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 16 June 2024:
1. Saying Goodbye: 30 Investing Lessons After 19% CAGR Over 7 Years – Eugene Ng
I had a near-death/paralysis accident over 10 years ago where I broke my neck. Thankfully, I survived it, but my neck still remains broken to this very day. Life is extremely precious, and I want to live my remaining life to the fullest, and positively impact as many people as I can…
…With a degree in economics and finance, and despite working in banking for over 11 years, I was ill-equipped from the onset to invest well. I decided to start from first principles, asking basic questions? What are stocks? Which they are part ownership stakes in business. Why stock prices rise, and eventually how much?
Eventually I came to realise that growth of revenues, profits and free cash flows matter the most over 5-10 years and beyond, not changes in valuation multiples. That’s why my favourite investing saying is where revenues, profits and free cash flows flow, the stock price eventually goes.
Could investing in this stock generate sufficient returns? Once you take the red pill, once the eyes see what truly matters, you can no longer un-see…
…Most investors are focused in not making errors of commission, or a Type I error, which is making a bad investment when you think it is a good one.
Instead, I am focused making less errors of omission, or Type II errors, rejecting a good investment when I think it is a bad one. Because the maximum a loser can lose is theoretically limited at 100%, but the maximum upside a missed winner can go higher is theoretically infinite…
…Ultimately, your investing strategy and style is unique to you. It must be comfortable to you, it must suit your personality and your strengths. Everyone’s investment portfolio is going to look different.
Most importantly, you must be able to sleep well at night. After some time, you will come to realise if your strategy is truly repeatable and scalable over the long-term…
…Investing in stocks is investing in businesses, and having some of the best CEOs running some of the best companies in the world with their employees working for you 24/7. When you view it that way, it changes your perspective in life…
…Wanted to share a personal story where we recently had a pair of olive-backed sunbirds building their hanging nest on our olive tree, at our balcony in our home in Singapore. We were delighted to welcome them to our home. It was an untidy nest, and our balcony floor was littered with fallen nest materials, but we didn’t mind.
Eggs have been laid, and the female sunbird has been incubating on and off during the day and full time at night over the last week. We are looking forward to see the eggs hatch in the coming week, hear the chicks chirp for the first time, watch them get older and fledge, and then get ready to take flight and leave the nest.
It was amazing to see how timely and beautiful this was, as it reminded me deeply of the journey that I am going to embark on with a new beginning.
2. A Revolution in Biology – Kasra
Our conventional picture of biology is that everything happens in a bottom-up manner: molecular mechanisms dictate the functions of cells, which dictate the functions of your organs and which ultimately control your body. What is the thing at the very bottom of this hierarchy—the foundation for everything else in life? The genome. Genes are considered the fundamental code of life, so when it comes to figuring out questions of how the body develops, or how to cure diseases or change specific biological traits, we tend to look there…
…That is, until Michael Levin (and many others) entered the scene. They came in and said: genes are great, and they do contain much of the necessary information for building our bodies. But they don’t contain all of it, and they are not always a useful level of abstraction of understanding how the body develops, and consequently they are not always the best way to intervene with biology (e.g. to regenerate damaged organs, or to cure diseases like cancer). If you’ve ever done any programming, you know that there are many levels of abstraction—higher-level and lower-level programming languages, higher-level and lower-level API’s—at which you can try to understand or manipulate the software that runs in your computer. Levin’s point is that genes are like machine code, and modern-day programmers never think about machine code—they think about higher-level software constructs like objects, modules, and applications. The bold claim embedded in his work—the real revolution here—is that higher levels of abstraction and control meaningfully exist in biology. And one of the ways in which this higher level of abstraction manifests is in something called the bioelectric network of the organism.
We usually think of neurons as the only cells in our body that produce intelligent behavior by communicating in large networks. Neurons are constantly communicating with each other in the form of electrical patterns on their membrane and neurotransmitters, which are chemicals that transfer messages between cells. But it turns out that cells throughout the body have the exact same building blocks for such communication. They do the same communication, but slower. Levin and company call this the bioelectric network, as distinguished from a neural network.
In the past few decades we’ve discovered all the ways in which bioelectric networks distributed through the body do the same kinds of things that brains do: store memories, solve problems, and guide development. To get a sense of the bioelectric network in action, we have to talk about a mind-blowing creature called the planarian. This little critter (about 2cm in length) is a developmental “genius” of sorts: it doesn’t age, it doesn’t get cancer, and it is extremely regenerative, capable of regenerating any part of its body that gets cut off, even if it’s cut up into more than 250 pieces…
…Imagine taking one of these worms and splitting it into two. You now have two half-worms, and each of those half-worms is tasked with rebuilding the rest of its body. There’s a crucial decision here that the cells have to make: what part of the body do we already have, and what part do we need to build? One of the half-worms needs to produce a tail, and the other half-worm needs to produce a head. But the cells are at the very middle of the body, extremely far (from a cell’s perspective) from both the head and the tail. How do the cells have any idea what they should generate?
The answer, at least in part, is that all along the body the cells of the worm have a gradient of “resting membrane potentials”, which is effectively a stable electrical state. The cells keep track of their “position” in the body in this way, and experiments have demonstrated that the cell’s electrical state relative to the rest of the body is what determines whether it will proliferate into a head or a tail…
…Levin’s team was able to induce the worm to generate two heads instead of one head, by putting it into a solution of drugs that blocked specific ion channels (which in turn altered the electrical state of the cells). They’ve also induced the worm to generate no heads at all, or to generate the head of a different worm species. All of these are living, functional worms, just with a very different body structure…
…Keep in mind a crucial point: in all these experiments, the genes of the worms are never edited. You get a wildly different functional worm with the same genes. And what’s even wilder is that some of these changes are enduring: without any further drugs or modifications, the two-headed worm produces offspring that are also two-headed, indefinitely…
…Levin’s lab and others have already demonstrated an astonishing level of control over development by modulating bioelectric networks. They’ve done things like getting frogs to develop extra limbs, and getting them to develop an eye in their gut, or an eye in their tail that they can actually see out of. The end goal that Levin dreams of is an “anatomical compiler” – a program which takes as input a specification for an arbitrary organ or body plan, and outputs the specific set of chemical and electrical signals needed to generate that organ. Imagine 3-d printing entire synthetic organs and organisms, except instead of having to specify all the micro-level details, you can just give a high-level description like “an extra eye at the tail.” This is Dall-E but for biology. And in the very long run, it could be the answer to virtually all of biomedicine, including traumatic injury, birth defects, degenerative disease, cancer, and aging.
3. The Investing Boom That’s Squeezing Some People Dry – Jason Zweig
The idea is that when you lock your money up for months or years, you’re less likely to panic in a downturn, enabling the managers to amass a portfolio that will pay off in the long run…
…That bumps up against a basic law of financial physics: Eliminating one risk creates another.
An investment that doesn’t trade may have some advantages, but once you buy it, how do you sell it? How deep a haircut, or discount from the reported price, will you take?
Many funds have so far been able to cash out investors at what seems like a fair price. Many haven’t…
…Highlands REIT, a private Chicago-based real-estate fund, is a more-extreme case. The company bought back about 19% of its stock in December at 14 cents a share. For the sellers, that was like getting a haircut with a lawn mower: Highlands’ annual report estimates net asset value at 32 cents per share as of Dec. 15, 2023.
Outsiders are offering an even harsher haircut. On May 20, MacKenzie Capital Management, an investment firm in Orinda, Calif., opened a mini-tender for Highlands’ stock at 4 cents a share, minus a $25 transfer fee. On Lodas, the latest sale was at 10 cents…
…Institutions can sell big blocks of their alternatives, like hedge funds or private equity, to what are called secondary funds at discounts that might run 10% to 30% below net asset value.
In many cases, you should be so lucky.
Often, if you can find a broker willing to buy your alternative investment, the commission can run up to or even exceed 5%. Your haircut could be as deep as 30% to 50%. Depending on the buyer, weeks may go by before you get paid.
Other electronic marketplaces besides Lodas, including Central Trade & Transfer and 1st Trade, also match buyers and sellers of alternatives—typically at gaping discounts to net asset value.
4. Book Summary Part 2: “Our Investing Strategy, who does the market smile upon” – Made In Japan
Right before launching his fund, Hokkaido Takushoku Bank went bankrupt and was undergoing liquidation. He immediately decided to use that opportunity. He went to Sapporo to buy the shares of a specific company from them, which was Nitori a company with almost zero liquidity at the time. Some readers may recognize the name today as the largest furniture retail chain in Japan oft compared to Ikea. They’re known for their value-for-money proposition, providing quality products at an affordable price point, and has been a huge success.
You might not believe this if you look at Nitori’s stock price today but it was an unpopular company back then. According to Kiyohara-san, it was trading at 750 Yen per share at the time. One of the main reasons it seems, was that the furniture market was in decline, making it an unattractive industry to invest in. His thesis was that the market was extremely fragmented. The largest furniture retailer Ootsuka, only had a 5% market share. Nitori was the only vertically integrated manufacturer (others were distributors) and believed this could help them gain share as a cost-effective producer of home furnishings. Nitori was listed on the Sapporo Exchange so no institutional investor would touch it (since it would be impossible to sell). However, when he spoke to IR, he picked up on a key insight. While the Hokkaido economy, which was their main market, was not doing well and they saw a decline in same-store sales in the region, the 3 stores open in Kanto were doing very well. Providing a hint to Nitori’s true competitiveness.
And it’s funny because you can immediately tell he was built differently. After the research was done and when the fund launched he bought as much as he could from the failing bank and at launch it became 25% of his NAV. The stock tripled in a year and in 5 years the stock was a six-bagger. A year later it was a ten-bagger at which point he sold out. If he had held it till now stock would been a hundred bagger. But by 2003 Nitori was starting to get more institutional coverage and attention. He believed it was time to exit when. He says “When the party starts, that’s when we go home.”
So here was the first lesson, which is that investing in an unpopular, shrinking market can still make you a lot of money. In fact, during the time he owned Nitori the market size halved. He also understood the opportunity to buy shares from distressed sellers, especially for stocks that are listed on some regional exchange that no one looks at…
…2007 Dec – 2009 Feb: “A sick patient getting hit by a truck, 3 times”
Just as the fund narrowly escaped its “matasaki”, it was followed by the 2008 crisis.
Whilst K1J Fund generated incredible returns from their bet in REITs and Real Estate and successfully exited from these. He still owned a lot of cheap real estate stocks in the fund. 3 holdings filed for bankruptcy and 1 went through an Alternate Dispute Resolution (ADR) The worse part? he owned 45%, 35%, 10% and 20% of the shares outstanding.
Needless to say, it was distressing and he lost weight.
The goal was no longer for him to generate returns in this period. It was simply to survive.
He never said this himself, but what follows is what you call an absolute shitshow. Or as he would put it, “like a sick patient getting hit by a truck 3 times”.
The fund’s top priority was to reduce its leveraged long and short positions to avoid a margin call.
But to add insult to injury, their prime broker Goldman decided to change its margin policy to save themselves. (from 50% to 30%) Which could have been fatal for the fund. Fortunately, Goldman eventually agreed to only implement this in steps, which helped the fund bide some time.
The issue is that in a crisis like this it’s not just one kind of risk that materializes, there are second-order and third-order effects which, in isolation might have a low probability. I believe, however, the odds of secondary and tertiary events no matter how unlikely will increase when the first ‘highly improbable event’ occurs. (You can also apply this to the Livedoor example).
Although not a surprise, the clients that entered in excitement when the fund was killing it in 2005 started redeeming (mainly pensions) and the fund lost half of its clients.
This created a new risk which forced him to reduce his longs which were mainly in small, illiquid companies. A forced selling driven by client redemptions would in effect make you dig your own grave.
So how does he try to solve this problem? He asks these companies to buy back their shares.
From its peak in October 2005 to its trough in February 2009 the fund’s NAV was -72% and its AUM -89%.
This is when you realize most people won’t be able to replicate what he did. I wrote this in part 1. He decides to put almost all of his net worth in the fund to try and save it. He adds “Because that is the responsibility of the manager”. Like a captain being the last to leave a sinking ship, an honorable and brave decision.
I want to reflect here because this is not something most of us could do. It’s really easy to read this as a brave story and just say “wow, awesome”, but never really understand the extent of how hard it was. (This is called the empathy gap in psychology where we underestimate our psychology to make decisions in a certain situation). If your fund is already down heavily, you have clients threatening to leave or have left, your prime broker changing the rules, and you’re being forced to exit your positions at ridiculous valuations, are you ready to risk going broke to save it? Remember your morale at this point is probably at an all-time low. In a world where limited liability corporations are the norm (i.e. the damage to your personal wealth can be legally limited where, at the very worst moment most of us would use to escape) he decided to go all in.
Also don’t forget, he’s had to tell his wife he did just that! (which might’ve been the scariest part!). Apparently, her response to him telling her was “Didn’t you also say that last week?” lol.
But the question begs, why did he do that? His confidence was far from crushed, and he was convinced if he closed his shorts and be as long as possible, that he would make alot of money. Why? because he knew a sudden decline will almost always result in a V-shape recovery. His game was to just survive until then. That is SOME confidence he had.
What’s amazing is that he went to clients telling them it would be foolish to leave now, “the fund can probably double from here”.
In the end, from its trough through Feb 2018 his fund 12x ed…
…Shorts are the most dangerous in a bear market, in this scenario, his game was to maximize his long positions. Maintaining a short book means, your prime broker will usually give you a hard time in these moments and pssibly reduce your margin which also limits your long exposure. The other is that when the market turns and your shorts also move up, this might also force you to reduce your long position (to cover). Understanding this helped him avoid a forced error of omission. Imagine having no choice but to sell your longs which could have multiplied but you were forced to sell them after a little move up to cover your shorts…
…Lasertec (Circa 2020)
- This was not a fundamental idea, though it did fit the typical target for his shorts: expensive-looking large-cap.
- He simply saw an opportunity through the lens of Japan’s inherent tax rules.
- The fourth largest shareholder was the widow of the founder who owned 4.24%.
- So he thought, what happens if she passes away too?
- Japan’s inheritance tax is the highest in the world, and her children will have to pay for it by selling shares.
- In the end, this is really what happened.
This is an important theme for owner-operated businesses, in which inheritance can play an outsized impact on the stock price.
5. An Interview with AMD CEO Lisa Su About Solving Hard Problems
– Ben Thompson and Lisa Su
What was your response in November 2022 when ChatGPT shows up?
LS: Well, it was really the crystallization of what AI is all about.
Obviously you’ve been in the graphics game for a long time, you’ve been thinking about high-performance computing, so the idea that GPUs would be important was not foreign to you. But were you surprised the extent to which it changed the perception of everyone else around you and what happened after that?
LS: We were very much on this path of GPUs for high-performance computing and AI. Actually, it was probably a very significant arc that we started, let’s call it back in the 2017 plus timeframe. We’ve always been in GPUs, but really focusing on-
What was it in 2017 that made you realize that, “Wait, we have these, we thought we bought ATI for gaming, suddenly, there’s this completely different application”?
LS: It was the next big opportunity, we knew it was the next big opportunity. It was something that Mark and I discussed, which was, by putting CPUs and GPUs together in systems and designing them together, we’re going to get a better answer and the first near-term applications were around super-computing. We were very focused on these large machines that would reside at national laboratories and deep research facilities and we knew that we could build these massively parallel GPU machines to do that. The AI portion, we always also thought about it as clearly a HPC plus AI play.
You said before that AI is the killer application for HPC.
LS: Yes.
But you will talk to people in HPC, they’re like, “Well, it’s a little bit different”, to what extent is that the same category versus adjacent categories?
LS: It’s adjacent but highly-related categories, and it all depends on the accuracy that you want in your calculations, whether you’re using the full accuracy or you want to use some of these other data formats. But I think the real key though, and the thing that really we had good foresight on is, because of our chiplet strategy, we could build a highly modular system that could be, let’s call it, an integrated CPU and GPU, or it could be just incredible GPU capability that people needed.
And so, the ChatGPT moment for me was the clarity around, now everybody knew what AI was for. Before, it was only the scientists and the engineers who thought about AI, now everybody could use AI. These models are not perfect, but they’re amazingly good, and with that, I think the clarity around how do we get more AI compute in people’s hands as soon as possible was clear. Because of the way we had built our design system, we could really have two flavors. We had HPC-only flavor, which is what we would call our MI300A and we had AI only flavor, which was the MI300X…
…One of the things that does strike me about the contrast is, and one of Nvidia’s really brilliant moves was the acquisition of Mellanox and their portfolio in networking, and to the extent it matters to tie all these chips together, particularly for training.
In your Computex keynote, you talked about the new Ultra Accelerator Link and Ultra Ethernet Link standards, and this idea of bringing lots of companies together, kind of calling back to the Open Compute Project back in the day as far as data centers. Makes perfect sense, particularly given Nvidia’s proprietary solutions have the same high margins, we all know and love, as the rest of their products.
But I guess this is my question about your long-term run — do you think it’s fair to say that, from a theoretical Clayton Christensen perspective, because we’re early in AI, maybe it’s not a surprise, the more proprietary integrated solution is the belle of the ball in many respects? There’s a bit where, yes, being open and modular all makes sense, but maybe that’s not going to be good enough for a while.
LS: I would say it this way. When you look at what the market will look like five years from now, what I see is a world where you have multiple solutions. I’m not a believer in one-size-fits-all, and from that standpoint, the beauty of open and modular is that you are able to, I don’t want to use the word customize here because they may not all be custom, but you are able to tailor.
Customize in the broad sense.
LS: That’s right.
Tailor is a good word.
LS: Tailor is the right word — you are able to tailor the solutions for different workloads, and my belief is that there’s no one company who’s going to come up with every possible solution for every possible workload. So, I think we’re going to get there in different ways.
By the way, I am a big believer that these big GPUs that we’re going to build are going to continue to be the center of the universe for a while, and yes, you’re going to need the entire network system and reference system together. The point of what we’re doing is, all of those pieces are going to be in reference architectures going forward, so I think architecturally that’s going to be very important.
My only point is, there is no one size that’s going to fit all and so the modularity and the openness will allow the ecosystem to innovate in the places that they want to innovate. The solution that you want for hyperscaler 1 may not be the same as a solution you want for hyperscaler 2, or 3.
Where do you think the balance is going to be then, between there being a standard approach versus, “This is the Microsoft approach”, “This is the Meta approach”? There’s some commonality there, but it is actually fairly customized to their use cases and needs. Again, not next year, but in the long run.
LS: I think as you get out three, four or five years, I think you’re going to see more tailoring for different workloads, and what happens is, the algorithms are going to — right now, we’re going through a period of time where the algorithms are just changing so, so quickly. At some point, you’re going to get to the place where, “Hey, it’s a bit more stable, it’s a little bit more clear”, and at the types of volumes that we’re talking about, there is significant benefit you can get not just from a cost standpoint, but from a power standpoint. People talk about chip efficiency, system efficiency now being as important if not more important than performance, and for all of those reasons, I think you’re going to see multiple solutions…
…How much inference do you see actually going back to the CPU?
LS: I think a good amount of inference will be done on the CPU, and even as you think about what we’re talking about is the very large models obviously need to be on GPUs, but how many companies can really afford to be on the largest of models? And so, you can see now already that for smaller models, they’re more fine-tuning for those kinds of things, the CPU is quite capable of it, and especially if you go to the edge.
Right. You noted on the last earnings call that the MI300, it’s been supply-constrained, your fastest ramp ever, but is maybe from the expectations of some investors, a little disappointing in the projections for the end of the year. How much do you feel that shift to being demand-constrained is about the 325 coming along, which you talked about this week, versus the fact that just generally Nvidia supply has gone up, as everyone’s trying to figure this stuff out? Yes, your long-term opportunity is being this sort of customized supplier — tailored supplier, sorry, is the word that we’re going for — versus, “Look, I don’t want to say picking up but just we need GPUs, we’ll buy them from anyone”. Where do you feel your demand curves are relative to the competition and the rapid progression of the space?
LS: Again, let me take a step back and make sure we frame the conversation. The demand for AI compute has been off the charts, I think nobody would have predicted this type of demand, and so when I say that there is tightness in the supply chain, that’s to be expected, because nobody expected that you would need this many GPUs in this timeframe. The fact is the semiconductor industry is really good at building capacity, and so that is really what we’ve seen. As we’ve started to forecast-
And so you feel it’s more a function of there’s just so much supply coming online?
LS: Absolutely, and that’s our job. Our job is to make it to a place where you’re not constrained by manufacturing capacity.
Really, for us, it is about ensuring that customers are really ramping their workloads and that is a lot of deep work, deep partnerships that we’re doing with our customers. So honestly, I feel really good about the opportunities here. We’ve been through this before where it’s very similar to what we saw when we did the initial data center server CPU ramps, which is our customers work very closely with us, they get their software optimized, and then they add new workloads, and add more volumes, and that’s what I would expect to happen here, too.
The difference in AI is that I think customers are willing to take more risk, because there’s a desire to get as much, as fast as possible.
Is there a challenge for you, because that desire to take more risks means they’re more accepting of say, high margins to get the leading GPUs or whatever it might be, or the GPU with the largest ecosystem, developer ecosystem?
LS: What I will say is I’m super happy with the progress we’ve made on software.
Fair enough.
LS: What we’re seeing is excellent out-of-box performance. The fact is things just run, the fact is that much of the developer ecosystem wants to move up the abstraction layer, because everybody wants choice.
And you feel you’re going to get to a stage where that move up the abstraction layer is a common layer across companies, as opposed to getting one company internally moves up the abstraction layer, and so they can buy any CPU, but that doesn’t necessarily benefit you going into another company, or do you feel that’s going to be-
LS: I absolutely believe that it’ll be across the industry. Things like PyTorch, I think PyTorch is extremely widely adopted, OpenAI Triton, similar. These are larger industry things where frankly, part of the desire is it takes a long time to program down to the hardware. Everyone wants to innovate quickly, and so the abstraction layer is good from the standpoint of just rapid innovation.
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