and so forth. Those characteristics are associated with Melanox, not Nvidia.

The day that I concluded that really Nvidia wants to build computers of the future, and computers of the future are going to be data centers, embodied in data centers, then if we want to be a data center–oriented company, then we really need to get into networking. That was one.

The second thing is observation that, whereas cloud computing started in hyperscale, which is about taking commodity components, a lot of users, and virtualizing many users on top of one computer, AI is really about distributed computing, where one training job is orchestrated across millions of processors.

It’s the inverse of hyperscale, almost. The way that you design a hyperscale computer with off-the-shelf commodity ethernet, which is just fine for Hadoop, it’s just fine for search queries, it’s just fine for all of those things—

Ben: But not when you’re sharding a model across.

Jensen: Not when you’re sharding a model across, right. That observation says that the type of networking you want to do is not exactly ethernet. The way that we do networking for supercomputing is really quite ideal.

The combination of those two ideas convinced me that Mellanox is absolutely the right company, because they’re the world’s leading high-performance networking company. We worked with them in so many different areas in high performance computing already. Plus, I really like the people. The Israel team is world class. We have some 3200 people there now, and it was one of the best strategic decisions I’ve ever made….

…Ben: Let’s say you do get this great 10-year lead. But then other people figure it out, and you’ve got people nipping at your heels. What are some structural things that someone who’s building a business can do to stay ahead? You can just keep your pedal to the metal and say, we’re going to outwork them and we’re going to be smarter. That works to some extent, but those are tactics. What strategically can you do to make sure that you can maintain that lead?

Jensen: Oftentimes, if you created the market, you ended up having what people describe as moats, because if you build your product right and it’s enabled an entire ecosystem around you to help serve that end market, you’ve essentially created a platform.

Sometimes it’s a product-based platform. Sometimes it’s a service-based platform. Sometimes it’s a technology-based platform. But if you were early there and you were mindful about helping the ecosystem succeed with you, you ended up having this network of networks, and all these developers and customers who are built around you. That network is essentially your moat.

I don’t love thinking about it in the context of a moat. The reason for that is because you’re now focused on building stuff around your castle. I tend to like thinking about things in the context of building a network. That network is about enabling other people to enjoy the success of the final market. That you’re not the only company that enjoys it, but you’re enjoying it with a whole bunch of other people.

David: I’m so glad you brought this up because I wanted to ask you. In my mind, at least, and it sounds like in yours, too, Nvidia is absolutely a platform company of which there are very few meaningful platform companies in the world.

I think it’s also fair to say that when you started, for the first few years you were a technology company and not a platform company. Every example I can think of, of a company that tried to start as a platform company, fails. You got to start as a technology first.

When did you think about making that transition to being a platform? Your first graphics cards were technology. There was no CUDA, there was no platform.

Jensen: What you observed is not wrong. However, inside our company, we were always a platform company. The reason for that is because from the very first day of our company, we had this architecture called UDA. It’s the UDA of CUDA.

David: CUDA is Compute Unified Device Architecture?

Jensen: That’s right. The reason for that is because what we’ve done, what we essentially did in the beginning, even though RIVA 128 only had computer graphics, the architecture described accelerators of all kinds. We would take that architecture and developers would program to it.

In fact, Nvidia’s first business strategy was we were going to be a game console inside the PC. A game console needs developers, which is the reason why Nvidia, a long time ago, one of our first employees was a developer relations person. It’s the reason why we knew all the game developers and all the 3D developers.

David: Wow. Wait, so was the original business plan to…

Ben: Sort of like to build DirectX.

David: Yeah, compete with Nintendo and Sega as with PCs?

Jensen: In fact, the original Nvidia architecture was called Direct NV (Direct Nvidia). DirectX was an API that made it possible for the operating system to directly connect with the hardware.

David: But DirectX didn’t exist when you started Nvidia, and that’s what made your strategy wrong for the first couple of years.

Jensen: In 1993, we had Direct Nvidia, which in 1995 became DirectX.

Ben: This is an important lesson. You—

Jensen: We were always a developer-oriented company.

Ben: Right. The initial attempt was we will get the developers to build on Direct NV, then they’ll build for our chips, and then we’ll have a platform. What played out is Microsoft already had all these developer relationships, so you learned the lesson the hard way of—

David: […] did back in the day. They’re like, oh, that could be a developer platform. We’ll take that. Thank you.

Jensen: They did it very differently and did a lot of things right. We did a lot of things wrong.

David: You were competing against Microsoft in the nineties.

Ben: It’s like […] Nvidia today.

Jensen: It’s a lot different, but I appreciate that. We were nowhere near competing with them. If you look now, when CUDA came along and there was OpenGL, there was DirectX, but there’s still another extension, if you will. That extension is CUDA. That CUDA extension allows a chip that got paid for running DirectX and OpenGL to create an install base for CUDA.

David: That’s why you were so militant. I think from our research, it really was you being militant that every Nvidia chip will run CUDA.

Jensen: Yeah. If you’re a computing platform, everything’s got to be compatible. We are the only accelerator on the planet where every single accelerator is architecturally compatible with the others. None has ever existed.

There are literally a couple of hundred million—250 million, 300 million—installed base of active CUDA GPUs being used in the world today, and they’re all architecturally compatible. How would you have a computing platform if NV30 and NV35 and NV39 and NV40 are all different? At 30 years, it’s all completely compatible. That’s the only unnegotiable rule in our company. Everything else is negotiable.

David: I guess CUDA was a rebirth of UDA, but understanding this now, UDA going all the way back, it really is all the way back to all the chips you’ve ever made…

…Ben: Well, as we start to drift toward the end here, we spent a lot of time on the past. I want to think about the future a little bit. I’m sure you spend a lot of time on this being on the cutting edge of AI.

We’re moving into an era where the productivity that software can accomplish when a person is using software can massively amplify the impact and the value that they’re creating, which has to be amazing for humanity in the long run. In the short term, it’s going to be inevitably bumpy as we figure out what that means.

What do you think some of the solutions are as AI gets more and more powerful and better at accelerating productivity for all the displaced jobs that are going to come from it?

Jensen: First of all, we have to keep AI safe. There are a couple of different areas of AI safety that’s really important. Obviously, in robotics and self-driving car, there’s a whole field of AI safety. We’ve dedicated ourselves to functional and active safety, and all kinds of different areas of safety. When to apply human in the loop? When is it okay for a human not to be in the loop? How do you get to a point where increasingly human doesn’t have to be in the loop, but human largely in the loop?

In the case of information safety, obviously bias, false information, and appreciating the rights of artists and creators, that whole area deserves a lot of attention.

You’ve seen some of the work that we’ve done, instead of scraping the Internet we, we partnered with Getty and Shutterstock to create commercially fair way of applying artificial intelligence, generative AI.

In the area of large language models in the future of increasingly greater agency AI, clearly the answer is for as long as it’s sensible—and I think it’s going to be sensible for a long time—is human in the loop. The ability for an AI to self-learn, improve, and change out in the wild in a digital form should be avoided. We should collect data. We should carry the data. We should train the model. We should test the model, validate the model before we release it in the wild again. So human is in the loop.

There are a lot of different industries that have already demonstrated how to build systems that are safe and good for humanity. Obviously, the way autopilot works for a plane, two-pilot system, then air traffic control, redundancy and diversity, and all of the basic philosophies of designing safe systems apply as well in self-driving cars, and so on and so forth. I think there are a lot of models of creating safe AI, and I think we need to apply them.

With respect to automation, my feeling is that—and we’ll see—it is more likely that AI is going to create more jobs in the near term. The question is what’s the definition of near term? And the reason for that is the first thing that happens with productivity is prosperity. When the companies get more successful, they hire more people because they want to expand into more areas.

So the question is, if you think about a company and say, okay, if we improve the productivity, then need fewer people. Well, that’s because the company has no more ideas. But that’s not true for most companies. If you become more productive and the company becomes more profitable, usually they hire more people to expand into new areas.

So long as we believe that they’re more areas to expand into, there are more ideas in drugs, there’s drug discovery, there are more ideas in transportation, there are more ideas in retail, there are more ideas in entertainment, that there are more ideas in technology, so long as we believe that there are more ideas, the prosperity of the industry which comes from improved productivity, results in hiring more people, more ideas.

Now you go back in history. We can fairly say that today’s industry is larger than the world’s industry a thousand years ago. The reason for that is because obviously, humans have a lot of ideas. I think that there are plenty of ideas yet for prosperity and plenty of ideas that can be begat from productivity improvements, but my sense is that it’s likely to generate jobs.

Now obviously, net generation of jobs doesn’t guarantee that any one human doesn’t get fired. That’s obviously true. It’s more likely that someone will lose a job to someone else, some other human that uses an AI. Not likely to an AI, but to some other human that uses an AI.

I think the first thing that everybody should do is learn how to use AI, so that they can augment their own productivity. Every company should augment their own productivity to be more productive, so that they can have more prosperity, hire more people.

I think jobs will change. My guess is that we’ll actually have higher employment, we’ll create more jobs. I think industries will be more productive. Many of the industries that are currently suffering from lack of labor, workforce is likely to use AI to get themselves off their feet and get back to growth and prosperity. I see it a little bit differently, but I do think that jobs will be affected, and I’d encourage everybody just to learn AI…

…David: Well, and that being our final question for you. It’s 2023, 30 years anniversary of the founding of Nvidia. If you were magically 30 years old again today in 2023, and you were going to Denny’s with your two best friends who are the two smartest people you know, and you’re talking about starting a company, what are you talking about starting?

Jensen: I wouldn’t do it. I know. The reason for that is really quite simple. Ignoring the company that we would start, first of all, I’m not exactly sure. The reason why I wouldn’t do it, and it goes back to why it’s so hard, is building a company and building Nvidia turned out to have been a million times harder than I expected it to be, any of us expected it to be.

At that time, if we realized the pain and suffering, just how vulnerable you’re going to feel, and the challenges that you’re going to endure, the embarrassment and the shame, and the list of all the things that go wrong, I don’t think anybody would start a company. Nobody in their right mind would do it.

I think that that’s the superpower of an entrepreneur. They don’t know how hard it is, and they only ask themselves how hard can it be? To this day, I trick my brain into thinking, how hard can it be? Because you have to.

Ben: Still, when you wake up in the morning.

Jensen: Yup. How hard can it be? Everything that we’re doing, how hard can it be? Omniverse, how hard can it be?

David: I don’t get the sense that you’re planning to retire anytime soon, though. You could choose to say like, whoa, this is too hard.

Ben: The trick is still working.

David: Yeah, the trick is still working.

… Jensen: Yeah. The thing to keep in mind is, at all times what is the market opportunity that you’re engaging in? That informs your size. I was told a long time ago that Nvidia can never be larger than a billion dollars. Obviously, it’s an underestimation, under imagination of the size of the opportunity. It is the case that no chip company can ever be so big. But if you’re not a chip company, then why does that apply to you?

This is the extraordinary thing about technology right now. Technology is a tool and it’s only so large. What’s unique about our current circumstance today is that we’re in the manufacturing of intelligence. We’re in the manufacturing of work world. That’s AI. The world of tasks doing work—productive, generative AI work, generative intelligent work—that market size is enormous. It’s measured in trillions.

One way to think about that is if you built a chip for a car, how many cars are there and how many chips would they consume? That’s one way to think about that. However, if you build a system that, whenever needed, assisted in the driving of the car, what’s the value of an autonomous chauffeur every now and then?

Obviously, the problem becomes much larger, the opportunity becomes larger. What would it be like if we were to magically conjure up a chauffeur for everybody who has a car, and how big is that market? Obviously, that’s a much, much larger market.

The technology industry is that what we discovered, what Nvidia has discovered, and what some of the discovered, is that by separating ourselves from being a chip company but building on top of a chip and you’re now an AI company, the market opportunity has grown by probably a thousand times.

Don’t be surprised if technology companies become much larger in the future because what you produce is something very different. That’s the way to think about how large can your opportunity, how large can you be? It has everything to do with the size of the opportunity.

5. The 4 Billion Pieces of Paper Keeping Global Trade Afloat – Archie Hunter

They are relatively easy to fake. Frequently get lost. And can add huge amounts of time to any journey. Yet paper documents still rule in the $25 trillion global cargo trade with four billion of them in circulation at any one time.

It is a system that has barely changed since the nineteenth century. But that dependence on bits of paper being flown from one party to another has become a vulnerability for companies which move and finance the world’s resources around the globe.

In one high profile case, banks including ING Groep NV discovered in 2020 that they had been given falsified bills of lading — shipping documents that designate a cargo’s details and assign ownership — in return for issuing credit to Singapore’s Agritrade Resources. In another dispute, HSBC Holdings Plc and other banks have spent three years in legal wrangling to recover around $3.5 billion from collapsed fuel trader Hin Leong, which is accused by prosecutors of using “forged or fabricated documentation,” when applying for credit.

The International Chamber of Commerce estimates that at least 1% of transactions in the global trade financing market, or around $50 billion per year, are fraudulent. Banks, traders and other parties have lost at least $9 billion through falsified documents in the commodities industry alone over the past decade, according to data compiled by Bloomberg…

…Less than 2% of global trade is transacted via digital means, but that is set to change. Of the world’s top 10 container shipping lines, nine — which account for over 70% of global container freight — have committed to digitizing 50% of their bills of lading within five years, and 100% by 2030. Some of the world’s biggest mining companies including BHP Group Ltd., Rio Tinto Group, Vale SA and Anglo American Plc have voiced their support for a similar campaign in the bulk shipping industry.

The greatest barrier to that expansion has been legal. Banks, traders, insurers and shipping companies have had the means to go digital, but up to now a paper bill of lading has been the only document recognized by English law that gives the holder title ownership to a cargo. A bank or insurer won’t cover a deal that isn’t legally secure, and without financing, deals are unlikely to happen.

To address that, the UK passed the Electronic Trade Documents Act in July which enshrines digital documents with the same legal powers as paper ones. English law on trade documents goes back centuries. It underpins around 90% of global commodities and other trade contracts. So the UK law change represents a big step. Singapore, another center for maritime law, created a similar legal framework in 2021 conducting its first electronic bill of lading transactions in 2022. Similar legislation is expected in France later this year.

The next challenge will be getting companies to change processes that have been in place for hundreds of years. For all its faults, paper is something that everyone understands and while businesses are happy to join a critical mass of digital trade, few are keen to be the first to take steps in that direction…

…For now, when a cargo of coffee is shipped from Brazil to a roaster like Illycaffe SpA in Europe it sets off a flurry of printing. Three identical bills of lading need to be produced and gradually make their way between sellers, banks and buyers, stopping off at law firms and consultants in order to guarantee the rights to the cargo across its 20 day journey. There are also paper invoices, certificates of analysis, and additional documents to measure weight, origin, packing, and moisture content if it is ores that are being shipped.

It is impossible to accurately calculate how many documents are printed for a given trade route but Brazil exports over 900,000 tons of coffee to the European Union every year. And that represents a lot of paper — McKinsey estimated that at least 28,000 trees a year could be saved by reduced friction in the container trade.

As well as providing details of the cargo, its destination and origin, the documents give the holder ownership rights over whatever is being shipped, crucial for holding transport companies accountable for any damages or loss that might occur, or indeed to give banks and insurers some security when providing hundreds of millions of dollars to finance a single shipment…

…The multi-step process begins when an agent prepares the bill of lading and receives sign-off that all the details are correct from the seller, ship owner, trader and end-buyer. The ship is then loaded and original bills of lading are issued by the vessel’s owner and signed by its captain. The three original bills of lading are then released to the seller — in the Brazilian example, the coffee producer — which passes them on to its financing bank along with additional documents to receive payment. The coffee company’s bank will endorse the bill of lading by writing on the back of it.

In many cases a carrier will need to set sail before this process is complete, so the vessel’s captain will provide a shipping agent with a letter of authority to complete the documents on their behalf.

The next leg of the journey for the bill of lading is from the coffee company’s bank to the trading group’s equivalent via DHL Worldwide Express or FedEx Corp. The trader’s bank then makes the payment to the producer’s bank against the receipt of those documents. Assuming everything is okay at this stage the bank working for the trader endorses the bill of lading, signs, stamps, dates and delivers it to its counterpart representing the buyer of the cargo, which in turn pays the trader’s bank for the goods and hands the bill of lading to the master at the destination port to obtain the release of goods.

This pass-the-parcel style approach to the bureaucracy is happening in parallel with the physical goods being loaded, shipped around the world and delivered. Sometimes the documents may only need to move between a small cluster of offices in Geneva or Singapore — where companies across the supply and finance chain have set up offices to be close to one another. But often they are far more tortuous…

…Digital startups like Vakt and ICE Digital Trade offer the opportunity to transfer trade and other financial documents electronically. Bolero has been doing it since the 1990s. Oil majors, such as BP Plc and Shell Plc, traders like Gunvor Group and banks including Société Générale SA have stakes in Vakt, while Intercontinental Exchange bought essDOCS last year for an undisclosed sum, betting that the move online will accelerate.

But the lack of public examples highlights the uphill battle to full adoption of electronic bills of lading. Trafigura used essDOCS for an Australian iron ore shipment back in 2014. Taiwanese shipping line Wan Hai used Bolero’s electronic bill of lading for a polyester filament trade to China in 2018.

This low take-up is largely due to the continued lack of legal recognition in many jurisdictions and banks — which finance the cargoes in transit — but will not accept a digital bill of lading as collateral in most cases. Advocates say the UK law reform should change that.

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 have a vested interest in Apple, Microsoft, and Netflix. Holdings are subject to change at any time.