I’ve long been sector-agnostic when it comes to the companies I’m interested in because I believe that great companies – and thus, great stocks – can come from anywhere. 

My belief was formed because of something I learnt more than a dozen years ago about the US-based airline, Southwest Airlines. Ned Davis Research was tasked by CNN’s MONEY Magazine in 2002 to find the five US-listed stocks with the highest returns over the past 30 years. The winner was Southwest Airlines with its annualised return of 26% from 1972 to 2002; a $1,000 investment at the start of the period would have become $1 million by the end. What is noteworthy here is airlines were widely regarded back then as businesses with horrendous economics. In Berkshire Hathaway’s 2007 annual shareholders’ letter, Warren Buffett wrote (emphasis is mine): 

“The worst sort of business is one that grows rapidly, requires significant capital to engender the growth, and then earns little or no money. Think airlines. Here a durable competitive advantage has proven elusive ever since the days of the Wright Brothers. Indeed, if a farsighted capitalist had been present at Kitty Hawk, he would have done his successors a huge favor by shooting Orville down.

The airline industry’s demand for capital ever since that first flight has been insatiable. Investors have poured money into a bottomless pit, attracted by growth when they should have been repelled by it. And I, to my shame, participated in this foolishness when I had Berkshire buy U.S. Air preferred stock in 1989. As the ink was drying on our check, the company went into a tailspin, and before long our preferred dividend was no longer being paid. But we then got very lucky. In one of the recurrent, but always misguided, bursts of optimism for airlines, we were actually able to sell our shares in 1998 for a hefty gain. In the decade following our sale, the company went bankrupt. Twice.” 

And yet, it was an airline that topped the charts in 2002 for the best-performing US stock in the past 30 years. The timeframe of 30 years is also sufficiently long, such that Southwest Airlines’ gains had to be the result of its business’s excellent long-term performance, and not some fortunate short-term hiccup in the fortunes of its business or its stock price.

A recent study from the highly-regarded investment researcher Michael Maubossin, titled Measuring the Moat: Assessing the Magnitude and Sustainability of Value Creation, bolsters my belief. He found that differences in the return on invested capital (ROIC) between industries is lower than the differences in ROICs of companies within industries. In Mauboussin’s data-set, the industry with the highest median ROIC from 1963 to 2023 is Personal Care Products at around 18%. But within Personal Care Products, the companies have ROICs ranging from a low of around 5% to a high of around 40%. Meanwhile, the Wireless Telecom Services industry has one of the lowest median ROICs at around 1%. Yet, the companies within have ROICs ranging from just below 40% to deeply negative figures. Said another way, the best company in a poor industry (Wireless Telecom Services) still has an excellent business that performs significantly better than the median company in a great industry (Personal Care Products). 

I continue to believe that excellent investing opportunities can be found everywhere, so I will, for the foreseeable future, remain sector-agnostic. Sometimes, a gleaming diamond can be found amongst a lump of coal for those with the ability to spot a true bargain.

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Disclaimer: The Good Investors is the personal investing blog of two simple guys who are passionate about educating Singaporeans about stock market investing. By using this Site, you specifically agree that none of the information provided constitutes financial, investment, or other professional advice. It is only intended to provide education. Speak with a professional before making important decisions about your money, your professional life, or even your personal life. I currently have no vested interest in any company 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 12 January 2025:

1. The art of outlasting: What we can learn from timeproof Japanese businesses – Eric Markowitz

Japan is home to an extraordinary number of shinise, or long-established businesses. A 2008 study found that Japan had over 21,000 companies older than 100 years, including more than 3,000 that had crossed the 200-year mark. These firms are not just historical artifacts — they are vibrant examples of how to endure and thrive in a rapidly changing world. Their strategies — balancing tradition with adaptability, patience with practicality — are a masterclass in long-term thinking that today’s entrepreneurs and executives would be wise to study…

…What ties these stories together is an approach to business that’s almost rebellious in its patience. While the modern world glorifies disruption and speed, Japan’s ancient companies remind us that longevity is often about playing the long game. It’s about building something so solid, so aligned with its environment, that it can weather any storm. But let’s not romanticize this too much. Strip away the poetry of water metaphors and ancient traditions, and you’ll find ruthless pragmatism at the core of these businesses’ survival.

When Japan’s post-war construction boom faded, Kongo Gumi didn’t just stick to temples — they pivoted hard into office buildings and apartments while maintaining their temple maintenance business as a hedge. During the lean years of the 1990s recession, Hōshi Ryokan cut costs to the bone while refusing to lay off staff, with family members taking deep pay cuts to keep their centuries-old workforce intact. Okaya transformed from selling samurai swords to becoming a global steel trader, making calculated bets on new technologies and markets while keeping their supply chain relationships rock solid.

These companies didn’t just drift through history — they clawed their way through wars, depressions, and cultural upheavals, making brutal choices about what to preserve and what to sacrifice. Their longevity wasn’t achieved through Zen-like detachment, but through gritted teeth and white-knuckled adaptability.

2. Notes on China – Dwarkesh Patel

I got quite mixed messages about the state of public opinion in China. This is to be expected in a society where you can’t establish common knowledge. One person told me that the new generation is quite nationalist, unlike the older reform generation which personally experienced the catastrophes of Mao and the tangible benefits of liberalization. He made the rather insightful point that this tilt in Chinese public opinion increasingly gives lie to the American talking point, “We’re against the CCP, not the Chinese people.” In fact, he went on to say that the current regime is way more liberal than what would result from an election in China.

Another person told me that these Chinese nationalists were only a vocal minority, similar to the wokes in America circa 2020. While they make up only about 10% of the population, they aggressively shout down others on Weibo (China’s Twitter equivalent). Most people find them annoying but feel uncomfortable confronting them directly. This matches what a student who graduated from a top university there told me – the vast majority of his classmates are simply apolitical. And in our own interactions with locals, we saw little evidence of widespread nationalism. In fact, when my Chinese-speaking trip mate (who could actually speak Chinese) would mention he was from the UK to taxi drivers, they would often respond enthusiastically: “Oh wonderful, we love the UK!”…

…We chatted up quite a lot of young people on night life streets. I was struck by how many young people expressed feeling stressed or overwhelmed. We met a musician in Chengdu who was writing songs about youth anxiety. We chatted up some modeling school students – even they complained about the intense pressure they felt. We met a guy who had studied in Australia but returned to China during COVID. He explained that many of his friends with prestigious degrees are moving away from Shanghai and Beijing – Yes, the pay there can be twice as high as in second or third tier cities. But the competitiveness is insane. And in order to actually land the high skilled positions, they have to work truly insane hours (9-9-6 is not a myth). He said that many of his friends were opting for these less ambitious lower-paying careers in smaller cities, where the rent is lower and the pressure is manageable…

…I’m still puzzled by how China can have both a demographic collapse and massive youth unemployment. You’d think with fewer young people being born, the ones who are around would be in high demand. One explanation I heard while there is that there are plenty of menial jobs available, but today’s educated youth – who’ve gone through high school and college – just won’t take the low-skilled positions their parents and grandparents did. Meanwhile, there’s a real shortage of the high-skilled jobs that would actually match their education and aspirations. It’s a mismatch between the jobs available and the jobs young people feel qualified for and willing to do…

…The biggest surprise from talking to Chinese VCs people at AI labs was how capital constrained they felt. Moonshot AI, one of China’s leading AI labs, raised $1 billion at a $3 billion valuation. Meanwhile, just xAI’s new cluster alone will cost $3-4 billion.

The tech ecosystem feels quite shell shocked from the 2021 crackdown. One VC half-jokingly asked if I could help him get his money out of China. If you keep your money in China, you’re basically stuck choosing between terrible options. You can either accept a measly 2% yield from state banks, or throw it into China’s perpetually struggling stock market. This helps explain why valuations for Chinese companies are chronically low – the exit opportunities just suck. Even if you build (or invest in) something great, there’s no guarantee the company will be able to raise the next round. And even if you do raise again and succeed, the government might randomly cancel your IPO. And even if you somehow make it to the public markets, Chinese equities have been performing terribly anyways. It’s a good reminder of how easy it is to completely wreck an innovation ecosystem that depends on risk-taking investors.

3. Is AI progress slowing down? – Arvind Narayanan and Sayash Kapoor

To be clear, there is no reason to doubt the reports saying that many AI labs have conducted larger training runs and yet not released the resulting models. But it is less clear what to conclude from it. Some possible reasons why bigger models haven’t been released include:

• Technical difficulties, such as convergence failures or complications in achieving fault tolerance in multi-datacenter training runs.
• The model was not much better than GPT-4 class models, and so would be too underwhelming to release.
• The model was not much better than GPT-4 class models, and so the developer has been spending a long time trying to eke out better performance through fine tuning.

To summarize, it’s possible that model scaling has indeed reached its limit, but it’s also possible that these hiccups are temporary and eventually one of the companies will find ways to overcome them, such as by fixing any technical difficulties and/or finding new data sources…

…Industry leaders don’t have a good track record of predicting AI developments. A good example is the overoptimism about self-driving cars for most of the last decade. (Autonomous driving is finally real, though Level 5 — full automation — doesn’t exist yet.) As an aside, in order to better understand the track record of insider predictions, it would be interesting to conduct a systematic analysis of all predictions about AI made in the last 10 years by prominent industry insiders.

There are some reasons why we might want to give more weight to insiders’ claims, but also important reasons to give less weight to them. Let’s analyze these one by one. It is true that industry insiders have proprietary information (such as the performance of as-yet-unreleased models) that might make their claims about the future more accurate. But given how many AI companies are close to the state of the art, including some that openly release model weights and share scientific insights, datasets, and other artifacts, we’re talking about an advantage of at most a few months, which is minor in the context of, say, 3-year forecasts.

Besides, we tend to overestimate how much additional information companies have on the inside — whether in terms of capability or (especially) in terms of safety. Insiders warned for a long time that “if only you know what we know…” but when whistleblowers finally came forward, it turns out that they were mostly relying on the same kind of speculation that everyone else does.

Another potential reason to give more weight to insiders is their technical expertise. We don’t think this is a strong reason: there is just as much AI expertise in academia as in industry. More importantly, deep technical expertise isn’t that important to support the kind of crude trend extrapolation that goes into AI forecasts. Nor is technical expertise enough — business and social factors play at least as big a role in determining the course of AI. In the case of self-driving cars, one such factor is the extent to which societies tolerate public roads being used for experimentation. In the case of large AI models, we’ve argued before that the most important factor is whether scaling will make business sense, not whether it is technically feasible…

…As an example, Sutskever had an incentive to talk up scaling when he was at OpenAI and the company needed to raise money. But now that he heads the startup Safe Superintelligence, he needs to convince investors that it can compete with OpenAI, Anthropic, Google, and others, despite having access to much less capital. Perhaps that is why he is now talking about running out of data for pre-training, as if it were some epiphany and not an endlessly repeated point.

To reiterate, we don’t know if model scaling has ended or not. But the industry’s sudden about-face has been so brazen that it should leave no doubt that insiders don’t have any kind of crystal ball and are making similar guesses as everyone else, and are further biased by being in a bubble and readily consuming the hype they sell to the world…

…Inference scaling is useful for problems that have clear correct answers, such as coding or mathematical problem solving. In such tasks, at least one of two related things tend to be true. First, symbolic reasoning can improve accuracy. This is something LLMs are bad at due to their statistical nature, but can overcome by using output tokens for reasoning, much like a person using pen and paper to work through a math problem. Second, it is easier to verify correct solutions than to generate them (sometimes aided by external verifiers, such as unit tests for coding or proof checkers for mathematical theorem proving).

In contrast, for tasks such as writing or language translation, it is hard to see how inference scaling can make a big difference, especially if the limitations are due to the training data. For example, if a model works poorly in translating to a low-resource language because it isn’t aware of idiomatic phrases in that language, the model can’t reason its way out of this.

The early evidence we have so far, while spotty, is consistent with this intuition. Focusing on OpenAI o1, it improves compared to state-of-the-art language models such as GPT-4o on coding, math, cybersecurity, planning in toy worlds, and various exams. Improvements in exam performance seem to strongly correlate with the importance of reasoning for answering questions, as opposed to knowledge or creativity: big improvements for math, physics and LSATs, smaller improvements for subjects like biology and econometrics, and negligible improvement for English.

Tasks where o1 doesn’t seem to lead to an improvement include writing, certain cybersecurity tasks (which we explain below), avoiding toxicity, and an interesting set of tasks at which thinking is known to make humans worse…

…We think there are two reasons why agents don’t seem to benefit from reasoning models. Such models require different prompting styles than regular models, and current agentic systems are optimized for prompting regular models. Second, as far as we know, reasoning models so far have not been trained using reinforcement learning in a setting where they receive feedback from the environment — be it code execution, shell interaction, or web search. In other words, their tool use ability is no better than the underlying model before learning to reason…

…The furious debate about whether there is a capability slowdown is ironic, because the link between capability increases and the real-world usefulness of AI is extremely weak. The development of AI-based applications lags far behind the increase of AI capabilities, so even existing AI capabilities remain greatly underutilized. One reason is the capability-reliability gap — even when a certain capability exists, it may not work reliably enough that you can take the human out of the loop and actually automate the task (imagine a food delivery app that only works 80% of the time). And the methods for improving reliability are often application-dependent and distinct from methods for improving capability. That said, reasoning models also seem to exhibit reliability improvements, which is exciting.

Here are a couple of analogies that help illustrate why it might take a decade or more to build products that fully take advantage of even current AI capabilities. The technology behind the internet and the web mostly solidified in the mid-90s. But it took 1-2 more decades to realize the potential of web apps. Or consider this thought-provoking essay that argues that we need to build GUIs for large language models, which will allow interacting with them with far higher bandwidth than through text. From this perspective, the current state of AI-based products is analogous to PCs before the GUI.

4. Waymo still doing better than humans at preventing injuries and property damage – Andrew J. Hawkins

The study is the product of the collaboration between Waymo and insurer Swiss Re, which analyzed liability claims related to collisions from 25.3 million fully autonomous miles driven by Waymo in four cities: Phoenix, San Francisco, Los Angeles, and Austin. They then compared those miles to human driver baselines, which are based on Swiss Re’s data from over 500,000 claims and over 200 billion miles traveled.

They found that the performance of Waymo’s vehicles was safer than that of humans, with an 88 percent reduction in property damage claims and a 92 percent reduction in bodily injury claims. Across 25.3 million miles, Waymo was involved in nine property damage claims and two bodily injury claims. The average human driving a similar distance would be expected to have 78 property damage and 26 bodily injury claims, the company says.

Waymo’s vehicles also performed better when compared to new vehicles equipped with all the latest safety tech, including automatic emergency braking, lane-keep assist, and blind spot detection. When compared to this group, Waymo’s autonomous driving system showed an 86 percent reduction in property damage claims and a 90 percent reduction in bodily injury claims.

5. SITALWeek #454 – Brad Slingerlend

I think we are approaching the point where we can start to estimate the value of AI for developers and the companies/consumers who are going to buy the next wave of innovative applications. I think the salient question for AI (and, frankly, humanity!) is: How much AI reasoning can you get for a human-equivalent salary? In other words, for a certain salary, how much compute power will it take to match or outperform a human (assuming the AI can collaborate with other humans/AIs using the same methods and tools a human would)…

… LLMs are shifting from a pure token-in/token-out model to a test-time scaling model, which may offer us better inroads for estimating costs. Essentially, they are thinking harder before spitting out a reply; thus, rather than just predicting the next words in a response using a probability model (see You Auto-Complete Me), they are doing some deep thinking to arrive at more accurate, useful answers. This is a major leap in capability that comes with a major leap in cost. OpenAI raised prices for their o1 model to $200/mo (Pro subscription) from $20 (Plus subscription). For developers, use of o1’s advanced reasoning API comes at 3-4x the cost of their “general purpose” GPT-4o. If o1 were priced at a typical Western office worker wage of $40/hr, the reasoning of the model would equate to around 5 hours of work per month. We also don’t know if the $200/mo price point is profitable for OpenAI or if they are just relying on Microsoft to further subsidize their business model (which brings us back to the principal-agent problem I started this section off with). So, all of my hand waving here seems to imply you can get a decent amount of human-equivalent reasoning for an amount of money in the realm of human labor cost. If true, after a few more years of advancements in semiconductors and AI models, we should have markedly affordable “human reasoning as a service”, an explosion in demand, and a wide range of outcomes for how much human supervision of AI will be required (it may be that human jobs stay relatively flat, but each human is 2x productive, then 4x, etc.).

Following this logic, at current AI reasoning costs, companies would need to lay off one human for every AI human equivalent they hire and would probably lose more skill/knowledge than they gain. In other words, based on my attempts to guess the cost of replacing human reasoning, today’s AI offerings aren’t likely compelling enough. In a couple years, however, maybe you will be able to lay off one human and hire a handful of AIs, which, by collaborating with each other and humans, may yield superior results. Even today, extremely high-value tasks, such as in-depth research or stock market predictions, may be able to take advantage of the high-cost test-time scaling AI models. And, if any of this math is in the realm of reason, you can easily see that AI may not require such high-value-add applications to be cost effective in the near to medium future. The proof will come within the next couple of years as today’s entrepreneurs develop the next generation of apps leveraging LLMs and overtaking human capabilities: If these apps are at price points that outcompete human employees, a significant wave of change could come much faster to society. 

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Disclaimer: The Good Investors is the personal investing blog of two simple guys who are passionate about educating Singaporeans about stock market investing. By using this Site, you specifically agree that none of the information provided constitutes financial, investment, or other professional advice. It is only intended to provide education. Speak with a professional before making important decisions about your money, your professional life, or even your personal life. We currently have a vested interest in Alphabet (parent of Google and Waymo) and Microsoft. 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 December 2024:

1. Quantum Computers Cross Critical Error Threshold – Ben Brubaker

In the 1990s, researchers worked out the theoretical foundations for a way to overcome these errors, called quantum error correction. The key idea was to coax a cluster of physical qubits to work together as a single high-quality “logical qubit.” The computer would then use many such logical qubits to perform calculations. They’d make that perfect machine by transmuting many faulty components into fewer reliable ones…

…This computational alchemy has its limits. If the physical qubits are too failure-prone, error correction is counterproductive — adding more physical qubits will make the logical qubits worse, not better. But if the error rate goes below a specific threshold, the balance tips: The more physical qubits you add, the more resilient each logical qubit becomes.

Now, in a paper(opens a new tab) published today in Nature, Newman and his colleagues at Google Quantum AI have finally crossed the threshold. They transformed a group of physical qubits into a single logical qubit, then showed that as they added more physical qubits to the group, the logical qubit’s error rate dropped sharply…

…At first, many researchers thought quantum error correction would be impossible. They were proved wrong in the mid-1990s, when researchers devised simple examples of quantum error-correcting codes. But that only changed the prognosis from hopeless to daunting.

When researchers worked out the details, they realized they’d have to get the error rate for every operation on physical qubits below 0.01% — only one in 10,000 could go wrong. And that would just get them to the threshold. They would actually need to go well beyond that — otherwise, the logical qubits’ error rates would decrease excruciatingly slowly as more physical qubits were added, and error correction would never work in practice…

…That variation, called the surface code, is based on two overlapping grids of physical qubits. The ones in the first grid are “data” qubits. These collectively encode a single logical qubit. Those in the second are “measurement” qubits. These allow researchers to snoop for errors indirectly, without disturbing the computation.

This is a lot of qubits. But the surface code has other advantages. Its error-checking scheme is much simpler than those of competing quantum codes. It also only involves interactions between neighboring qubits — the feature that Preskill found so appealing.

In the years that followed, Kitaev, Preskill and a handful of colleagues fleshed out the details(opens a new tab) of the surface code. In 2006, two researchers showed(opens a new tab) that an optimized version of the code had an error threshold around 1%, 100 times higher than the thresholds of earlier quantum codes. These error rates were still out of reach for the rudimentary qubits of the mid-2000s, but they no longer seemed so unattainable…

…Fowler, Martinis and two other researchers wrote a 50-page paper(opens a new tab) that outlined a practical implementation of the surface code. They estimated that with enough clever engineering, they’d eventually be able to reduce the error rates of their physical qubits to 0.1%, far below the surface-code threshold. Then in principle they could scale up the size of the grid to reduce the error rate of the logical qubits to an arbitrarily low level. It was a blueprint for a full-scale quantum computer…

…When you put the theory of quantum computing into practice, the first step is perhaps the most consequential: What hardware do you use? Many different physical systems can serve as qubits, and each has different strengths and weaknesses. Martinis and his colleagues specialized in so-called superconducting qubits, which are tiny electrical circuits made of superconducting metal on silicon chips. A single chip can host many qubits arranged in a grid — precisely the layout the surface code demands.

The Google Quantum AI team spent years improving their qubit design and fabrication procedures, scaling up from a handful of qubits to dozens, and honing their ability to manipulate many qubits at once. In 2021, they were finally ready to try error correction with the surface code for the first time. They knew they could build individual physical qubits with error rates below the surface-code threshold. But they had to see if those qubits could work together to make a logical qubit that was better than the sum of its parts. Specifically, they needed to show that as they scaled up the code — by using a larger patch of the physical-qubit grid to encode the logical qubit — the error rate would get lower.

They started with the smallest possible surface code, called a “distance-3” code, which uses a 3-by-3 grid of physical qubits to encode one logical qubit (plus another eight qubits for measurement, for a total of 17). Then they took one step up, to a distance-5 surface code, which has 49 total qubits. (Only odd code distances are useful.)

In a 2023 paper(opens a new tab), the team reported that the error rate of the distance-5 code was ever so slightly lower than that of the distance-3 code. It was an encouraging result, but inconclusive — they couldn’t declare victory just yet…

…At the beginning of 2024, they had a brand-new 72-qubit chip, code-named Willow, to test out. They spent a few weeks setting up all the equipment needed to measure and manipulate qubits…

…Then a graph popped up on the screen. The error rate for the distance-5 code wasn’t marginally lower than that of the distance-3 code. It was down by 40%. Over the following months, the team improved that number to 50%: One step up in code distance cut the logical qubit’s error rate in half…

…The team also wanted to see what would happen when they continued to scale up. But a distance-7 code would need 97 total qubits, more than the total number on their chip. In August, a new batch of 105-qubit Willow chips came out…

…When the group returned the following morning, they saw that going from a distance-5 to a distance-7 code had once again cut the logical qubit’s error rate in half. This kind of exponential scaling — where the error rate drops by the same factor with each step up in code distance — is precisely what the theory predicts. It was an unambiguous sign that they’d reduced the physical qubits’ error rates well below the surface-code threshold…

…At the same time, researchers recognize that they still have a long way to go. The Google Quantum AI team only demonstrated error correction using a single logical qubit. Adding interactions between multiple logical qubits will introduce new experimental challenges.

Then there’s the matter of scaling up. To get the error rates low enough to do useful quantum computations, researchers will need to further improve their physical qubits. They’ll also need to make logical qubits out of something much larger than a distance-7 code. Finally, they’ll need to combine thousands of these logical qubits — more than a million physical qubits.

2. History: Kodak & Fujifilm – Find Value

Ultimately, Kodak couldn’t adapt to the changing world and filed for bankruptcy in 2012.

In the game for over 100 years, Kodak survived two World Wars and the Great Depression and helped humans photograph the moon and Mars. Like Coca-Cola and McDonald’s, it used to be one of the most recognized brands in the world…

…Faced with a sharp decline in sales from its cash cow product, Fujifilm acted swiftly and changed its business through innovation and external growth. Under Shigetaka Komori (President in 2000), Fujifilm quickly carried out massive reforms. In 2004, Komori came up with a six-year plan called VISION75.

The management restructured its film business by downscaling the production lines and closing redundant facilities. In the meantime, the R&D departments moved to a newly built facility to unify the research efforts and promote better communication and innovation culture among engineers.

Realizing that the digital camera business would not replace the lucrative film due to the low margins, Fujifilm performed a massive diversification based on capabilities and innovation.

Even before launching the VISION75 plan, Komori had taken stock of their technologies and compared them with the demand of the international market. After which the R&D team came up with a chart listing the all existing in-house technologies that could match future markets.

For instance, Fujifilm was able to predict the boom of LCD screens and invested heavily in this market. Leveraging on photo film technology, they created FUJITAC, a variety of high-performance films essential for making LCD panels for TV, computers, and smartphones. Today, FUJITAC owns 70% of the market for protective LCD polarizer films.

Fujifilm also targeted unexpected markets like cosmetics. The rationale behind cosmetics comes from 70 years of experience in gelatin, the chief ingredient of photo film which is derived from collagen. Human skin is 70% collagen. Fujifilm also possessed deep knowledge in oxidation, a process connected both to the aging of human skin and to the fading of photos over time.

When promising technologies didn’t exist internally, Fujifilm proceeded by mergers and acquisitions. Based on technological synergies, it acquired Toyoma Chemical in 2008 to enter the drug business. Delving further into the healthcare segment, Fujifilm also brought a radio-pharmaceutical company now called Fujifilm RI Pharma. It also reinforced its position in existing joint ventures such as Fuji-Xerox which became a consolidated subsidiary in 2001 after Fujifilm purchased an additional 25% share in this partnership.

Fast forward 9 years after the peak of film sales, in 2010, Fujifilm was a new company. In 2000, 60% of sales and 70% of profits came from the film ecosystem, compare this to 2010 where the “Imaging segment” accounted for less than 16% of sales. Fujifilm managed to emerge victorious through a restructuring and diversification strategy…

…Unlike Fujifilm which recognized early on that photography was a doomed business and tackled new markets with a completely different portfolio, Kodak made multiple wrong moves and persisted in the decaying film industry.

It was not that Kodak didn’t want to change, it tried hard, but it did it wrong. Kodak’s management didn’t fully recognize that the rise of digital imaging would have dire consequences for the future of photo printing. It tried to replicate the film print business model in the digital world. In 2004, Facebook was launched, and people are just not going to print pictures anymore.

Interestingly, Kodak understood the impact of digitalization and predicted that pictures would be shared online. They acquired a photo-sharing website called Ofoto in 2001. Unfortunately, the company used Ofoto to make people print digital pictures. They failed in realizing that online photo sharing was the new business, not just a way to expand printing sales…

…While Fujifilm invested heavily in the pharmaceutical and healthcare sector to reduce its exposure to the challenging photo industry, Kodak sold its highly profitable Healthcare Imaging branch in 2007 to put more resources into its losing consumer camera division.

3. One Bed, Two Dreams: Building Silicon Valley Bank in China with Ken Wilcox (Transcript here) – Bernard Leong and Ken Wilcox

Wilcox: In the US, banks sometimes fail. When I started my career 40 years ago in banking, we had 18,000 banks. Today we have about 5,000. What happened to all of them? Where did 13,000 banks go? Some of them got acquired, but many of them failed. When a bank makes too many bad loans, the Federal Reserve causes it to fail and it disappears. In China, banks don’t fail. First of all, banks are fundamentally owned by the government and when they make too many bad loans, they don’t typically fail. Usually the government, the regulators, come and somebody gets arrested and the government re-capitalizes the bank. It’s often very quiet – it’s not even necessarily announced to the world – and the bank keeps on going. What does that mean? That means that Chinese banks can take more risk than US banks can. In the US, we had almost no competitors because everybody thought “Lending to technology companies is way too risky, so we’ll just let Silicon Valley Bank do it. None of the rest of us will try.” In China, many, many, many banks want to copy us and do the same thing, because they’re not worried about what happens if we lose too much money. So that’s another big difference there…

…Wilcox: After I’d been there for several months, it occurred to me one day that my main conversation partner, the guy who is the Chairman, who was from Shanghai Pudong Development Bank, it occurred to me that he actually wears three hats. The only hat I wear is banker / businessman. But he had a banker / businessman hat, and he had a party hat, and he had a government hat. Then I started to wonder, when I’m talking with him, which hat is he wearing? It took me a long time before I figured out he doesn’t even think he has three hats. He thinks they’re all the same hat, so he’s not even thinking about it the same way I was. So I think that’s quite confusing. 

It’s also confusing when people find out, when a US company comes to China and finds out that it’s going to get a Party Committee in their organization. They get very confused because they don’t know what a Party Committee is. If you ask people in government or in the party, “What’s a Party Committee?” You say, “We’re going to have one , but I don’t understand what it is?” It’s hard for them to explain. You get multiple definitions and then you don’t know what is actually going to happen. Some people will tell me, “When you get a Party Committee, it’ll be so good because all the employees in your organization who are members of the party will have a place to gather once a month and discuss things.” Then somebody else says, “When you get a Party Committee, it’ll be so much easier because the Party Committee will help you put on social events for the employees, all of the employees.” But then somebody else told me, “No, when you get a Party Committee, it’ll be like another board, but a secret board. You won’t know who’s on it and they will influence what the real board does – or what I would call the real board.” Then other people told me, “Don’t pay any attention. That’s all silliness. There is no such thing as a Party Committee.” So it’s very, very confusing…

…Wilcox: I’ll give you the best example and that is that I believe based on the years I spent in China, that ultimately the main reason they wanted us in China – and they actually were very determined to get us to come to China. I remember that early on, a couple of years before my wife and I moved to China, I had a series of meetings with a very high-level government official who’s also got a lot of status in the party. He was saying to me, “Ken, we really want you to bring your bank to China. Your bank is more important than any bank we’ve ever met. You’re more important than – he explicitly said this – he says, You’re more important than Morgan Stanley and more important than Goldman Sachs. And by the way Ken, you’re one of the smartest Americans we’ve met.” So you think to yourself, “Well this is an exaggeration, but it does feel nice.” He obviously is going to help me get established in China. But what I didn’t realize is that the main reason they wanted us in China was so that they could study our business model and figure out how to copy it over time. That was something I wasn’t expecting, but I should have if I were less naive. If I were better prepared, I would have realized that was the intention. So the original title, the working title I had for my book, which I had to change because the publisher didn’t like it, my original title was, “One Bed, Two Dreams”, because that’s a phrase that most Chinese are familiar with. It explains why it didn’t work well, because my dream was working with all these Chinese technology companies and helping them do business with the rest of the world, and their dream was learning our business model.

The result was that when they gave us our license, they also told us that we would not be able to use Chinese currency for three years. That made it almost impossible to do business for the first three years. The people that said these things were both members of the government and members of the party. So I don’t know which one was talking. But they said, “We understand that you won’t be able to do much business for the first three years because the companies that you want to work with all want renminbi, they don’t want US dollars. But you can still be a good citizen. You can do what we would do, and that is we here in China help each other. So you can be helpful and prove that you care about China by teaching other banks your business model during the three years when you can’t really do much business. We’ll give you subsidy to help support you during the three years when you can’t earn much money because you can’t really do any business.” Then at the end of the three years when they gave us permission to use renminbi, they said to us, “We are so happy that you came to China and we really admire your business model and we admire it so much that we’re starting a bank of our own using your business model. Would you mind staying a little longer and being an advisor to this new bank that’s going to use your business model?” It felt like they were stealing my intellectual property but I’m not sure they thought of it that way…

…Wilcox: General Motors when it went over to China in 1985, the Chinese really didn’t have an auto industry. They wanted General Motors there not because they wanted General Motors to make a lot of money. It was because they wanted to learn about automobile manufacturing and because it took so long to build up the knowledge base, General Motors was welcome for about 30 years. But now General Motors is slowly losing market share and it’s probably going to withdraw from China. Then what will happen is China has made so much progress partially because they’re hardworking and smart, partially because they had General Motors there to learn from them, and then once General Motors retracts and goes back to the US, the auto industry in China will begin exporting and competing globally. I think actually the Chinese have done such a good job of first of all, learning from foreign automakers, but then on top of that, taking it further that the foreign automakers are in huge trouble. I think China’s automobile industry will dominate in the future. 

4. Weekend thoughts: crypto, mania, and reflexivity follow up – Andrew Walker

When I first saw the “BTC yield” metric, I thought it was pretty crazy. MSTR is trading for approaching 3x the value of their bitcoin; if they issue stock and use all the stock to buy bitcoin, of course it’s going to cause their bitcoin holdings per share to go up…. and even more so if they issue debt and use that to buy bitcoin and then judge themselves on a per share basis! Taken to its extreme2, if you thought BTC yield was truly the be all, end all of value creation, and the higher the BTC yield the better, then any company following a pure BTC yield strategy should lever themselves up to the maximum amount possible, no matter the terms, and use all of the proceeds to buy BTC. Obviously no one does that because it would be insanity and eventually banks would stop lending, but I illustrate that only to show that purely maximize BTC yield is clearly not value maximizing….

But, if you look at the fine print, BTC yield is even crazier than simply suggesting increasing BTC per share is the only value creation metric that matters. If you really look at the MSTR BTC yield table above or read their disclosures, you’ll notice that the BTC yield assumes that all of their convertible debt converts…

…So, go back to MSTR’s BTC yield table; they have a set of 2029 converts that convert at $672.40/share. Those are far, far out of the money (MSTR’s stock trades for ~$400/share as I write this)…. yet MSTR’s BTC yield assumes those converts are in the money / will convert for their BTC yield.

That is an insane assumption that casually assumes MSTR’s shares almost double3. And, again, by taking this assumption to its extreme, we can see how wild it is. Like all things, convert debt involves different trade offs; for example, you could get a higher strike price by taking on a higher interest rate (i.e. if your strike price is ~$670 at a 0% interest rate, you could probably push it up to $770 by taking on a 3% interest rate or $870 by taking on a 6% interest rate4). MSTR has issued all of this convert debt deals at 0% interest rates, which is a great pitch (“we’re borrowing for free, we don’t have to pay a carry to buy BTC, etc”)…. but if BTC yield is all that matters, MSTR could start issuing convertible debt with really high interest rates, which would jack that strike price of the convert up, thus decreasing dilution and increasing the BTC yield…

…MSTR fans would say “but raising converts with interest doesn’t make sense; it’s no longer free money / now it has a carry cost.” And I understand that argument…. but convertible debt isn’t free money either, and I just do this to highlight how insane BTC yield is as a be all / end all metric!…

…The BTC yield that all of these companies present assumes that their convert debt converts, and that is a big / crazy assumption…. but it’s interesting to think about what will happen in five years. There is, of course, a world where BTC goes to $250k (or higher) and all of these stocks moon. In that world, the converts will be well in the money, and all of this worry will sound silly…. but there is also a world where BTC stalls out or drops over the next few years, and that world is really interesting. All of these companies are raising converts with 5-7 year maturities, so if BTC doesn’t moon and the converts aren’t in the money, you’re going to have all of the BTC standard companies facing a maturity wall at the same time. What happens then? I doubt they can roll the converts at anything close to the same terms (remember, cheap converts require high volatility, and if the stocks have stalled out for five years vol is going to be a lot lower), so they’ll either need to sell a ton of equity to paydown the debt (which will be tough; there probably won’t be much enthusiasm for the stock, and I’m not sure the market would be able to absorb the hypothetical amount of stock they’d need to issue without some enthusiasm)…. or you’ll have a wave of BTC standard companies all looking to sell down some of their bitcoin to payoff converts at the exact same time.

5. Satya Nadella | BG2 (Transcript here)- Bill Gurley, Brad Gerstner, and Satya Nadella

Gerstner: Shifting maybe to enterprise AI, Satya. The Microsoft AI business has already reported to be about $10 billion. You’ve said that it’s all inference and that you’re not actually renting raw GPUs to others to train on, because your inference demand is so high. As we think about this, there’s a lot of skepticism out there in the world as to whether or not major workloads are moving. If you think about the key revenue products that people are using today and how it’s driving that inference revenue for you today, and how that may be similar or different from Amazon or Google, I’d be interested in that.

Nadella: The way for us this thing has played out is, you got to remember most of our training stuff with OpenAI is sort of more investment logic. It’s not in our quarterly results – it’s more in the other income, based on our investment.

Gerstner: Other income or loss right?

Nadella: That is right. That’s how it shows up. So most of the revenue or all the revenue is pretty much our API business or in fact, to your point, ChatGPT’s inference costs are there, so that’s a different piece. The fact is the big-hit apps of this era are ChatGPT, Co-Pilot, GitHub Co-Pilot, and the APIs of OpenAI and Azure OpenAI. In some sense, if you had to list out the 10 most hit apps, these would probably be in the four or five of them and so therefore that’s the biggest driver.

The advantage we have had, and OpenAI has had, is we’ve had two years of runway pretty much uncontested. To your point, Bill made the point about everybody’s awake and it might be. I don’t think there will be ever again maybe a two-year lead like this, who knows? It’s all you say that and somebody else drops some sample and suddenly blows the world away. But that said, I think it’s unlikely that that type of lead could be established with some foundation model. But we had that advantage, that was the great advantage we’ve had with OpenAI. OpenAI was able to really build out this escape velocity with ChatGPT.

But on the API side, the biggest thing that we were able to gain was.. Take Shopify or Stripe or Spotify. These were not customers of Azure, they were all customers of GCP or they were customers of AWS. So suddenly we got access to many, many more logos, who are all “digital natives” who are using Azure in some shape or fashion and so on. So that’s sort of one. When it comes to the traditional enterprise, I think it’s scaling. Literally it is people are playing with Co-Pilot on one end and then are building agents on the other end using Foundry. But these things are design wins and project wins and they’re slow, but they’re starting to scale. Again, the fact that we’ve had two years of runway on it, I think…

I like that business a lot more, and that’s one of the reasons why the adverse selection problems here would have been lots of tech startups all looking for their H100 allocations in small batches. Having watched what happened to Sun Microsystems in the dotcom, I always worry about that. You just can’t chase everybody building models. In fact, even the investor side, I think the sentiment is changing, which is now people are wanting to be more capital-light and build on top of other people’s models and so on and so forth. If that’s the case, everybody who was looking for H100 will not want to look for it more. So that’s what we’ve been selective on.

Gerstner: You’re saying for the others that training of those models and those model clusters was a much bigger part of their AI revenue versus yours? 

Nadella: I don’t know. This is where I’m speaking for other people’s results. It’s just I go back and say, “What are the other big-hit apps?” I don’t know what they are. What models do they run? Where do they run them? When I look at the DAU numbers of any of these AI products, there is ChatGPT, and then there is – even Gemini, I’m very surprised at the Gemini numbers, obviously I think it’ll grow because of all the inherent distribution. But it’s kind of interesting to say that they’re not that many. In fact, we talk a lot more about AI scale, but there is not that many hit apps. There is ChatGPT, Github Co-Pilot, there’s Co-Pilot, and there’s Gemini. I think those are the four I would say, in a DAU, is there anything else that comes to your mind?…

…Gurley: Satya, on the enterprise side, obviously the coding space is off to the races and you guys are doing well and there’s a lot of venture-backed players there. On some of the productivity apps, I have a question about the the Co-Pilot approach and I guess Marc Benioff’s been obnoxiously critical on this front, calling it Clippy 2 or whatever. Do you worry that someone might think first-principles AI from ground-up, and that some of the infrastructure, say in an Excel spreadsheet, isn’t necessary to know if you did a AI-first product. The same thing by the way could be said about the CRM right? There’s a bunch of fields and tasks that that may be able to be obfuscated for the user.

Nadella: It’s a very, very, very important question. The SaaS applications or biz apps, let me just speak of our own Dynamics thing. The approach at least we’re taking is, I think the notion that business applications exist, that’s probably where they’ll all collapse in the agent era. Because if you think about it, they are essentially CRUD databases with a bunch of business logic. The business logic is all going to these agents, and these agents are going to be multi-repo CRUD. They’re not going to discriminate between what the back-end is, they’re going to update multiple databases, and all the logic will be in the AI tier so to speak. Once the AI tier becomes the place where all the logic is, then people will start replacing the backends right? In fact it’s interesting, as we speak, I think we are seeing pretty high rates of wins on Dynamics backends and the agent use, an we are going to go pretty aggressively and try and collapse it all, whether it’s in customer service, whether it is in… 

By the way, the other fascinating thing that’s increasing is just not CRM, but even what we call finance and operations, because people want more AI-native biz app. That means the biz app, the logic tier, can be orchestrated by AI and AI agents. So in other words, Co-Pilot to agent to my business application should be very seamless.

Now in the same way, you could even say, “Why do I need Excel?” Interestingly enough, one of the most exciting things for me is Excel with Python, is like GitHub with Co-Pilot. So what we’ve done is, when you have Excel – by the way this would be fun for you guys – which is you should just bring up Excel, bring up Co-Pilot, and start playing with it. Because it’s no longer like – it is like having a data analyst, so it’s no longer just making sense of the numbers that you have. It will do the plan for you. It will literally – like how GitHub Co-Pilot Workspace creates the plan and then it executes the plan – this is like a data analyst who is using Excel as a sort of row/column visualization to do analysis scratch pad. So it kind of tools you. So the Co-Pilot is using Excel as a tool with all of its action space because it can generate and it has python interpreter. That is in fact a great way to reconceptualize Excel. At some point you could say, “I’ll generate all of Excel” and that is also true. After all, there’s a code interpreter, so therefore you can generate anything.\

So yes, I think there will be disruption. The way we are approaching, at least our M365 stuff is, one is build Co-Pilot as that organizing layer UI for AI, get all agents, including our own agents – you can say Excel is an agent to my Co-Pilot, Word is an agent, it’s kind of a specialized canvases, which is I’m doing a legal document, let me take it into Pages and then to Word and then have the Co-Pilot go with it, go into Excel and have the Co-Pilot go with it. That’s sort of a new way to think about the work in workflow…

…Gurley: Satya, there’s been a lot of talk about model scaling and obviously there was talk, historically about 10x-ing the cluster size that you might do, over and over again, not once and then twice. X.AI is still making noise about going in that direction. There was a podcast recently where they flipped everything on their head and they said “If we’re not doing that anymore, it’s way better because we can just move on to inference which is getting cheaper and you won’t have to spend all this capex. I’m curious, those are two views of the same coin. But what’s your view on LLM model scaling and training cost, and where we’re headed in the future?

Nadella: I’m a big believer in scaling laws I’ll first say. In fact, if anything, the bet we placed in 2019 was on scaling laws and I stay on that. In other words, don’t bet against scaling laws. But at the same time, let’s also be grounded on a couple of different things.

One is these exponentials on scaling laws will become harder, just because as the clusters become harder, the distributed computing problem of doing large scale training becomes harder. That’s one side of it. But I would just still say – and I’ll let the OpenAI folks speak for what they’re doing – but they are continuing to – pre-training I think is not over, it continues. But the exciting thing, which again OpenAI has talked about and Sam has talked about, is what they’ve done with o1. This Chain of Thought with autograding is just a fantastic. In fact, basically, it is test-time compute or inference-time compute as an another scaling law. You have pre-training, and then you have effectively this test-time sampling that then creates the tokens that can go back into pre-training, creating even more powerful models that then are running on your inference. So therefore, that’s I think a fantastic way to increase model capability.

The good news of test-time or inference-time compute is sometimes, running of those o1 models means… There’s two separate things. Sampling is like training, when you’re using it to generate tokens for your pre-training. But also customers, when they are using o1, they’re using more of your meters, so you are getting paid for it. Therefore, there is more of an economic model, so I like it. In fact, that’s where I said I have a good structural position with 60-plus data centers all over the world.

Gurley: It’s a different hardware architecture for one of those scaling versus the other, for the pre-training versus…

Nadella: Exactly. I think the best way to think about it is, it’s a ratio. Going back to Brad’s thing about ROIC, this is where I think you have to really establish a stable state. In fact, whenever I’ve talked to Jensen, I think he’s got it right, which is you want to buy some every year. Think about it, when you depreciate something over 6 years, the best way is what we have always done, which is you buy a little every year and you age it, you age it, you age it. You use the leading node for training and then the next year it goes into inference, and that’s sort of the stable state I think we will get into across the fleet for both utilization and the ROIC and then the demand meets supply.

Basically, to your point about everybody saying, “Have the exponentials stopped?” One of the other things is the economic realities will also stop, right? At some point everybody will look and say, “What’s the economically rational thing to do?” Which is, “Even if I double every year’s capability but I’m not able to sell that inventory,” and the other problem is the Winner’s Curse, which is – you don’t even have to publish a paper, the other folks have to just look at your capability and do either a distillation… It’s like piracy. You can sign off all kinds of terms of use, but like it’s impossible to control distillation. That’s one. Second thing is, you don’t even have to do anything, you just have to reverse engineer that capability and you do it in a more computer efficient way. So given all this, I think there will be a governor on how much people will chase. Right now a little bit of everybody wants to be first. It’s great, but at some point all the economic reality will set in on everyone and the network effects are at the app layer, so why would I want to spend a lot on some model capability with the network effects are all on the app?…

…Gurley: Does your answer to Brad’s question about the balancing of GPU ROI, does that answer the question as to why you’ve outsourced some of the infrastructure to Coreweave in that partnership that you have?

Nadella: That we did because we all got caught with the hit called ChatGPT. It was impossible. There’s no supply chain planning I could have done. None of us knew what was going to happen. What happened in November of ‘22, that was just a bolt from the blue, therefore we had to catch up. So we said, “We’re not going to worry about too much inefficiency.” That’s why whether it’s Coreweave or many others – we bought all over the place. That is a one time thing and then now it’s all catching up. That was just more about trying to get caught up with demand.

Gerstner: Are you still supply-constrained Satya?

Nadella: Power, yes. I am not chip supply-constrained. We were definitely constrained in ‘24. What we have told the street is that’s why we are optimistic about the first half of ‘25, which is the rest of our fiscal year and then after that I think we’ll be in better shape going into ‘26 and so on. We have good line of sight.

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Disclaimer: The Good Investors is the personal investing blog of two simple guys who are passionate about educating Singaporeans about stock market investing. By using this Site, you specifically agree that none of the information provided constitutes financial, investment, or other professional advice. It is only intended to provide education. Speak with a professional before making important decisions about your money, your professional life, or even your personal life. We currently have a vested interest in Alphabet (parent of Google), Amazon, Meta Platforms (parent of Facebook), and Microsoft. Holdings are subject to change at any time.

Editor’s note: This is the latest edition in the “Company Notes Series”, where we periodically share our notes on companies we’ve studied in the recent past but currently have no vested interest in (we may invest in or sell shares in the companies mentioned at any time). The notes are raw and not updated, and the “as of” date for the data is given at the start of the notes. The first two editions in the series can be found here and here. Please give us your thoughts on the series through the “Contact Us” page; your feedback will determine if we continue with it. Thanks in advance!

Start of notes for Golden Throat Holdings

Data as of 16 January 2023

History of Golden Throat Holdings and current management/major shareholders

• Current HQ: Guangxi Zhuang, China
• IPO date: July 2015, on Hong Kong Stock Exchange
• Golden Throat Holdings’ history dates back to 1956 when Liuzhou No.2 Sweet Factory (柳州市糖 果二廠), the predecessor of Golden Throat Company (also known as Guangxi Golden Throat), was established. Golden Throat Company today manufactures and sells lozenges and other pharmaceutical and food products.
• Golden Throat Holdings’ flagship product is Golden Throat Lozenges (OTC), which was launched in 1994. Wang Yao Fa contributed to the creation of the formula for the Golden Throat Lozenges (OTC) product and his portrait was historically used by Golden Throat Holdings on the product packaging; the portrait was changed to Jiang Peizhen in 2015.
• Golden Throat Company (the main operating entity in China of Golden Throat Holdings) was established in Liuzhou, Guangxi Zhuang, China, on 18 September 1998 by Jiang Peizhen as the original controlling shareholder. She has been involved with Golden Throat Holdings for over 60 years, since 1956.
• Jiang and her son, Zeng Yong, control 69.79% of Golden Throat’s shares (the 69.79% equates to 516.0137 million shares) as of 30 June 2022. At the 11 January 2023 share price of HK$1.98, their stake equates to HK$1.02 billion.
• Jiang, 76, is currently chairman and non-executive director of Golden Throat Holdings, while Zeng, 48, is an executive director and vice chairman of the board. Zeng has been involved with Golden Throat Holdings since 1995. Both Jiang and Zeng have been in their respective roles since February 2015.

Golden Throat Holdings’ business

• Revenue in 2021 was RMB 820.5 million, of which 99.6% was from Mainland China.
• The company reports its revenue by three product categories, which include Golden Throat Lozenges (OTC), Golden Throat Lozenge Series Products, and other products.
• Golden Throat Lozenge (OTC): A type of lozenge mainly designed to relieve symptoms of sore and dry throat and hoarse voice caused by acute pharyngitis. Golden Throat Lozenges (OTC) was approved as over-the-counter medicine by the National Medical Products Administration (NMPA), China’s version of the FDA in the USA. As such, Golden Throat Lozenges (OTC) can be purchased by the public in pharmacies without requiring the prescription of a qualified medical professional. As of 31 December 2021, Golden Throat Lozenges (OTC) were exported to the United States, Canada, Russia, the European Union, Australia, Southeast Asia, Middle East, Mexico and Africa, and Mongolia, a newly explored export country in 2019. For the year ended 31 December 2021, Golden Throat Lozenges (OTC) accounted for 90.1% of Golden Throat Holdings’ total revenue.
• Golden Throat Lozenge Series Products: Includes seven products comprising of Dule Lozenges (都樂含片), sugar-free Dule Lozenges, and five other sugar-free flavours of this series, namely orange (香橙), fructus momordicae (羅漢果), chrysanthemum (桑菊), American ginseng (西洋參) and hawthorn (山楂). A major difference between Golden Throat Lozenges (OTC) and Golden Throat Lozenge Series Products is that the former is approved as over-the-counter medicine, whereas the latter is approved as food products. The sugar-free series of Golden Throat Lozenge Series Products was launched in 2013, which supplements the company’s original sales channel and provides consumers with more diversified choices. As of 31 December 2021, Golden Throat Lozenge Series Products were exported to 17 countries and regions, and accounted for 8.7% of Golden Throat Holdings’ total revenue in 2021.
• Other products: Accounted for approximately 1.2% of Golden Throat Holdings’ total revenue in 2021. Includes: (1) Yinxingye Tablet ( 銀杏葉片), which is designed to facilitate blood circulation, remove blood stasis and dredge energy channels and was approved as a prescription medicine by the NMPA; (2) a new product, Golden Throat Intestinal Series (金嗓子腸寶), which is an exclusive nutrition for probiotics, also known as prebiotics; and (3) Golden Throat Compound Probiotic Lozenges, which was launched in June 2022 and was developed by Golden Throat Holdings and the scientific research team of “Food Microbial Function Development” of Beijing Agricultural College. Golden Throat Compound Probiotic Lozenges addresses the lack of self-developed probiotics in China. Golden Throat Holdings has developed six kinds of proprietary probiotic bacteria in three new flavors and the company is committed to using “Chinese bacteria” to improve the physique of Chinese citizens. Golden Throat Compound Probiotics adopts the internationally leading three-layer embedding technology, 360-degree thermal radiation freeze drying technology, and automatic ingredient fermentation and cultivation system.
• Golden Throat Holdings has established an extensive and structured sales and distribution network throughout China for its (i) over-the-counter medicines, (ii) food products, and (iii) prescription medicines. As of 31 December 2021 and 30 June 2022, substantially all of the company’s revenue was generated from sales to distributors. In 2021, there was only one customer that accounted for more than 10% of Golden Throat Holdings’ revenue (11.7%); there was no such customer in 2020.
• Golden Throat Holdings has a well-established brand in China: 
  • In October 2021, in the 2021 ranking of China nonprescription medicines enterprises and product brands, Golden Throat Lozenges (OTC) was recognised as No. 1 amongst Chinese traditional medicines (Throat) by the China Nonprescription Medicines Association.
  • Golden Throat Holdings was ranked 43rd amongst the nonprescription manufacturing enterprises in the 2021 ranking of China non-prescription medicines enterprises and product brands.
  • Golden Throat Holdings was listed in the Top 500 Chinese Brands at the 14th China Brand Festival in August 2020.
  • In August 2020, Golden Throat Holdings claimed the title of “2019 China Traditional Medicines Pharmaceutical Industry Top 100 Enterprise” at the China Pharmaceutical Industry Top 100 Annual Assembly.
  • In 2019, Golden Throat was awarded the Best Brand Value Award at the China Financial Market Awards 2019, and won the Huapu Award at the 13th China Brand Festival in August.
  •  In 2017, the Golden Throat (金嗓子) brand was selected as a world famous brand by the China America Branding Strategy Forum and also ranked amongst the listed companies on the Forbes China Up-and-Comers List.

Golden Throat Holdings’ market and future expansion

• According to a 2015 Euromonitor Report, retail sales value of lozenges in China increased 10.4% per year from RMB 2.09 billion in 2009 to RMB 3.42 billion in 2014, and was expected to increase to RMB 5.46 billion in 2019, at a CAGR of 9.7%. Lozenges accounted for 72% of the total throat remedies market in China in 2014; the throat remedies market primarily includes over-the-counter medicines and medicated confectionery (which are food).
• In 2021, plants and office buildings of a new medicine production and research and development base for Golden Throat Holdings, located at Luowei Industrial Concentration Area, Liuzhou, Guangxi Zhuang Autonomous Region, as well as the commissioning of product lines and trial production were completed. Golden Throat Holdings completed the overall relocation in the second half of 2021. The new production base covers a usable area of about 60,000 square metres, including research and development centres, production plants, warehouses and administrative office buildings. “The fully automated production line in the production plant will improve the efficiency of the production process. A brand-new modern production enterprise will be formed with the new production and research and development base, new factories, new workflow and new production lines, which will completely upgrade the management platform and manufacturing platform of the factories, comprehensively improving the manufacturing quality and technology content of the products, enhancing the comprehensive competitiveness of the Company, and will lay a solid foundation for expanding and strengthening the Company.” The new production base increased Golden Throat’s production capacity for its main products by 57% to 198.5 million boxes of Golden Throat Lozenges. See video of the new production base: https://news.gxtv.cn/article/detail_567c4b49e6924346917643b221fe9555.html
• Also in 2021, Golden Throat Holdings selected a 48 mu (~32,000 square metres) piece of land in the south of the new drug production and R&D base as the site for the second phase of the new Golden Throat Base, which is expected to have a usable area of approximately 50,000 square metres after completion. The second phase will house a food production plant and a food research and development centre. After completion, a high-tech R&D team, smart manufacturing and smart sales will be introduced to develop more comprehensive health products. The second phase of the Golden Throat new base will form the core of Golden Throat Doctor Workstation, the Golden Throat Professor Workstation, the Golden Throat Research Institute, the Golden Throat Gastrointestinal Research Institute, and the Golden Throat Heart and Brain Research Institute. It will also facilitate the development of new products such as genetic medicines, traditional Chinese medicine prescriptions, specialty medical devices, and specialty health foods. As of 30 June 2022, the second phase of the Golden Throat new base is in the initial stage of construction.
• The Golden Throat WeChat Mini Program Mall was launched in early 2020. “We will continue to expand online sales channel in 2022, and we believe there would be breakthroughs in our online business in the future.”

Golden Throat’s sales volumes and pricing of products

• There was a change in packaging-configuration in August 2013, so numbers for 2012 and 2013 are not like-for-like comparisons with numbers in later years.
• Golden Throat Holdings has managed to raise the prices for its Golden Throat Lozenges (OTC) products over time, while keeping  gross margin steady, keeping sales volume steady (although less steady then gross margin), and increasing revenue → signs of pricing power for Golden Throat Lozenges (OTC) product
• Golden Throat Holdings has managed to raise the prices for its Golden Throat Lozenge Series Products over time, while increasing gross margin, increasing sales volume, and increasing revenue → signs of pricing power for Golden Throat Lozenge Series Products
• Golden Throat Holdings’ sales volume was hurt in 2020 because of COVID, but the company still maintained or increased its product prices.
• Golden Throat’s sales volume for Golden Throat Lozenge (OTC) products did not increase much over time because the volume was already near the company’s capacity – prior to the expansion mentioned in Point 3, Golden Throat’s annual production capacity was ~126 million boxes of the Golden Throat Lozenge (OTC) product.

Golden Throat financial performance

Annual numbers

• Revenue has grown over time but had some ups and downs – same with net profit
• Was always generating positive operating cash flow and free cash flow (with exception of 2017), although there’s no clear growth in cash flows.
• Balance sheet was always in a strong net-cash position
• No history of dilution (IPO happened in 2015 – immediately after IPO, there was around 726.36 million shares)
• There was a dividend paid in every year since the company’s IPO, and it has increased over time; the dividend also looks fairly sustainable

Half-yearly numbers

• Revenue growth in H1 2022 was affected by resurgence of COVID in China, and so was net-income
• But cash flows have improved tremendously and balance sheet remains rock-solid
• Worth noting that Golden Throat’s borrowings are all on fixed rates, so there’s no danger of rising interesting rates negatively affecting the company’s profit and/or cash flow 

Management’s integrity and kindness

• There are related party transactions (RPTs), but they are minimal. In 2021, Golden Throat Holdings incurred RMB 9.576 million in expenses to procure raw ingredients (such as liquid isomalt, isomalt AG, syrup, and probiotics) from a related entity, Changbao; in 2020, the amount was RMB 4.388 million. These amounts make up only a single-digit percentage of total net profit (and even much smaller percentage of total revenue) in their respective years.
• The remuneration of Jiang Peizhen and Zeng Yong has largely increased at a faster rate than Golden Throat Holdings’ revenue, net income, and FCF over the years, especially after the company’s IPO. But their remuneration levels only make up a single-digit percentage of Golden Throat Holdings’ net income (see table below).
• Golden Throat Holdings ended 2021 with 937 full-time employees, of which 100 are disabled persons. In August 2020, Golden Throat Holdings provided electric vehicles for employees commuting to work. The EVs are produced by Liuzhou SGMW (柳州上汽通用五菱) and Golden Throat Holdings ordered over 700 of them from SGMW. Management thinks the EVs “would not only solve the transportation problem of employees with long commuting distance, but also effectively stimulate domestic demand and help economic growth and recovery.”

Valuation

• Valuation numbers based on 11 January 2023 share price of HK$1.98
• Trailing PE (price-to-earnings) of 7.8, trailing PFCF (price-to-free cash flow) of 7.7
• Net-cash per share of HK$0.88
• Trailing PE net of cash of 5.0, trailing PFCF ratio net of cash of 4.9
• Trailing dividend yield of a massive 9.1%
• Management wanted to acquire the company in August 2021 at HK$2.80 per share together with Affirma (emerging market private equity firm owned and operated by former senior leadership team of Standard Chartered Private Equity; managed over US$ 3.5 billion in assets at the time of the announcement).I think this price could be seen as a floor on the value of Golden Throat holdings. Golden Throat’s trailing earnings per share and free cash flow per share was RMB 0.30 (~HK$ 0.36 ) and RMB 0.18 (~HK$ 0.21), respectively, based on the company’s financials for the first half of 2021, meaning the acquisition price valued the company at a trailing PE and trailing PFCF ratio of just 7.8 and 13.1. Net of cash, the PE and PFCF ratios would be 5.3 and 8.8

Final thoughts (as of 16 January 2023)

• Very cheap valuation right now
• Possibility of much higher revenue in 2023 (compared to 2022 and 2021) as China has reopened and Chinese citizens depend on the Golden Throat Lozenge (OTC) product to soothe their ailments from COVID or otherwise; 2022’s overall numbers may be lower than in 2021 as China was in lockdown mode for most of 2022 and only opened up late in the year.
• Selling prices for Golden Throat Lozenge (OTC) products on Tmall are currently easily more than RMB 10 per box, and more commonly around RMB 12-14 per box (see screenshots below, taken on 16 Jan 2023 from Tmall app – sidenote: Tmall has better reputation than Taobao). The unit sale price to distributors reported by the company in H1 2022 was just RMB 7.0 per box; I think it’s reasonable to expect the unit sale price to distributors for 2023 – as well as overall volume – to be materially higher than 2022 and 2021, thereby boosting profit and cash flow margins for Golden Throat Holdings.
• Golden Throat Holdings had expanded production capacity in 2021, and is building a new plant right now.
• Golden Throat Holdings has also received strong government support for the production of its products. See the following English translations of a Mandarin article from the Guangxi government website:
  • “On January 4, Wei Guanghui, a member of the party group and deputy director of the Food and Drug Administration of the Autonomous Region, led a team to Guangxi Liangmianzhen Yikang Pharmaceutical Co., Ltd. and Guangxi Golden Throat Pharmaceutical Co., Ltd. The production of Golden Throat Lozenges provides door-to-door service guidance, and pays close attention to ensuring the supply of drugs for the prevention and control of the new crown epidemic.”
  • “Golden Throat Lozenges were selected into the “Catalogue of Drugs for New Coronary Virus Infection (First Edition)” issued by the Beijing Municipal Health and Health Commission. In order to meet the clinical needs of the general public, the company has expanded its capacity and production at full capacity, and the Food and Drug Administration of the Autonomous Region has followed up the whole process.”
  • “The working time of Golden Throat Lozenges has been extended from the original 8 hours to 12 hours, and the daily production has increased from 7.37 million tablets to 9.21 million tablets, which strongly supports the anti-epidemic needs of the people across the country.”
• For now, I see Golden Throat Holdings as a deep-value stock, but it could also change into a growth stock if its plans for new products such as genetic medicines, traditional Chinese medicine prescriptions, specialty medical devices, and specialty health foods succeed.
• One risk to the company’s future business prospects is if its Golden Throat Lozenge (OTC) product price gets controlled by the government. According to the IPO prospectus, “there had been no fixed or maximum prices promulgated by any authorities in China on Golden Throat Lozenges (OTC).” There’s been no update on the matter that I could find in subsequent annual reports.

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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 15 December 2024:

1. SpaceX: Rocket Ship – Matt Reustle and Luke Ward

Luke

So if we take the CapEx part of that first, NASA estimated that the cost to develop the Falcon 9 from scratch would be about $4 billion. But SpaceX ended up doing it for about a tenth of that price. So to begin with, that’s an order of magnitude improvement in the level of investment required.

SpaceX gives you the prices for launches on their website. So $70 million per launch of a Falcon 9 flight—that’s already 20 times cheaper than the Space Shuttle was per kilogram into orbit. But the real kicker, as you point out, is the operating leverage that comes from having partial reusability…

…Starship is designed to be fully and rapidly reusable. So unlike Falcon 9, which is only partially reusable but also able to fly multiple times every day, it’s going to have a payload capacity that’s about 100 tons to orbit at the beginning, but probably rising to closer to 200 tons to orbit over time.

And Musk has suggested that a variable cost of around $10 million per launch is the ballpark figure which they’d be aiming for at scale in a steady state, ambitiously maybe even falling to $2 million—a figure which has been touted. If you believe those kinds of performance levels are feasible, that gets the cost down to around $10 per kilogram. That’s over 100 times cheaper than the Falcon 9 we’re talking about at the moment. And that would have a dramatic effect on what’s economically feasible for humanity to do in space…

…Matt

Satellites in Low Earth Orbit—there is quite a bit of history in terms of that being the obvious space use case, that having an existing economy. I think Starlink is an extension of that. Different, absolutely, but an extension of what was going on.

Are there brand new industries being unlocked or obvious things with line of sight that open up from a space economy perspective that you see either today or, when I say near future, you could extend that out however far you think is reasonable.

Luke

A lot of these options which SpaceX has to develop, brand new markets that don’t exist already, are a function ultimately of the cost curve. Take semiconductor manufacturing on Earth; at the moment, we spend billions of dollars per fab to recreate the conditions which are readily accessible in space for free, if you can get there.

And so there’s some point on the cost curve intersecting between the cost of building a fab and the cost of launching a fab or the equipment of a fab into orbit and operating there instead. Same can be said of pharmaceutical research. The crystallization structures which are able to happen in space are different from the ones which are able to happen under the influence of gravity.

So if you think about pricing on pharmaceuticals, extending patent lives, etc., if you can move the manufacturing or the research lab for cutting-edge pharmaceuticals into space, you could make high-value, low-volume products. Something which would really make sense to do and doesn’t require a huge technological innovation to happen.

The list can go on and on—artificial organs, for example, being able to manufacture perfectly spherical lenses. There’s lots and lots of things which could be made.

Maybe the way to think about that is that space-based manufacturing could be the next large market for this if the costs can continue to come down. Starship having the volume of an A380 or a 747—think of the equivalent size of factory that represents. And if that can be launched every single day and recovered every single day for $10 per kilogram, that could be a really compelling way to do quite a lot of manufacturing.

Incidentally, that’s something that Jeff Bezos really focuses on in his vision for space as opposed to Mars per se, is where we can move a lot of the heavy-polluting industry off the planet. And why don’t we turn Earth into this perfect nature reserve, and all these polluting aspects of manufacturing can go into orbit, which again is very compelling.

Probably needs a lot more innovation to deliver communications from orbit, but I’d say it’s maybe an inevitability if the cost gets to a low enough point. You think how much solar energy is available without the atmospheric attenuation, for example—you know, 24/7. There’s lots of compelling reasons why if it’s cheap enough, at some point a lot of these things probably should happen, not just could happen.

Matt

The solar energy point, great example of something that is an entirely different dynamic in space than on Earth. What would the other things be? Just out of curiosity, when you mentioned semiconductors or pharmaceuticals, is it just purely gravity? Are there other things that are happening in space or not happening in space that happen on Earth that would drive that difference?

Luke

There’s the vacuum conditions—so there isn’t an atmosphere—so the level of impurities which you need to get rid of for a vapor deposition machine, for example. You don’t have the same kind of challenges there of having to have this deep vacuum.

Then, arguably, in space, because you don’t have gravity, you could construct much larger structures there rather than construct them on the ground and then launch them.

So again, that volume constraint which we were talking about earlier, in terms of how big your payload is—if you’re able to get enough stuff up there and assemble it in space, as we did with the International Space Station, things can be much, much larger given the payload bay of Starship than they could with the Space Shuttle.

Matt

When you think about low Earth orbit versus geosynchronous orbit versus something like Mars—which I think was the original vision with Elon and SpaceX—how much does that change the economics when you extend out?

Is it orders of magnitude where it’s an exponential cost curve to go further out? Even just if we focus on the launch and use a satellite for an example, before we get into all the manufacturing dynamics, is there any way to contextualize that from a cost perspective?

Luke

The really good news here is that gravitational force decreases with the square of distance. So the biggest challenge is getting off the surface and into orbit. Once you’re there, from an energy point of view, it’s a lot easier to go anywhere else in the solar system.

So if you were to take Falcon 9 again as the example, for the same price, it can place 20 tons into low Earth orbit, or it can place 4 tons into Martian orbit. That’s despite the latter being over a million times further away. Now, this feeds into what I think is probably the biggest misconception about SpaceX and its Mars ambitions.

I’d say for most people, the idea of a commercial entity pursuing exploration is naive at best. But I’d argue that long-term investors should be absolutely ecstatic about SpaceX having this mission as a forcing function. Firstly, it’s the key to getting the best people in the world to come and work for the organization and allow it to innovate in a manner and speed that others simply can’t match. That’s a huge competitive advantage.

Secondly, the way to get more cargo to Mars is actually about figuring out how to get more cargo into orbit around Earth, because that’s where the cost is all concentrated. It’s all in that first initial leap off the surface of our planet. So rather than framing Starship as a system that makes it possible to get to other planets, think about it instead being a system that could make it enormously more profitable to operate a business in Earth orbit and unlock brand new commercial use cases there as well…

…Luke

When we talk to SpaceX, they’re still very much focused on the here and now in the next couple of years. They have ambitions for things which they could do, but the focus is very much on the core business: serving the core customers, serving Starlink, getting Starship to launch status. We’ll deal with the next things next.

They’ve got so many things which they could be doing at the moment. When we come to this, a lot of this is us hypothesizing of how that could evolve beyond information which they’ve given us. The trend which you’ve seen of them to be vertical integrators could be quite informative. It might be that they end up being the ones who are commercializing a lot of these other services.

Rather than having a customer paying them for it at substantial scale, it would make more sense for them to do it. Could you start seeing some of these aspects? If they get into space-based manufacturing, for example, could that be priced on a value-added basis rather than a subscription basis or a volume basis? Certainly seems possible. If you start running data centers in space because it’s easier to power or cool them, etc., could you start offering data storage and machine learning alongside Starlink connectivity?

The further you look out, the more and more wacky it can get, but it’s also potentially financially plausible as well. You maybe have to take a bit of inspiration from science fiction here, but it’s quite a common trope in some of these movies of these large mega-corporations—the Weyland-Yutani Corporation from the Alien movies, or the Resources Development Administration from the Avatar films—where one mega-corporation was able to dominate access to space early on and then ends up controlling the entire extrasolar economy because of the advantages it had at that really early stage…

…Luke

The human spaceflight at the moment definitely has been the preserve of the rich and famous, but at scale that becomes cheaper and cheaper. And if we are talking about launching, Starship could be used as much for sending cargo and people to other points on the planet rather than other points in space. And so one option that the government’s looking into is this notion of rocket cargo delivery. Starship would be able to deliver 200,000 kg anywhere on the planet within 40 minutes.

What does that do for sort of a rapid reaction force, and what does that do for next-day delivery? At some stage, it’s going to be feasible to put a lot of astronauts or paying passengers on something like that, and it will be a quicker and potentially more efficient way to do long-distance travel. These things really could get quite wild, but it could be plausible at some stage. Again, that’s not the reason to invest in the company today; that’s not the basis of what they’re doing, and it’s a lot of people getting excited about things.

But come back in 10 years, I’d be disappointed if you or I weren’t able to go into space at some point in our lifetime for the cost of a premium economy ticket or something like that.

2. Japan vs Big Tech – Daye Deng

Put simply, US big tech has grown so dominant that it’s singlehandedly blowing a hole in the trade balance of a nation as large as Japan…

…In 2023, Japan recorded JPY 5.5 trillion in so-called digital trade deficit. The Ministry of International Trade and Industry (MITI) projects this to grow to JPY 8 trillion by 2030, at which point it could surpass Japan’s annual import of crude oil.

Japan’s total goods and services trade deficit in 2023 was JPY 6 trillion, with the digital deficit accounting for JPY 5.5 trillion…

…Japan has been in a structural deficit for goods trade over the past two decades. This may come as a surprise to those who have held onto the old idea that Japan is an export powerhouse.

There are several reasons for the shift:

• Japanese firms have moved production overseas. This isn’t entirely negative since Japanese firms (and their profits) continue to grow, but it has contributed to a widening trade deficit.
• Japan’s loss of global competitiveness in certain industries, like chips and appliances, to rivals such as South Korea.
• Rising cost of imports driven by energy shocks, rising overseas inflation, and weak yen.

The third point deserves elaboration. Japan’s reliance on imported energy has long been a critical structural weakness. For example, following 2011 Fukushima nuclear disaster, Japan significantly reduced domestic nuclear energy production and increased its reliance on imported LNG, becoming a major contributor to trade deficit.

A similar pattern emerged post-Covid. Global oil and commodity prices surged. This was compounded by high rates of overseas inflation on general imports. On top, a historically weak yen made imports even more expensive…

…Since 2014, the Japanese government has been disclosing the digital deficit, which has grown 2.6-fold from 2014 to JPY 5.5 trillion in 2023. This is a net figure derived from JPY 9.2 trillion paid for digital services and JPY 3.7 trillion received from abroad…

…The picture is quite clear: on the services side, Japan is taking its hard-earned surplus from tourism and spending it all on paying for digital services.

How will this play out? While I’m personally bullish on the Japanese tourism industry, it still has natural growth constraints. However, there is no ceiling on how much Japan can continue to spend on digital services. In fact, digital services spend could accelerate given:

• Japan is already playing catch-up in the digital realm, and is behind other major countries in many key digital metrics.
• AI is poised to make Japan’s digital dependency crisis even worse, in a world where firms like Nvidia and those that are able to scale AI services (e.g. hyperscalers) dominate AI economics.

Without an AI champion of its own, Japan has few options if it wants to avoid being left behind in the new digital paradigm…

…Based on our discussion so far, does it surprise you that the Japanese yen has been weak?

“According to an analysis by Mizuho Research & Technologies, if the digital deficit doubles from the 2023 level by the end of March 2026, it will add another 5 to 6 yen of depreciation in the Japanese currency’s value against the dollar.”

– Nikkei Asian Review

Or let me put it another way — would you feel bullish about the currency of a country that relies on tourism as its primary growing surplus, while ultimately funneling all those earnings (and more) into paying for essential energy imports and ever-increasing digital spend on big tech?…

…In recent years we’ve seen how hard Japan has been trying to reclaim its position in the semiconductor industry. But do they only care about hardware and not its digital sovereignty? Will Japan continue to sit back and let US tech giants profit endlessly, or will it finally confront its position as a digital colony?

3. Guyana and the mystery of the largest ranch in the Americas – Swen Lorenz

Many mistakenly believe that Guyana is located in Africa – when it’s actually nestled right next to Venezuela…

…In 2015, ExxonMobil discovered oil off the coast of Guyana.

The discovery changed the course of the country. Long one of the poorest nations of the Western hemisphere, Guyana has since become the world’s fastest growing economy.

Since 2015, its GDP per capita has more than quintupled. In 2022 and 2023, its economy grew by 67% and 33%, respectively. Another stunner of a year is forecast for 2024, with 34% GDP growth.

The former British colony benefits from a large amount of oil wealth spread around a relatively small population of 800,000 people. Per head, there is twice as much oil as in Saudi Arabia. To put things in perspective, Guyana’s landmass is nearly as big as the UK, but it only has 1.2% of the UK’s population…

…Just a week ago, ExxonMobil reported that it had reached 500m barrels of oil produced in Guyana since output began in 2019. The goal is to lift production to 1.3m barrels per day by 2027, up from currently 650,000 barrels. In comparison, the UK’s North Sea produces just 1m barrels per day…

…Supporters of the country’s energy projects claim that they will bring untold riches to the population. Indeed, Guyana recently started to hand out cheques to its citizens, including the Guyanese diaspora of 400,000 people, who the government encourages to come back as it needs more labour to support the strong economic growth.

4. Capital, Compute & AI Scaling – Patrick O’Shaughnessy, Chetan Puttagunta, and Modest Proposal

Modest

Everyone knows the Mag 7 represent a larger percent of the S&P 500 today. But beyond that, I think thematically AI has permeated far broader into industrials, into utilities and really makes up, I would argue, somewhere between 40 and 45% of the market cap as a direct play on this. And if you even abstract to the rest of the world, you start bringing in ASML, you bring in TSMC, you bring in the entire Japanese chip sector. And so if you look at the cumulative market cap that is a direct play on artificial intelligence right now, it’s enormous…

… I think at the micro level this is a really powerful shift if we move from pre-training to inference time and there are a couple big ramifications.

One, it better aligns revenue generation and expenditures. I think that is a really, really beneficial outcome for the industry at large, which is in the pre-training world you were going to spend 20, 30, $40 billion on CapEx, train the model over 9 to 12 months, do post-training, then roll it out, then hope to generate revenue off of that in inference. In a test time compute scaling world you are now aligning your expenditures with the underlying usage of the model. So just from a pure efficiency and scalability on a financial side, this is much, much better for the hyperscalers.

I think a second big implication, again we have to say we don’t know that pre-training scaling is going to stop. But if you do see this shift towards inference time, I think that you need to start to think about how do you re-architect the network design? Do you need million chip super clusters in energy low-cost land locations or do you need smaller, lower-latency, more efficient inference-time data centers scattered throughout the country? And as you re-architect the network, the implications on power utilization, grid design?

A lot of the, I would say, narratives that have underpinned huge swaths of the investment world I think have to be rethought and I would say today because this is a relatively new phenomenon, I don’t believe that the public markets have started to grapple with what that potential new architecture looks like and how that may impact some of the underlying spend…

… Chetan

But at the moment, at this plateauing time, we’re starting to see these small teams catch up to the frontier. And what I mean by frontier is where are the state-of-the-art models, especially around text performing? We’re seeing these small teams of quite literally two to five people jumping to the frontier with spend that is not one order, but multiple orders of magnitude less than what these large labs were spending to get there.

I think part of what’s happened is the incredible proliferation of open-source models. Specifically, what Meta’s been doing with LLaMA has been an extraordinary force here. LLaMA 3.1 comes in three flavors, 405 billion, 70 billion, 8 billion. And then LLaMA 3.2 comes in 1 billion, 3 billion, 11 billion, and 90 billion.

And you can take these models, download them, put them on a local machine, you can put them in a cloud, you can put them on a server, and you can use these models to distill, fine-tune, train on top of, modify, et cetera, et cetera, and catch up to the frontier with pretty interesting algorithmic techniques.

And because you don’t need massive amounts of compute, or you don’t need massive amounts of data, you could be particularly clever and innovative about a specific vertical space, or a specific technique, or a particular use case to jump to the frontier very, very quickly…

…Chetan

The force of Llama today has been two things, and I think this has been very beneficial to Meta is one. The transformer architecture that Llama is using is a sort of standard architecture, but it has its own nuances.

And if the entire developer ecosystem that’s building on top of Llama is starting to just assume that that Llama 3 transformer architecture is the foundational and sort of standard way of doing things, it’s sort of standardizing the entire stack towards this Llama way of thinking, all the way from how the hardware vendors will support your training runs to the hyperscalers and on and on and on. And so standardizing on Llama itself is starting to become more and more prevalent.

And so if you were to start a new model company, what ends up happening is starting with Llama today is not only great because Llama is open source, it’s also extraordinarily efficient because the entire ecosystem is standardizing on that architecture…

…Modest

So I think the interesting part for OpenAI was because they just raised the recent round and there was some fairly public commentary around what the investment case was. You’re right, a lot of it oriented around the idea that they had escape velocity on the consumer side and that ChatGPT was now the cognitive reference and that over time they would be able to aggregate an enormous consumer demand side and charge appropriately for that and that it was much less a play on the enterprise API and application building.

And that’s super interesting if you actually play out what we’ve talked about when you look at their financials, if you take out training runs, if you take out the need for this massive upfront expenditure, this actually becomes a wildly profitable company quite quickly in their projections. And so in a sense it could be better.

Now then the question becomes what’s the defensibility of a company that is no longer step function advancing on the frontier?…

…Chetan

These products are truly, as a software investor, absolutely amazing.

They require a total rethinking from first principles on how these things are architected. You need unified data layers, you need new infrastructure, you need new UI and all this kind of stuff. And it’s clear that the startups are significantly advantaged against incumbent software vendors. And it’s not that the incumbent software vendors are standing still, it’s just that innovator’s dilemma in enterprise software is playing out much more aggressively in front of our eyes today than it is in consumer.

I think in consumer, the consumer players recognize it, are moving it, and are doing stuff about it. Whereas I think in enterprise, even if you recognize it, even if you have the desire to do something, the solutions are just not built in a way that is responsive to dramatic re-architecture. Now could we see this happening? Could a giant SaaS company just pause selling for two years and completely re-architect their application stack?

Sure, but I just don’t see that happening. And so if you just look at any sort of analysis on what’s happening on AI software spend, something like it’s 8x year-over-year growth between 2023 and 2024 on just pure spend. It’s gone from a couple of hundred million dollars to well over a billion in just a year’s time…

…Modest

If you listen to AWS, one of the fascinating things they say is they call AWS a logistics business.

I don’t think anyone externally would sort of look at cloud computing and say, oh yeah, that’s a logistics business. But their point is essentially what they have to do is they have to forecast demand and they have to build supply on a multi-year basis to accommodate it.

And over 20 years they’ve gotten extraordinarily good at what has happened in the last two years, and I talked about this last time, is you have had an enormous surge in demand hitting inelastic supply because you can’t build data center capacity in three weeks. And so if you get back to a more predictable cadence of demand where they can look at it and say, okay, we know now where the revenue generation is coming from.

It’s coming from test time, it’s coming from Chetan and his companies rolling out. Now we know how to align supply with that. Now it’s back to a logistics business. Now it’s not grab every mothballed nuclear site in the country and try to bring it online.

And so instead of this land grab, I think you get a more reasonable, sensible, methodical rollout of it maybe. And I actually would guess that if this path is right, that inference overtakes training much faster than we thought and gets much bigger than we may have suspected.

But I think the path there in the network design is going to look very different and it’s going to have very big ramifications for the people who were building the network, who were powering the network, who were sending the optical signals through the network. And all of that, I think, has not really started to come up in the probability-weighted distributions of a huge chunk of the public market.

And look, I think most people overly fixate on NVIDIA because they are sort of the poster child of this, but there are a lot of people downstream from NVIDIA that will probably suffer more because they have inferior businesses. NVIDIA is a wonderful business doing wonderful things. They just happen to have seen the largest surge in surplus. I think that there are ramifications far, far beyond who is making the bleeding edge GPU, even though I do think there will be questions about, okay, does this new paradigm of test time compute allow for customization at the chip level much more than it would have if we were only scaling on pre-train…

…Modest

If you think about a training exercise, you’re trying to utilize them at the highest possible percent for a long period of time. So you’re trying to put 50, 100,000 chips in a single location and utilize them at the highest rate possible for nine months. What’s left behind is a hundred thousand chip cluster that if you were to repurpose for inferencing is arguably not the most efficient build because inference is peaky and bursty and not consistent.

And so this is what I’m talking about that I just think from first principles you are going to rethink how you want to build your infrastructure to service a much more inference focused world than a training focused world. And Jensen has talked about the beauty of NVIDIA is that you leave behind this in place infrastructure that can then be utilized.

And in a sunk cost world you say, sure, of course if I’m forced to build a million chip supercluster in order to train a $50 billion model, I might as well sweat the asset when I’m done. But from first principles it seems clear you would never build a 350,000 chip cluster with 2 1/2 gigawatts of power in order to service the type of request that Chetan’s talking about.

And so if you end up with much more edge computing with low latency and high efficiency, what does that mean for optical networking? What does that mean for the grid? What does that mean for the need for on site power versus the ability to draw from the local utility?…

…Chetan

Semiconductor company called Cerebras, and they recently announced that inference on Llama 3.1 405 billion for Cerebras is it can generate 900-plus tokens per second, which is a dramatic order-of-magnitude increase. I think it’s like 70 or 75 times faster than GPUs for inference as an example. And so as we move to the inference world, the semiconductor layer, the networking layer, et cetera, there’s tons of opportunities for startups to really differentiate themselves…

…Modest

On a less sort of dramatic view, the way I think about this, there’s AlphaGo, which famously did that move that no one had ever seen, and I think it’s like move 37, everybody was super confused about, ended up winning. And another example I love is Noam Brown, because I like poker, talked about his poker bot confused—it was playing high stakes, no limit, and it continually over-bet dramatically larger sizes than pros had ever seen before.

And he thought the bot was making a mistake. And ultimately it destabilized the pros so much. Think about that. A computer destabilized humans in their approach that they have to some extent taken on over-betting now into their game.

And so those are two examples where if we think about pre-training being bounded by the data set that we’ve given it, if we don’t have synthetic data generation capabilities, here you have two examples where algorithms did something outside of the bounds of human knowledge. And that’s what’s always been confusing to me about this idea that LLMs on their own could get to superintelligence, is functionally they’re bounded by the amount of data we give them up front.

5. Will China Take Over the Global Auto Industry? – Brad Setser

China has, according to the New York Times, the capacity to produce over 40 million internal combustion engine (ICE) cars a year.

Goldman Sachs thinks China will also have the capacity to produce around 20 million electric vehicles by the end of 2024…

…China’s internal market is around 25 million cars, and not really growing —so rising domestic EV sales progressively frees up internal combustion engine capacity for export.   Domestic demand for traditional cars is likely to be well under 10 million cars next year given the enormous shift toward EVs now underway inside China…

…Historically, the autos market has been largely regional (setting aside trade in luxury cars, where volumes are smaller). Most cars sold in China were made in China, most cars sold in Europe are produced in Europe, most cars sold in the North America are produced in North America, and so on. The U.S. did import a few million cars, on net, from Asia, and China imported a million or so luxury cars from Europe, but those were the exceptions rather than the rule.

That could change, absent hefty restrictions on Chinese auto imports (like the 100 percent tariff the U.S. now levies on EVs imported from China).

The global market—with massive overcapacity in China’s internal combustion engine (ICE) sector, massive capacity expansion in China’s EV sector, effectively unlimited credit for Chinese manufacturing firms from China’s state banks, and a Chinese yuan that is weaker against the dollar than it was back in 2008—is pushing for global auto manufacturing to become more like global electronics manufacturing, with a concentration of global production in a single region and, for that matter, a single country…

…Overcapacity in China’s automotive sector is not, in fact, all that new.

China’s traditional automotive sector was dominated by the joint ventures (“JVs”) formed by the large foreign firms and their (typically state-owned) Chinese partners. Chinese auto demand took off after the global financial crisis, and global firms responded by massively expanding their Chinese production capacity – as only the German luxury markets were interested in paying the 25 percent tariff and supplying the Chinese market from abroad.

But demand growth eventually slowed, and by 2018, the Wall Street Journal was reporting that the Chinese market was oversupplied…

…China’s EV industry—like EV industries in the U.S. and Europe—initially received substantial state backing. Chinese EV manufactures benefitted from downstream subsidies that built out China’s battery and battery chemical industry, as well as access to the world’s cheapest steel.
EV firms benefitted from cheap state financing—both equity injections from a myriad of state-backed funds and loans from state banks who (still) have to meeting lending quotas.

Moreover, China was quite explicitly protectionist in the application of its “consumer” EV subsidies.

Only EVs that were on state lists of qualifying vehicles were eligible for the subsidy, and the subsidy was only provided to cars that were made in China…

…And initially, only cars that were made in China with a battery made in China by a Chinese firm qualified for the lists…

…The only exception to the basic rule that qualifying for the list required using a battery made in China by a Chinese firm only confirmed the broad pattern of discrimination: Chinese-owned Volvo was allowed to use a Korean battery in one of its early EVs.

State support has not disappeared in any way as China’s EV industry took off.   Looking at direct cash subsidies from the central government to the manufacturers misses the myriad of ways China, Inc helps out firms producing in China…

…Nio received a significant ($1.9 billion) equity investment from the City of Hefei and the Province of Anhui, helping to offset ongoing losses. That equity injection was on top of state support for a factory in Hefei, which The New York Times reports was effectively a gift from the local government.

“‘The local government provided the land and the building’, said Ji Huaqiang, Nio’s vice president for manufacturing. ‘Nio does not own the factory or the land — it is renting, but the factory was custom built for Nio’”

That kind of support explains how Nio managed to build out its EV capacity even when its existing factories weren’t really being used that much:

“Nio’s two factories give it the capacity to assemble 600,000 cars a year, even though its annual rate of sales this autumn [2023] is only about 200,000 cars. Nio is nonetheless already building a third plant.”…

...What’s even more striking is that the investments that built out China’s EV capacity came in a market that was already saturated with modern auto production capacity.  That kind of investment wouldn’t have taken place without state guidance and support, support that was intended both to develop an indigenous Chinese industry (See China 2025) and to support a green transition that would reduce Chinese dependence on import fossil energy. It was the result of policy driven by the central government and backed financially by all levels of government. It also worked, China is now the world leader in EVs and batteries…

…If the world’s global firms can only compete with Chinese firms by using Chinese batteries and Chinese parts, that will hollow out much of the automotive industries of Europe and North America—a European brand on a Chinese-made car with a Chinese battery and drive train won’t sustain the current European auto supply chain or current European employment in the auto industry.

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Disclaimer: The Good Investors is the personal investing blog of two simple guys who are passionate about educating Singaporeans about stock market investing. By using this Site, you specifically agree that none of the information provided constitutes financial, investment, or other professional advice. It is only intended to provide education. Speak with a professional before making important decisions about your money, your professional life, or even your personal life. We currently have a vested interest in ASML, Meta, 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 03 November 2024:

1. We Need to Have a Talk About “Bond Vigilantes” – Cullen Roche

In May the 10 year interest rate was as high as 4.75%. By September it was as low as 3.6%. Today it’s bounced back to 4.2%. And as rates tick higher in recent months there’s been a growing chorus about how “bond vigilantes” are going to teach the Fed a lesson. This has been especially loud coming from Stanley Druckenmiller, Paul Tudor Jones and Elon Musk. The basic narrative is that bond markets will teach the Fed and US government a lesson about reckless spending which will drive up interest rates and bankrupt the USA. Except there’s a huge problem in this narrative – the bond market has a lot less control over interest rates than this story would have you believe…

…Individuals go bankrupt. Large aggregated sectors do not. For example, the aggregated private sector cannot go bankrupt. The US government is a huge aggregated sector in the US economy. It does not go bankrupt. In the aggregate it can print money to fund all spending and it cannot run out of this money unless it borrows in a foreign currency, which it doesn’t do. Of course, it can cause wildly huge inflation. We know that after Covid, but comparing the Federal Government to an individual is just wrong…

…The Fed is the reserve monopolist. So, as I’ve explained in the past, they have absolute control over something like short-term interest rates. The market quite literally cannot force them to change this rate because the market cannot compete with the Fed. If a bank tries to set the short-term interest rate the Fed just comes in and smashes them with their bottomless pit of money…

…The best way to understand the yield curve is to think of the Fed as a dog walker who has absolute control of the leash at the handle and allows the dog to wander further out the leash. The Fed has absolute control over the handle (the Fed Funds Rate) and lets the 30 year wander from side to side, but still within a certain control. It might look like the dog is walking the Fed, but the Fed always has the ability to pull that leash in and grab that dog by the neck. This is what “yield curve control” would look like and if the Fed entered the market for 30 year bonds and started explicitly setting a target they could drive that rate to whatever they wanted. But they let it float a bit…

…I wrote nearly this exact same article 11 years ago in response to a WSJ op-ed in which Druckenmiller said the exact same thing. He said the USA was bankrupt and that the Fed was too loose…

… In my humble opinion, the error in this analysis is two-fold:

1. Assuming that interest payments are problematic – they are not because the Fed can control them with a dial and also because high rates put DOWNWARD pressure on inflation.
2. Assuming high inflation must result from government deficits. I think it’s absolutely true that large deficits put upward pressure on inflation. I’ve said this a billion times during Covid. But government spending is 23% of GDP. That’s the same level it was at in 1982! And while it’s a large portion of aggregate spending we should remember that 77% of spending is coming from OTHER sources. In most cases, it’s much more efficient sources such as the most efficient corporate machine the human race has ever seen (corporate America).

2. You’re Not Paranoid. The Market Is Out to Get You – Jason Zweig

Graham wasn’t only one of the best investors of all time; he may have been the wisest. His intellectual brilliance, six decades of investing and study of history gave him a profound understanding of human nature.

As he wrote: “The investor’s chief problem—and even his worst enemy—is likely to be himself.”…

…To be an intelligent investor doesn’t require a stratospheric IQ. It does require discipline and the ability to think for yourself.

As Graham pointed out, individual investors are “scarcely ever” forced to sell stocks or funds and—unlike professional portfolio managers who are continually measured against the market—are never compelled to care what other investors are doing.

That independence is your single most valuable asset, a luxury most professional investors can only dream of possessing. It’s what Graham called the “basic advantage” of the intelligent investor. But, he warned, “the investor who permits himself to be stampeded [by other people’s behavior]…is perversely transforming his basic advantage into a basic disadvantage.”

As I argue in the new edition of the book, it has never been harder to be a disciplined and independent investor. In today’s incessantly twitchy, infinitely networked markets, the siren song of smartphones, social media and streaming video can tempt you to trade more and copy the crowd.

After all, it often makes sense—and just feels right—to join the herd…

…Yet crowds aren’t always right, and their errors are contagious. What separates the wisdom from the madness of the crowd?

In 1907, the statistician Francis Galton described a contest at an agricultural fair in which nearly 800 visitors tried to guess the weight of an ox. Although many knew little or nothing about oxen and their guesses varied widely, their average estimate turned out to match the weight of the ox exactly.

Galton’s guessers had a variety of viewpoints, sought to win a prize for accuracy, didn’t know other people’s estimates and had to pay an entry fee. The sponsors of the contest collected and tallied all the guesses.

The judgments of that crowd were independent, confidential, diverse, incentivized and aggregated—and, therefore, remarkably accurate at estimating simple values.

But the judgments of today’s crowds are often the opposite of Galton’s…

…The weight of an ox doesn’t change with people’s estimates of it. However, if thousands of speculators decide a stock or cryptocurrency is worth $100,000, it will skyrocket—at least temporarily—even if it’s worthless.

Joining the crowd can change how you think, no matter how much you pride yourself on your independence. That’s especially insidious because it occurs subconsciously.

One recent study found that investors on social media are five times more likely to follow users who agree with them and will see nearly three times more messages they agree with than disagree with. Falling into such an echo chamber, the study showed, leads people to trade more—and earn lower returns.

Meanwhile, bucking the consensus engages circuits in the brain that generate pain and disgust. Experiments have shown that when you find out your peers disagree with you, your choices become up to three times more likely to match theirs, although you have no conscious awareness of being influenced…

…If you have views about which asset or investing strategy is right for you, write down your reasoning before you explore what some online group is saying. Take no action without reviewing your original rationale and determining that there’s a reasonable basis for changing it—grounded in independently verifiable evidence, not just the opinions of random people online.

Use a checklist to focus on the stability of the underlying business rather than share-price movements. Have I read the company’s financial reports? Do its executives admit mistakes, use conservative accounting and avoid hype? Have I written down at least three reasons why this is a good business that will be even better five years from now? What, exactly, do I understand about this company that most other investors are missing, and why?

3. Why the Fed Cut Rates and Mortgage Rates Jumped – Joe Weisenthal, Tracy Alloway, and Tom Graff

Joe (05:45):

But I actually have to refinance a mortgage in a couple of years. I could do it today, I guess, but I have to do it at some point. Alright. Government 30-year yields are 4.3%, 4.32% as we’re talking right now. I’ll probably want to get a 30-year fixed. Why can’t I just borrow at 4.32% if the government is already backstopping it?

Tom (06:05):

Well, so the key difference between a mortgage bond and a Treasury bond is that, in the United States, virtually all mortgages and all the ones that Fannie Mae and Freddie Mac back can be refinanced at any time without any penalty.

Joe (06:18):

Can’t I just promise not to? Well, I guess because I can always sell the house or something like that.

Tom (06:20):

Yeah. You can’t do that, Joe. And so, from an investor perspective, what that means is, if interest rates rise, no one refinances, everyone just stays where they are. Witness all the people kind of stuck in 2.5%, 3% mortgages right now, right? And so, those mortgages just stay outstanding and they might stay outstanding for 30 years for all we know, right?

Whereas if interest rates fall, you kind of don’t get any of the benefits. So if I buy a 30-year Treasury and interest rates drop, I can make 10%, 15%, 20% price appreciation as that happens. But in a mortgage bond, if interest rates fall, everyone just refinances, I just get all my money back at par, I’m no better off. And so you got to get paid for that, what — we’ll get into it —but what’s called negative convexity, you’ve got to get paid for that risk and that’s why there’s a spread between mortgage bonds and Treasury bonds…

…Tracy (09:53):

So talk to us about what goes into producing a mortgage rate. So if I want to buy a house and I go to a bank and I ask for a mortgage, what are the individual factors that go into the number that eventually gets quoted back to me?

Tom (10:07):

Okay. Yeah. So let’s assume for sake of argument, this is a loan that conforms to Fannie and Freddie’s standards. because That’s the ones we’re talking about here. So assuming that right? Your bank has to pay Fannie or Freddie a guarantee fee. The G-Fee. And that is based on your credit situation. So how much you’re putting down, what your credit score is, that sort of thing. And it’s all algorithmic. So they’re just typing it into a computer, Fannie and Freddie kicking back, here’s the rate, right?

Then they’re also going to think to themselves, okay, well where can I sell this mortgage? Right? What price am I going to get when I sell it in the open market? And that depends mostly on just what the general price is for the going rate for mortgages, but it might depend a little on your situation so we can get into it how certain kinds of mortgages command a bit more of a premium in the market than others and that will go into the rate you’re going to get quoted. And so every night the bank’s mortgage desk is sort of plugging in, hey, for mortgage like this, we’ll we’ll offer this rate for mortgage like that we’ll offer this rate and all these factors are going into that. So when your loan officer’s typing this into his computer, that’s what’s spitting out, right?

Joe (11:17):

Actually, let’s back up. What makes a mortgage conforming versus non-conforming?

Tom (11:21):

The biggest thing is the price. So the price relative to used to be a hard number, but now Fannie and Freddie do it relative to your sort of MSA or what, what your area? Yeah.

Joe (11:31):

So wait, above a certain price? Can you go into that a little further? Above a certain price, Fannie and Freddie just won’t back?

Tom (11:37):

Yeah. They’re just not backing it. And that has to do with their mandate from Congress to be about affordable housing…

…Tracy (25:56):

So I’m going to ask the question that I’m sure is on everyone’s minds per that Google trends chart, but when do mortgages come down?

Joe (26:04):

Yeah, or what will it take at least?

Tom (26:06):

Yeah, so, so we should, let’s, let’s talk about why they’ve risen since that Fed meeting, and then I think that’ll inform where they’re headed, right. So look, the 10-year Treasury is not a function of where the Fed is today. It’s a function of where people anticipate the Fed being in the next year, two, three. Beyond three, it’s sort of fuzzy, but like, you know, year two we sort of have a sense we can make a guess.

And so going into that September meeting, people started thinking themselves, boy Fed may cut 50 basis points in September, 50 basis points in November, maybe even 50 more basis points in December, right? If you pull up your WIRP chart on the Terminal, you can see this, right? If you go back to then, but since then what happened, we got a big jobs report the beginning of October. That was the September report, but came out in October.

And that was kind of a game changer because not only did we get a solid number for September, but it was huge upward revisions kind of erased what looked like a downward trend in hiring, right? Well now all of a sudden we’re like, boy, the Fed might be a lot closer to that neutral rate than we think, right? Eh, probably going to still cut in November, but maybe they’ll cut in December, maybe they won’t. But if they do, it’s certainly not going to be 50 basis points unless something changes.

And so that change in expectations has caused the tenure to rise. So commensurately the mortgage rate has risen, right? And so from that story you can say, all right, well it becomes pretty easy to see what’s going to cause mortgage rates to drop, the tenure needs to drop, right? And what’s going to cause the tenure to drop? Well, we’re going to need more Fed cuts priced in. Well what’s going to cause more Fed cuts to get priced in? We need the economy to get weaker.

4. An Interview with Hugo Barra About Orion and Meta’s AR Strategy – Ben Thompson and Hugo Barra

HB: Yeah, and this is worth taking a step back and talking about in a bit of detail, because there’s a few things that we don’t think about too much. The thing that annoys me having worked on smartphones for the last 15 plus years is that our smartphones make us work too hard. These workflows, these mobile app workflows are too repetitive, they’re not smart, they treat us generically.

It doesn’t make any sense that this world will continue for a lot longer and we know that AI is going to fundamentally change this. All apps are going to become agentic. Think of developers writing apps in their respective agents. Agents will make it possible to have much, much simpler workflows, which are highly, highly personalized. They’re still being rendered by the app, but the flows themselves are highly personalized, they have a much lower burden on users. Agents can do a lot of the prediction and anticipation and pre-thinking on a user’s behalf so that everything is boiled down to hopefully a small number of simple choices or no choices at all.

Oh, here you go, I have an analogy, you have to tell me if this fits what you’re going for here. So arguably the ultimate agentic experience that people experience right now, even though they don’t realize it, are their social media feeds, in that the feed is perfectly customized to serve up to you the entertainment that it thinks you want at every moment, and it actually turns out based on engagement numbers, it works pretty well, and while people claim they want a chronological timeline or whatever, that’s like saying you want a grid of apps and the reality is revealed preferences says that no, they don’t want that. Is that a good analogy for what you’re going for?

HB: I think that’s halfway there. I would say an agentic version of Instagram is going to be a little bit different. Instagram thinks it’s pretty smart, but it doesn’t have a lot of context from your life. As much as people say that Instagram listens to their conversations, that’s not true.

If only it did.

HB: Exactly. If only it did, it’d be great, but it of course doesn’t. So Instagram, to use an example, knows very little about you relatively speaking, about the broader context of your life. So it’s like a poor man’s agent that tries to represent your interests and serve what you want. A true agentic version of Instagram has an agent that represents what you want and can do a much better job ranking, filtering the content that you see at any point in time based on a bunch of other things, and it’s very tricky because Instagram can’t know about these things, because if they do, they will create this massive profile about you. So there’s a whole new architecture of the Internet that will have to be invented for these agentic capabilities to become unleashed because you have to keep your data…

…HB: Yeah, and this is where we get into I think the meat of the topic, which is what does a world of AR apps look like? What does it feel like to live in it? I’ve used this YouTube video called Hyper-Reality multiple times when I’ve given talks on AR. It’s completely absurd, it’s a world that we don’t want to live in, but it’s a joke, but it’s also not. So I always encourage people to go watch Hyper-Reality, it’s a beautiful artistic piece.

Before we get into what living in this AR world looks like, there’s a couple of things that I always like to talk about. One is that direct manipulation, which is what Apple brought to the world with multi-touch — we’ve had other forms in the past, but that’s really when it arrived — is genius and it has and will continue to exist, and direct manipulation when you’re literally touching something with your fingers has to be tactile, meaning it requires a physical surface. Pinch-to-zoom in midair isn’t nearly as useful as something on a tactile hard surface, so that’s the first thing.

The second thing is our arms get tired. This idea of midair computing is only really useful for quick actions. There’s this hilarious scene in Minority Report where Tom Cruise is probably sweating by making lots and lots of gestures in midair, and perhaps ahead of their time in their vision, but that’s not a thing, people don’t want to be computing in midair.

And look, if Tom Cruise can’t do it, none of us can do it.

HB: (laughing) Exactly. So anyway, we have to keep those things in mind. Direct manipulation is genius and your arms get tired, so there are three modalities of UI and UX in the Spatial Computing paradigm. The first are your tools, they’re like your utility belt, they’re things that walk with you wherever you go. They might be body-locked or in some cases head-locked, your notifications tray, your settings, your menu, etc., these things walk with you where you go, and you will access them through both 2D gestures and 3D gestures. But it’s all quick, it’s just how you get into the thing that you want to do.

Right, this is almost like the mechanical wristwatch of UI layers.

HB: Exactly right. So that’s your utility belt, we’re going to bring that with you everywhere.

The second thing are world-anchored apps. So it’s basically walking to your house and instantiating an app on your table sitting down and then playing with it. That app might be a 2D iPad style app, it might be a 2D app on a massive surface, it might be a little 3D app, like a tabletop app. Imagine calling an Uber using a tabletop 3D map that allows you to say exactly where you want to get picked up.

Right. You can pick up the car and put it on the map where you want it.

HB: You can put it on the map. So this is really useful because you can instantiate any app on any surface at any time.

Then the third thing, which is where it gets really exciting, are world-anchored virtual objects and maybe screens as well. So these are things that are just in the world. You walk into your house, you’ve got art on the walls, you’ve got maybe a control panel where ordinarily a light switch would be, and it allows you to do all sorts of things with your house because it’s not actually there, it’s just the wall. But you see something, you see a control panel on the wall that’s rendered for you and it will be agentic, etc., all that stuff.

That’s like real augmented reality because you are actually augmenting reality.

HB: Yes, this is real augmented reality. Think about annotating the world as well. You saw in the Orion demo the recipe thing where it annotates the ingredients and visually tracks them so if you walk away and look back, it’s still tracking them, they’re still there. This is crazy interesting stuff, and that’s where a lot of the new types of use cases are going to come from, and that’s it. Those are the three categories of UI in an augmented reality world…

…Yeah, this is the challenge here. You have a couple Apple points here, the one thing about linking it to the smartphone is, if you can offload all that compute and offload all that battery and offload all that connectivity into one device, it makes it a lot easier. I mean, you said for Apple, “Number eight, Apple will continue to slow-follow Meta on camera glasses and mixed reality headsets, but will be several years behind on AR glasses”.

HB: Yeah. I think that it’s a really easy win for Apple to launch a competitor to the Ray-Ban glasses. I mean, it’s a proven form factor, just do it and I think they’ll do a fantastic job at it. It makes total sense because of Apple Visual Intelligence. It’s just, just do it. So that was a rumor from Mark Gurman from I think last week, which I really believe in. The earbuds with cameras, I’m not so sure, but I do believe that camera glasses are a thing that makes sense for Apple to be building.

Now, the AR glasses though is not, in my opinion, a product that we’re going to see from Apple in much less than 10 years.

Wow.

HB: I think that one is going to take a very, very, very long time.

And why is that?

HB: I just think their product bar is going to be insanely high, and I think they’re going to have some hard architectural decisions to make. Is it attached to your iPhone as an accessory or is it a standalone thing with its own puck like Meta did? There’s a lot of trade-offs there that I think people don’t necessarily think about carefully. It is not easy for Apple, that’s my next point.

Number nine.

HB: To ship AR glasses as a smartphone accessory, because in practice they have significant cost margin, thermal envelope constraints on the iPhone because the iPhone is a single, super high volume product that needs to be a great product and a highly profitable smartphone, first and foremost, so as soon as you have to start to add more components to this thing to power AR glasses, you’re tasking your primarily profit center for the whole company and creating all these architectural constraints.

Couldn’t they just make an extra model like the AR model? But I guess then that ruins your TAM.

HB: Yeah, I think that’s like the worst of all worlds, in my opinion.

This is really interesting, 10 years does blow my mind because yeah, your thought immediately, let me restate your argument, make sure I get it, your thought immediately goes to Apple already has a smartphone, they can just do an accessory, but actually the issue is the smartphone is so successful and so profitable and so essential that, 100 million, is that in a quarter, whatever, all those smartphones can’t be compromised to support this because they’re so important.

HB: And the attach rate just doesn’t justify it.

That’s right.

HB: Look at the attach rate of Apple Watch, the attach rate of Apple Watch is still fairly small.

And yet if you did a separate model, you’re giving away your entire advantage so you’re stuck.

HB: Exactly. So I think it’s a harder trade-off space than people realize for Apple, and my guess is that this is a discussion that is highly unresolved.

5. Researchers say an AI-powered transcription tool used in hospitals invents things no one ever said –  Garance Burke and Hilke Schellmann

Tech behemoth OpenAI has touted its artificial intelligence-powered transcription tool Whisper as having near “human level robustness and accuracy.”

But Whisper has a major flaw: It is prone to making up chunks of text or even entire sentences, according to interviews with more than a dozen software engineers, developers and academic researchers…

…More concerning, they said, is a rush by medical centers to utilize Whisper-based tools to transcribe patients’ consultations with doctors, despite OpenAI’ s warnings that the tool should not be used in “high-risk domains.”

The full extent of the problem is difficult to discern, but researchers and engineers said they frequently have come across Whisper’s hallucinations in their work. A University of Michigan researcher conducting a study of public meetings, for example, said he found hallucinations in eight out of every 10 audio transcriptions he inspected, before he started trying to improve the model.

A machine learning engineer said he initially discovered hallucinations in about half of the over 100 hours of Whisper transcriptions he analyzed. A third developer said he found hallucinations in nearly every one of the 26,000 transcripts he created with Whisper.

The problems persist even in well-recorded, short audio samples. A recent study by computer scientists uncovered 187 hallucinations in more than 13,000 clear audio snippets they examined.

That trend would lead to tens of thousands of faulty transcriptions over millions of recordings, researchers said…

…The tool is integrated into some versions of OpenAI’s flagship chatbot ChatGPT, and is a built-in offering in Oracle and Microsoft’s cloud computing platforms, which service thousands of companies worldwide. It is also used to transcribe and translate text into multiple languages…

…Professors Allison Koenecke of Cornell University and Mona Sloane of the University of Virginia examined thousands of short snippets they obtained from TalkBank, a research repository hosted at Carnegie Mellon University. They determined that nearly 40% of the hallucinations were harmful or concerning because the speaker could be misinterpreted or misrepresented.

In an example they uncovered, a speaker said, “He, the boy, was going to, I’m not sure exactly, take the umbrella.”

But the transcription software added: “He took a big piece of a cross, a teeny, small piece … I’m sure he didn’t have a terror knife so he killed a number of people.”

A speaker in another recording described “two other girls and one lady.” Whisper invented extra commentary on race, adding “two other girls and one lady, um, which were Black.”

In a third transcription, Whisper invented a non-existent medication called “hyperactivated antibiotics.”

Researchers aren’t certain why Whisper and similar tools hallucinate, but software developers said the fabrications tend to occur amid pauses, background sounds or music playing…

…Over 30,000 clinicians and 40 health systems, including the Mankato Clinic in Minnesota and Children’s Hospital Los Angeles, have started using a Whisper-based tool built by Nabla, which has offices in France and the U.S.

That tool was fine-tuned on medical language to transcribe and summarize patients’ interactions, said Nabla’s chief technology officer Martin Raison.

Company officials said they are aware that Whisper can hallucinate and are addressing the problem.

It’s impossible to compare Nabla’s AI-generated transcript to the original recording because Nabla’s tool erases the original audio for “data safety reasons,” Raison said.

Nabla said the tool has been used to transcribe an estimated 7 million medical visits.

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Disclaimer: The Good Investors is the personal investing blog of two simple guys who are passionate about educating Singaporeans about stock market investing. By using this Site, you specifically agree that none of the information provided constitutes financial, investment, or other professional advice. It is only intended to provide education. Speak with a professional before making important decisions about your money, your professional life, or even your personal life. We currently have a vested interest in Apple, Meta Platforms, and Microsoft. Holdings are subject to change at any time.

JPMorgan Chase (NYSE: JPM) is currently the largest bank in the USA by total assets. Because of this status, JPMorgan is naturally able to feel the pulse of the country’s economy. The bank’s latest earnings conference call – for the third quarter of 2024 – was held last week and contained useful insights on the state of American consumers and businesses. The bottom-line is this: the world remains treacherous, but the US economy – and the consumer – remains on solid footing 

What’s shown between the two horizontal lines below are quotes from JPMorgan’s management team that I picked up from the call.

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1. The geopolitical situation looks treacherous to JPMorgan’s management, and could have major impacts on the economy in the short term

We have been closely monitoring the geopolitical situation for some time, and recent events show that conditions are treacherous and getting worse. There is significant human suffering, and the outcome of these situations could have far-reaching effects on both short-term economic outcomes and more importantly on the course of history.

2. The US economy remains resilient, but there are risks; JPMorgan’s management wants to be prepared for any environment, as they think the future can become quite turbulent

While inflation is slowing and the U.S. economy remains resilient, several critical issues remain, including large fiscal deficits, infrastructure needs, restructuring of trade and remilitarization of the world. While we hope for the best, these events and the prevailing uncertainty demonstrate why we must be prepared for any environment…

…I’ve been quite clear that I think things — or the future could be quite turbulent. 

3. Net charge-offs for the whole bank (effectively bad loans that JPMorgan can’t recover) rose from US$1.5 billion a year ago; Consumer & Community Banking’s net charge offs rose from US$0.5 billion a year ago

Credit costs were $3.1 billion, reflecting net charge-offs of $2.1 billion and a net reserve build of $1 billion, which included $882 million in Consumer, primarily in Card and $144 million in Wholesale. Net charge-offs were up $590 million year-on-year, predominantly driven by Card…

…In terms of credit performance this quarter, credit costs were $2.8 billion driven by Card and reflected net charge-offs of $1.9 billion, up $520 million year-on-year and a net reserve build of $876 million predominantly from higher revolving balances.

4. JPMorgan’s credit card outstanding loans was up double-digits

Card outstandings were up 11% due to strong account acquisition and the continued normalization of revolve. 

5. Auto originations are down

In Auto, originations were $10 billion, down 2%, while maintaining strong margins and high-quality credit. 

6. JPMorgan’s investment banking fees had strong growth in 2024 Q3, signalling higher appetite for capital-markets activity from companies; management is cautiously optimistic about companies’ enthusiasm towards capital markets activities, but headwinds persist 

IB fees were up 31% year-on-year, and we ranked #1 with year-to-date wallet share of 9.1%. And advisory fees were up 10%, benefiting from the closing of a few large deals. Underwriting fees were up meaningfully with debt up 56% and equity up 26% primarily driven by favorable market conditions. In light of the positive momentum throughout the year, we’re optimistic about our pipeline, but the M&A, regulatory environment and geopolitical situation are continued sources of uncertainty.

7. Management is seeing muted demand for new loans from companies partly because they can easily access capital markets; demand for loans in the multifamily homes market is muted; management is not seeing any major increase in appetite for borrowing after the recent interest rate cut

In the middle market and large corporate client segments, we continue to see softness in both new loan demand and revolver utilization, in part due to clients’ access to receptive capital markets. In multifamily, while we are seeing encouraging signs in loan originations as long-term rates fall, we expect overall growth to remain muted in the near term as originations are offset by payoff activity…

…[Question] Lower rates was supposed to drive a pickup in loan growth and conversion of some of these Investment Banking pipelines. I mean, obviously, we just had one cut and it’s early. But any beginning signs of this in terms of the interest in borrowing more, and again, conversion of the banking pipelines?

[Answer] Generally no, frankly, with a couple of minor exceptions…

… I do think that some of that DCM [debt capital markets] outperformance is in the types of deals that are opportunistic deals that aren’t in our pipeline. And those are often driven by treasurers and CFOs sort of seeing improvement in market levels and jumping on those. So it’s possible that, that’s a little of a consequence of the cuts…

…I mentioned we did see, for example, a pickup in mortgage applications and a tiny bit of pickup in refi. In our multi-family lending business, there might be some hints of more activity there. But these cuts were very heavily priced, right? The curve has been inverted for a long time. So to a large degree, this is expected. So I’m not — it’s not obvious to me that you should expect immediate dramatic reactions, and that’s not really what we’re seeing.

8. Management expects the yield curve to remain inverted

The way we view the curve remains inverted. 

9. Management thinks asset prices are elevated, but they are unclear to what extent

We have at a minimum $30 billion of excess capital. And for me, it’s not burning a hole in my pocket…

…Asset prices, in my view, and you — and like you’ve got to take a view sometimes, are inflated. I don’t know if they’re extremely inflated or a little bit, but I’d prefer to wait. We will be able to deploy it. Our shareholders will be very well served by this waiting…

…I’m not that exuberant about thinking even tech valuations or any valuations will stay at these very inflated values. And so I’m just — we’re just quite patient in that. 

10. Consumer spending behaviour is normalising, so a rotation out of discretionary spending into non-discretionary spending is not a sign of consumers preparing for a downturn; retail spending is not weakening; management sees the consumer as being on solid footing; management’s base case is that there is no recession

I think what there is to say about consumer spend is a little bit boring in a sense because what’s happened is that it’s become normal. So meaning — I mean I think we’re getting to the point of where it no longer makes sense to talk about the pandemic. But maybe one last time.

One of the things that you had was that heavy rotation into T&E as people did a lot of traveling, and they booked cruises that they hadn’t done before, and everyone was going out to dinner a lot, whatever. So you had the big spike in T&E, the big rotation into discretionary spending, and that’s now normalized.

And you would normally think that rotation out of discretionary into nondiscretionary would be a sign of consumers battening down the hatches and getting ready for a much worse environment. But given the levels that it started from, what we see it as is actually like normalization. And inside that data, we’re not seeing weakening, for example, in retail spending.

So overall, we see the spending patterns as being sort of solid and consistent with the narrative that the consumer is on solid footing and consistent with the strong labor market and the current central case of a kind of no-landing scenario economically. But obviously, as we always point out, that’s one scenario, and there are many other scenarios.

11. Management thinks that the Federal Reserve’s quantitative tightening (QT) should be wound down because there are signs of stress in certain corners of the financial markets caused by QT

[Question] You I think mentioned QT stopping at some point. We saw the repo sort of market spike at the end of September. Just give us your perspective on the risk of market liquidity shock as we move into year-end. How — and do you have a view on how quickly Fed should recalibrate QT or actually stop QT to prevent some [indiscernible]?

[Answer] The argument out there is that the repo spike that we saw at the end of this quarter was an indication that maybe the market is approaching that lowest comfortable level of reserves that’s been heavily speculated about, and recognizing that, that number is probably higher and driven by the evolution of firms’ liquidity requirements as opposed to some of the more traditional measures…

…It would seem to add some weight to the notion that maybe QT should be wound down. And that seems to be increasingly the consensus, that, that’s going to get announced at some point in the fourth quarter.

12. Management sees inflationary factors in the environment

I’m not actually sure they can actually do that because you have inflationary factors out there, partially driven by QE. 

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Disclaimer: The Good Investors is the personal investing blog of two simple guys who are passionate about educating Singaporeans about stock market investing. By using this Site, you specifically agree that none of the information provided constitutes financial, investment, or other professional advice. It is only intended to provide education. Speak with a professional before making important decisions about your money, your professional life, or even your personal life. I don’t have a vested interest in any company 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 01 September 2024:

1. Aidan Gomez, Co-founder & CEO @Cohere: What No One Understands About Foundation Models (Transcript here) – Harry Stebbings and Aidan Gomez

Aidan Gomez: It’s definitely true that if you throw more compute at the model, if you make the model bigger, it’ll get better. It’s kind of like it’s the most trustworthy way to improve models. It’s also the dumbest. Right? Like, if all else fails, just make it bigger. And so for folks who have a lot of money, that’s a really compelling strategy. It’s super low risk. You know it’s going to get better. Just scale the model up, pay more money, pay for more compute and go. I believe in it. I just think it’s extremely inefficient. There are much better ways. If you look at the past, let’s say year and a half, I guess by now it would be between ChatGPT coming out, or GPT-4 coming out, and now GPT-4, if it’s true what they say, and it’s 1.7 trillion parameters this big MoE, we have models that are better than that model, that are like 13 billion parameters. And so the scale of change, how quickly that became cheaper, is absurd, kind of surreal. And so, yes, you can achieve that quality of model just by scaling, but you probably shouldn’t.

Harry Stebbings: Do we continue to see that same scaling advantages, or does it actually plateau at some point, as you said there, we always hear about Moore’s Law. At some point, it just becomes a better calculator for the iPhone.

Aidan Gomez: It certainly requires exponential input. You need to continuously be doubling your compute in order to sustain linear gains in intelligence. But I think that probably goes on for a very, very, very long time. It’ll just keep getting smarter. But you run into economic constraints, right? Not a lot of people bought the original GPT-4, certainly not a lot of enterprises, because it was huge. It was massive. Super inefficient to serve. So costly, not smart enough to justify that cost. There’s a lot of pressure on making smaller, more efficient models smarter via data and algorithms methods, rather than just scaling up due to market forces. Just pressure on price.

Harry Stebbings: Will we live in this world of unbundled verticalised models, which are much more efficient and smaller, designed for specific use cases. Or will there be much larger three to five models which kind of rule it all?

Aidan Gomez: There will be both. There will be both. The one pattern I think we’ve seen emerge over the past couple years is that people love prototyping with a generally smart model. They don’t want to prototype with a specific model. They don’t want to spend the time fine tuning a model to make it specifically good at the thing that they care about. What they want to do is just grab an expensive big model prototype with that, prove that it can be done, and then distill that into an efficient focus model at the specific thing they care about. That pattern has really emerged. I think we’ll continuously exist in a world of multiple models, some focused and verticalized, others completely horizontal…

…Aidan Gomez: Yeah, sometimes we do. Sometimes we do. There’s this very obvious next step for models, which is you need to let them think and work through problems. You need to let them fail. They need to try something. Fail, understood why they failed, roll that back, and make another attempt. And so, at present, there’s no notion of problem solving in models.

Harry Stebbings: And when we say problem solving, that is the same as reasoning, correct?

Aidan Gomez: Yeah.

Harry Stebbings: Why is that so hard? And why do we not have any notion of that today?

Aidan Gomez: I think it’s not that reasoning is hard, it’s that there’s not a lot of training data that demonstrates reasoning out on the Internet. The Internet is a lot of the output of a reasoning process. Like, you don’t show your work when you’re writing something on the web. You sort of present your conclusion, present your idea, which is the output of loads of thinking and experience and discussion. So we just lack the training data. It’s just not freely available. You have to build it yourself. And so that’s what companies like Cohere and OpenAI and Anthropic, etc, that’s what we’re doing now, is collecting data that demonstrates human reasoning…

…Harry Stebbings: One thing I’m concerned about bluntly or I look at with hesitation is you see OpenAI price dumping. You see Meta releasing for free and Mark pronouncing the value of open source and open ecosystem. Are we seeing this real diminishing value of these models? And is it a race to the bottom and a race to zero.

Aidan Gomez: I think if you’re only selling models for the next little while, it’s going to be a really tricky game. It won’t be a small market. There will be a lot.

Harry Stebbings: This question may be really stupid. Who’s only selling models and who’s selling models and something else?

Aidan Gomez: I don’t want to name names, but let’s say Cohere right now only sells models. We have an API, and you can access our models through that API. I think that that will change soon. There are going to be changes in the product landscape and what we offer to sort of push not away from that, but to add on to that picture and that product suite. But if you’re only selling models, it’s going to be difficult because it’s going to be like a zero margin business because there’s so much price dumping, people are giving away the model for free. It’ll still be a big business, it’ll still be a pretty high number because people need this tech. It’s growing very, very quickly, but the margins at least now are going to be very, very tight.

And so that’s why there is a lot of excitement at the application layer. And I think that discourse in the market is probably right to point out that value is occurring beneath, like at the chip layer because everyone is spending insane amounts of money on chips to build these models in the first place. And then above at the application layer where you see stuff like ChatGPT, which is charged on a per user basis, $20 a month type thing, that seems to be where at this phase, value is accruing. I think that the model layer is an attractive business in the long term, but in the short term with the status quo, it is a very low margin, commoditized business if we just break it down…

…Aidan Gomez: I think it will be. Right now, chips are just exceptionally high margin and there’s very, very little choice in the market. That’s changing. I think it’s going to change faster than other people think. But I’m very confident.

Harry Stebbings: I think you’ve also seen the stockpiling of GPU’s change a lot. Before there was a sign of real supply chain shortage.

Aidan Gomez: Yes. Yeah.

Harry Stebbings: And now it’s not so much.

Aidan Gomez: No. Yeah. The shortage is going down. I think it’s becoming clear there are going to be more options available and not just on the inference side. Inference is already quite heterogeneous. You actually already have loads of options on the inference side, which is like not the training of the models, but the serving. On the training side, the picture has been, it’s essentially one company that creates the chips that you can use to train big models. That’s still true today. But – actually it’s not true today. There’s two companies. You can definitely train big models on TPUs. Those are actually now a usable platform for super large scale model training. And I think Google has proven that quite convincingly. And then there’s Nvidia. But I think soon, AMD, Tranium, these platforms are going to really be ready for primetime…

…Harry Stebbings: On enterprises, Canva is obviously making a hard push for enterprise. You sell into amazing enterprises. What’s the number one blocker today for why enterprises don’t adopt?

Aidan Gomez: It’s mostly trust in the technology. So security. Everyone is very sketched out by the current state of things. Who’s training.

Harry Stebbings: Sketched out means concerned?

Aidan Gomez: Yeah, yeah, right.

Harry Stebbings: Not like a flop.

Aidan Gomez: Well, they’re hoping that they don’t have a flop. So they’re really scared that someone’s going to take their data, train on it, and put them in some sort of security vulnerability, or that they’ll lose IP. I think that’s a very valid concern because people have been training on user data.

Harry Stebbings: Is there anything you can do to reassure them other than, “hey we’re using new synthetic data?”

Aidan Gomez: Yeah. So our deployment model is set up to do that. We focus on private deployments inside their VPC, on prem. What that means is just, it’s on their hardware, completely privately. We’re not asking them to send data over to us. We’ll process it and give you back the response from the model. We’re saying we’ll bring our models to where your data is. We can’t see any of it.

Harry Stebbings: Will we see the movement back to on-prem in this new world?

Aidan Gomez: When I speak to folks, it’s super conflicted in financial services. Yeah. People are pulling away from cloud. They’re pulling away from cloud. They’re building out their own data center capacity. Everywhere else still seems to be we need to migrate to cloud. It doesn’t make sense for us to have these data centers. I think that it probably depends on the vertical that you’re looking at…

…Harry Stebbings: Are we still in the experimental budgets for enterprise? Everyone’s like, oh, we’re just playing with budgets now. Is that fair? Or are we actually moving into mainstream?

Aidan Gomez: It’s really started to shift. So last year, 100%, it was like the year of the proof of concept. Everyone was sort of testing it out, playing around with it. But recently there’s been a big shift to urgency to get this tech into production. I think a lot of enterprises are scared of being caught flat footed. They’ve spent a year running POCs and testing stuff out. Now they’re sprinting towards, I want to put this into production, transform my product, augment my workforce.

Harry Stebbings: What’s the number one use case for them in terms of what they need or want?

Aidan Gomez: The number one use case…

Harry Stebbings: Because it feels like every board is saying, hey, what’s your AI strategy? And it’s like, what does that actually mean? Is it Klarna, who’s very much, we want to optimize our customer service and we’re going to do that. Is that the number one? Customer service? Is it employee augmentation and productivity?

Aidan Gomez: I think it’s employee augmentation. It’s these models becoming a partner or a colleague to your entire workforce. That’s the most popular use case.

Harry Stebbings: I think Copilot is the right way to do that.

Aidan Gomez: I think Copilot is great and it’s the right idea of augmenting a workforce with an assistant. But it’s siloed again within an ecosystem, so it plugs into Office and the Microsoft suite of products. Enterprises don’t just use Microsoft. They use Microsoft for their email and docs and spreadsheets and then they use Salesforce for their CRM. They have SAP for their ERP, they have some HRM, they have internal software that they built for themselves. And if you really want to augment the workforce, you need to have a platform for developing these assistants, these agents, that’s agnostic to a particular toolset and that prioritizes the tool sets rationally across what people actually use, what the market actually uses. So I don’t think that that’s going to be done by Copilot.

Harry Stebbings: You mentioned the word agent there. Agents is one of the hottest topics in ventureland. Do you think it’s justified, the hype around agent’s agentic behavior, what it does to workflows?

Aidan Gomez: I mean, the hype is justified 100%. That’s the promise of AI. The promise of these models is that they would be able to carry out work by themselves that just dramatically transforms productivity. Once you have a model that can go off and do things independently over a very long time horizon. So no longer like, I’m gonna do this one thing for you immediately in return and I’m done. But like, over the next six months, I’m going to be pumping deals into your top of funnel or something like that, right? Like doing outbound for you. It just completely transforms what an organization can do. The hype is justified. I think my critique would be, is that work going to be most effectively done outside the model builders or within? Who’s going to be best positioned to actually build that product?

Harry Stebbings: Why would it be best done within the models?

Aidan Gomez: Completely depends on the quality of the model. It entirely depends on the model. Like, the model is the reasoner behind the agent, and you have to be able to intervene at that level. If you’re not able to actually transform the model to be better at the thing that you care about. If you’re not the one building the model, if you’re just a consumer of the model, you’re structurally disadvantaged to build that product…

…Aidan Gomez: I think there’s sort of like a meme that’s going around of people saying we plateaued, nothing’s coming, it’s slowing down. I actually really think that’s wrong and not just from like a we need to 10x compute and that type of thing perspective and trust me, it’ll get better. But from a methods perspective. So when I was talking about reasoners and planners and models that can try things, fail and recover from that failure, and carry out tasks that take a long time to accomplish, these are, for the technologists, obvious things that just don’t exist in the technology today. We just haven’t had time to turn our focus there and add that capability into the model. For the past year plus, folks have been focusing on that and it will be ready for production, so we’ll see that come out, and I think that will be a big change in terms of capability…

…Harry Stebbings: What does AI not do today that you think it will do in three years? It will be completely transformative.

Aidan Gomez: I think robotics is like the place where there will be big breakthroughs. The cost needs to come down, but it’s been coming down. And then we need models that are much more robust just because a lot of the barriers have fallen away like before. Reasoners and planners inside of these robots, the software behind them, they were brittle and you had to program each task you wanted it to accomplish. And it was super hard coded to a specific environment. So you have to have a kitchen that is laid out exactly like this.

Harry Stebbings: Exactly the same dimensions, nothing different.

Aidan Gomez: Yeah, so it was very brittle. And on the research side, using foundation models, using language models, they’ve actually come up with much better planners that are more dynamic, that are able to reason more naturally around the world. I know this is already being worked on. There’s like 30 humanoid robotic startups and that type of thing. But soon someone’s going to crack the nut of general purpose humanoid robotics that are cheap and robust. And so that will be a big shift. I don’t know if that comes in the next five years or ten years, it’s going to be somewhere in that range…

…Harry Stebbings: So what have you changed your mind on most in the last 12 months?

Aidan Gomez: The importance of data. I underrated it dramatically. I thought it was just scale. And a lot of proof points have happened internally at Cohere that have just transformed my understanding of what matters in building this technology.

Harry Stebbings: So now it’s the quality of data.

Aidan Gomez: Yeah, quality. Like a single bad example, right, amongst like billions. It’s so sensitive. It is a bit surreal how sensitive the models are to their data. Everyone underrates it.

2. Chip War’s Chris Miller on Putin, China, and The Future – Mario Gabriele and Chris Miller

Which current or historical figure has most impacted your thinking?

Vladimir Putin. He is the most striking embodiment of my belief that you can’t understand people through traditional utility functions.

My background is in Russian studies, and I’m struck by the extent to which our analysis of Putin has changed over time. Twenty years ago, when he first came to power, he portrayed himself – and with some level of accuracy, I think – as a relatively modern leader of Russia. He was reforming the tax system and doing stuff that political leaders do. When we talked about his motivations at the time, the focus was often very financial. I remember very distinguished economists who I respect greatly saying, “Isn’t it the case that Putin is primarily driven by money?” And indeed, there are lots of examples of Putin being hugely corrupt and his friends stealing all sorts of stuff. He’s got his gaudy palaces on the shores of the Black Sea.

But we’ve learned that it’s not all about money. When he invaded Ukraine in 2022, Putin cited Peter the Great and Catherine the Great as justifications for territorial conquest. It’s an illustration that “modern people” are not always driven by modern impulses. The desire for power and glory and control, the desire to be on top and dominate others – for better or worse – are central to many people’s utility functions. These impulses might seem more base, but I think, to some degree, they’re present within all of us. You ignore them at your peril…

…What is the most significant thing you’ve changed your mind about over the past decade?

I’ve changed my mind about the usefulness of thinking like an economist. Even though I may criticize them sometimes, I have great admiration for economists. But they think of everything in terms of utility functions and how to maximize them. They only know how to calculate that in dollars and cents. Though that’s valid, I’ve come to appreciate its limitations.

I’ve spent a lot of time over the past decade studying great entrepreneurs and geopolitical competition. Fundamentally, neither founders nor countries think like economists. Great founders may have shareholders who would like them to consider return on equity, but that’s not how they make decisions. Think of Jensen Huang ten years ago – even though Wall Street was warning him against it, he still poured Nvidia’s money into building out CUDA and the ecosystem around it. If your mode of thinking is purely economic – focused on return on equity or maximizing shareholder value – you miss a lot of what actually drives competitive, successful people.

The same thing is true at the international level. Governments don’t think like economists, either. They try to maximize glory or territory or reputation or power. Like great entrepreneurs, sometimes they simply want to win, just for the sake of besting an adversary. There are ultimately so many things that drive nations and the humans within them that are non-quantifiable. Often, they’re much more significant than strictly quantifiable economic variables…

…What risk are we radically underestimating as a species? What are we overestimating?

We’re underestimating the risk of a great power conflict – World War III. World wars happen roughly every half-century. We shouldn’t forget that. Whether as part of a world war or not, the risk of a nuclear weapon being used in conflict within the next 50 years also seems highly plausible.

You can see points of tension across the border between China and the Western sphere. You see it in the South China Sea with the Philippines, in the East China Sea with Taiwan, and in the Himalayas with India – and those are just the border disputes. It’s easy to imagine how that could spiral in an escalatory manner.

If you put a dollar value on the cost of this kind of conflict, it would be measured in the many trillions. Yet the amount of time we spend thinking about it is not remotely commensurate with that outcome. Some people console themselves by saying, “It’s high magnitude but low risk, so the expected cost is low.” I’m not so sure about that. If you talk about the risk this year, maybe it’s low. But if you think about it over the next decade and factor in the risk compounding every year, suddenly, I don’t think those assumptions hold.

If you think the risk is high, we have two options. You can either offer concessions or build up your capabilities to deter more successfully. From the US perspective, we’ve been doing a little bit of the latter and a little bit of the former under Biden – but not much of either. I think it’s intellectually coherent to say, “Let’s do more of one or more of the other.” I think it’s not intellectually coherent to say, “Let’s just do a little bit of both,” when in reality, defense spending is at historic lows relative to the post-Cold War period.

3. Joel Greenblatt: Value and Special Situation Investment Lecture with Rob Goldstein (Transcript here) – Joel Greenblatt and Rob Goldstein

Rob Goldstein (02:32): We came across Moody’s in early 2000 when it was in the process of being spun off. It was obvious that Moody’s was one of the great businesses that we had ever seen and the problem was it was trading at 21 times forward earnings, and 24 times trailing earnings. So the question we had to ask ourselves was just how much of that greatness was already reflected in the stock price. Just to give a little perspective, typically at that time, we would buy stocks at 10 times earnings and sell it at 14 or maybe even 15 times earnings if we got lucky. So the thought of paying up for a business like this was really a new thing for us. So what I did was I compared Moody’s to Coke… 

…Goldstein (04:06): Okay. So several decades ago Buffett figured out that if he identified a really great business he could pay what seemed like a lot of money and still make a fortune. In 1988, Buffett bought $600 million of Coke stock. He paid around 13 times forward earnings, 15 times trailing earnings, and back then the value investment community didn’t understand why that was any great bargain. But 12 years later, the $600 million was worth over $7 billion. So Coke became the classic example of paying up for a great business and making a fortune doing it so that’s why I looked at Coke…

…Goldenstein (05:39): Okay, so there’s three really good things about Coke. [Writes on board: (1) Organic Growth, (2) High ROE, (3) Lasting Competitive ADVANTAGE]. To sum up, those are the three really important things to remember about Coke. In addition it was a relatively easy business to understand and it was a predictable business. Most businesses are neither of those things…

…Okay, my first slide. We have Moody’s historical financials and in the 19 previous years to 2000, revenues had grown at a compounded annual rate of 15% and operating profits have grown at a compounded annual rate of 17%. Not many companies have that kind of terrific performance. In the 19-year period, year-over-year revenue declined only one time and that decline was just a few percent and happened after a period of rapid growth. So you know they’ve done great in the past. But does past success equal future success, and as Moody’s a great business, how should we think about that?…

…Just to explain where this growth came from because it’s important for the rest of the analysis. 30 years ago, when you get a loan, the lending institution would retain that loan. Today, many of these loans are securitized and sold into the capital markets. The guy originating the loan is not necessarily the guy financing the loan. Today there’s trillions of dollars of these securities, including credit card loans, home equity loans, commercial mortgage loans, auto loans, etc. To do these securitizations, you need ratings.  Financing loans through the capital markets is more efficient than the old way, so one would expect that the growth would continue. In addition, Europe was way behind the US in terms of their growth curve of issuing these asset-backed securities and Asia was behind Europe. They were just sort of starting to go down that path. So basically there was lots of growth ahead.

We talked about good return on capital which we can get to later. In terms of the lasting competitive advantage, we talked about why there can be no new entrants and we touched on why there won’t be any pricing pressure, because their fees seem reasonable in the larger scope of things. You really have to go to S&P and Moody’s to get ratings, and they both know that, so they’re not going to be very negotiable on price. So the company was in the right place at the right time, and the same factors responsible for the past growth would be expected to continue into the future. So we concluded that Moody’s was a great business…

…This is a price chart of Coke. How much did Berkshire Hathaway make over those 12 years? We’ll assume that he paid $5 a share on $6.88. 12 years later, and his stock was $58 a share – be right around here – and he had collected $4.75 in dividends over that time. Just to keep it simple, let’s assume he was able to earn 6% on those dividends that he received, so let’s value the dividend at $6. So his $5 turned into $64,  and he’s got a 23.7% rate of return on his investment, annualised.

I basically pulled these numbers out of an annual report at the time. Question is, why did Buffett do so well on his Coke investment?…

…You’re correct, over the 10-year period, revenues grew at 8.8% and unit case volumes at 7%. Oh it is industry… oh no, the industry’s 4%-5% percent. So over the 10-year period they’ve got some price increases. Of course their cost also went up. They were able to grow their unit case volume to 7% a year, so they had organic growth, they didn’t need that much of it. That translated to 12% operating income growth. There was a little bit of leverage so they got 13% in net income growth and they bought the stock, so they got 15% EPS growth over that time.

The other reason why he did so well was because – we just talked about this – he only had to reinvest 20% of the earnings back into the business. So that meant that in addition to the buybacks he was able to pay our dividends.

Just one formula I’m going to put up on the board because we’re gonna come back to it later, is [writes on board: Growth rate divided by reinvestment rate equals return on equity]. So their growth rate was 12%, reinvestment rate was 0.2, so the return on equity was 60%. So that’s how the business performed and in addition, he did so well because there was big PE expansion. He paid about 15 times forward earnings when he bought the stock and in 2000 when we looked at it, it was trading at north of 30 times expected 12-month earnings.

So how can we expect Moody’s to perform for us over the next 12 years? What growth rate should we assume?…

…Well we settled on 12% and the reason why we settled on 12% is because (1) management guidance was low single digits, and (2) because 12% seemed very reasonable considering the historical operating performance had been so much better in the belief that the same factors responsible for the past growth were going to continue… 

…So we felt very comfortable that they could grow at very healthy rates in the future. An estimated 12% operating earnings growth rate for Moody’s happen to be very convenient, because that was Coke’s growth rate during those 10 years we looked at. So for the remaining analysis I could now just focus exclusively on the difference in return on capital and how that impacted the different valuations…

…What would you guess Moody’s return on capital was?

Attendees (33:06): [Indecipherable]

Goldstein (33:09): That’s exactly right. Their return on capital was infinite, because they had no – their $50 million in PP&E, they needed desks and computers for 2,000 employees and that was it. In addition their customers paid on time or in advance. They were in a very strong position. They could demand payment upfront and you typically see that kind of a thing with companies that earn good returns on capital. But the answer was their returns on capital were infinite. Very few businesses like that.

So Coke needed to spend 20% of its earnings on…  So they earn a dollar, they spend 20 cents, and you have 80 cents left over. Moody’s would spend nothing. They’d have a dollar left over. So how much more was Moody’s earning stream worth more than Coke? 25%? Okay, a dollar is 25% higher than eighty cents.

Now does this mean that the higher return on capital makes Moody’s worth 25% more than Coke? Well yes and no…

…Goldstein (36:02): The question is, everything else saying the same, does this fact that Moody’s has a higher return on capital mean that their business is worth 25% more than Coke?

Attendees (36:18): Yes, in terms of free cash flow.

Goldstein (36:22): In the short term that’s correct. But in the longer term, they’re not gonna grow at these 12% rate forever. So if you assume that in the very long run that growth rates drop to 5%, then if you go back to this formula [pointing to formula on growth rate, reinvestment rate, and ROE], you see that for Coke, will mean that they need to reinvest 8%-10% of their earnings in the business as their growth rate drops. This formula here is what we use to calculate this 60% ROE for Coke. Growth rate over return on capital equals the reinvestment rate. The growth rate at some point in the future drops to 5%, 20 years down the road or whenever, the return on equity is 60% for Coke we calculated, so that means the reinvestment rate would be 8.3%. The slower the growth, the less capital you need, the more capital you can pay out. So let’s just assume that at some point Coke will be paying out 90%-92% of earnings. So we split the difference instead. Let’s assume that this return on capital thing is going to mean that Moody’s is worth 15% more than Coke. It’s just somewhere in the middle between 25% and 10%, or 8%…

…Okay so you just raised my next point, which was is there something else you need to consider? What are they going to do with the money? So we saw that Coke returns all their excess capital, and we felt that Moody’s was very likely to return all their excess capital. In fact, they were gonna put more of that money into buybacks because that’s what management had said they were gonna do. So we basically took this important point and we could leave it out of our analysis at this point. because they were basically gonna be equal for both companies.

So can we justify 21 times earnings? 13 times 1.15 – the benefits from the higher return on capital – so you can pay 15 times earnings and get the same thing. How about 21 times earnings?

Attendees (46:20): [Indecipherable]

Goldstein (46:31): We concluded that because Moody’s had a much higher return on capital, the business was worth 15% more.

Attendees(46:48): 13 was the PE in Coke in 1988, but you’re saying Moody’s can justify a PE of 15?

Goldstein (46:57): Based on the higher return on capital. We saw that we were going to use similar revenue growth assumptions. Growth rate was the same, ROE was different, and let’s assume this [pointing to reinvestment rate] is the same.

Attendees (47:10): [Indecipherable]

Goldstein (47:18): Yes and I’m gonna get to that in a minute. The analysis I made was, I said what would have happened if back in 1988, Buffett paid 18 times earnings, or $7 a share for his stock. So what would have happened is he still would have done great. He would have made 8 times his money, and he would have had a compounded annual return of 20%. Still a great purchase. So he could have paid 18 times earnings at that point and still have done great. So $5 to $7, increased the price by 40%, gets you your 21 multiple – that’s what we had to get. Which is why we used 1.4. Does that make sense?

Well, let’s say you went back to the 1988 and you said that he couldn’t pay 13 times earnings, he had to pay 18 times earnings, how would he have done on his investment, and he still would have done great. So basically he did so well that he had so much room that he could have paid a lot more for his stock and still had a very good investment. Not as good, but still very good. He would have made 20% a year, each year, over those 12 years, and that 40% number got us to our 21 multiple.

[Equation on board: 13 x 1.15 = 15, 15 x 1.4 = 21]

So we kind of backed into it that way and that was the original analysis. And the reasoning was very sound despite the short cut we used. Actually the first time I spoke in Joel’s class, one of the students like you, said it didn’t – interest rate or something had to do with this – and immediately I knew that interest rates had a lot to do with it. Only I never really thought about it.

So what happened was after Buffett purchased Coke, interest rates over the remaining 12 years dropped from 9% to 6% [uses projector for a chart showing interest rates]. So 9% and over here down to 6%. So if you price the 30-year bonds and said that that 30-year bonds, how would that change in price if rates went from 9% to 6%, the answer would be, it would go up by 42%. If it was a perpetuity, it would go up by 50%, but it’s not, it’s a 30-year bond. So it’d go up by 42% percent and that’s the right way of really looking at things. So, it so happens that – this was somewhat random – but the 42% is basically the 40% that we came up with right here [pointing to the equation on the board of “13 x 1.15 = 15, 15 x 1.4 = 21”].

Attendees (51:50): [Indecipherable] For Moody’s, now interest rates are low…

Goldstein (52:08): Okay, let’s see how Buffett would have done. It’s a very good question actually. Let’s see how he would have done. So if interest rates drop from 9% to 6%, thing’s worth 40% more if rates go up. The way the math works, they’re worth 30% less. So going from 1 to 1.4, it’s 40% up, from 1.4 to 1, it’s 30% less. Had his stock traded for 30% less at the time we did this analysis, he would have had a $40.60 stock, he would have gotten $6 in dividends, he’d have $46.60 over his original $5 investment, he would have made over 8 times his money. I did the math, so I know that’s a compounded annual return of 20%.  It’s not as good as the 23.7%, but it’s still very good.

So taking your point. it wasn’t that we were expecting to do 23.7%, we were assuming that if interest rates stayed the same or went down, we could expect to make 20%, and that’s probably what he was looking at when he bought Coke. I don’t think he was betting on lower interest rates although who knows what he was doing. That makes sense right?…

…What happened to Moody’s was a good part and a bad part. The good part is that it did trade up. In October when this stock was actually spun off, it was up 20% from where it was in March. And by that following April – so I guess that had been just over a year – the stock was up 50% from where it had started. Now what happened with Moody’s is – and here’s the sad part because we sold our stock too early on this one – but what happened was in 2001, the business exploded to the upside. Profits didn’t grow 12%, they grew at 40%. In the next year, they didn’t grow at 12%, they grew it 35%. So profits have compounded over the following 6-plus years at 25%, at least 25%. I guess that’s what happens when you use conservative assumptions. But the stock was up 6 or 7 times since then and a lot of those gains came early before earnings really took off.

Attendees (59:10): So do you decide on what price to sell?

Goldstein (59:13): That’s a very good question. We obviously made a bad decision. It went up a bunch, earnings had started to shoot up, yet we thought – we got higher hurdle rates then a guy managing zillions of dollars, so the stock was up 50%, so had to think seriously about selling and putting your money into something else. When you make these analyses, hindsight is 20/20 and everything is so easy in retrospect. But in real time when you’re doing this, you’re obviously worried that stock’s 30 times earnings, what happens if I’m wrong, what happens if things do poorly next year and all of a sudden you’re not paying 30 times earnings, you’re paying 40 times earnings. Now the business looks shaky. So it’s never as easy at the time as it is after the fact. But we sold when it was up 50% or more. of all time.

4. Why China Is Starting a New Trade War –  Lingling Wei and Jason Douglas

Interviews with policy advisers in Beijing and people who have consulted with Chinese officials show that China’s leadership faced a pivotal crossroads last year, as the country’s real-estate bust brought the economy to one of its weakest points in decades.

Some advisers argued that China’s economy needed a fundamental rethink, graduating from its traditional heavy reliance on manufacturing and construction and instead prioritizing more domestic consumption—a shift that would make China more like the U.S., and potentially put it on a more stable growth path.

Instead, Chinese leader Xi Jinping ordered officials to double down on the country’s state-led manufacturing model, with billions of dollars in fresh subsidies and credit. He used a slogan to make sure officials got the message: “Establish the new before breaking the old,” or xian li hou po in Chinese.

The “new” in Xi’s model doesn’t mean a pivot to a new growth model. Instead, it is the top leader’s way of refining his idea of what kind of manufacturing for the state to back. In essence, the phrase calls for building industries China wants to dominate for the future—such as EVs, semiconductors and green energy—while also maintaining the country’s traditional areas of strength in “old” sectors such as steel. Any overcapacity problems can be punted to the future…

…Two principles have guided Xi’s thinking, Chinese policy advisers say. The first is that China must build an all-encompassing industrial supply chain that can keep the domestic economy running in the event of severe sanctions by the U.S. and other Western countries. In the top leader’s views, advisers say, industrial security sits at the core of China’s stability as tensions with the developed world rise.

The second is a deep-rooted philosophical objection to U.S.-style consumption, which Xi sees as wasteful.

That leaves China with few options other than investing in exports to stabilize its weakened economy and create jobs to make up for losses in domestic construction…

…Loans to industry, including manufacturing firms, have increased 63% since the end of 2021, while Chinese banks have pulled back sharply on lending to real-estate developers.

Government subsidies, though long central to China’s economic playbook, have also ramped up significantly. Companies listed on the Shenzhen and Shanghai stock exchanges declared $33 billion in government subsidies in 2023, according to figures from data provider Wind—23% more than in 2019…

…In all, 99% of publicly listed Chinese companies now disclose some form of subsidy, according to the Kiel Institute, a German think tank. China spends about 4.9% of its gross domestic product on nurturing industries—several times higher than the U.S., Germany and Japan, according to Scott Kennedy, a China expert at the Center for Strategic and International Studies in Washington.

Craig Allen, president of the U.S.-China Business Council, a lobbying group for American companies in China, said Xi’s manufacturing fixation was on display when he met recently with the governor of one of China’s poorest farm provinces.

When Allen asked the governor about his economic priorities, the governor listed semiconductors, software, biotechnology, robotics, aerospace, batteries, and EVs.

“I would have thought that addressing the immediate needs of his overwhelmingly rural constituents, such as improving agricultural harvests, might be at the top of his economic priorities list,” Allen said.

The fire hose of financial support looks set to keep spraying. The People’s Bank of China in April said it set up a new facility with roughly $70 billion to help bank lending to tech firms. In May, a national fund aimed at financing semiconductor production raised $48 billion from state-owned banks and other government-linked investment vehicles…

…“China’s production of advanced electric vehicles, lithium-ion batteries and photovoltaic products, first met our domestic demand, but also enrich global supply,” Chinese premier Li Qiang said in an address to the World Economic Forum’s June meeting in Dalian, China. The real source of China’s manufacturing edge isn’t government subsidies but its huge scale, which helps pin down costs, he added…

…China has added capacity to produce some 40 million vehicles a year, even though it sells only around 22 million at home. It’s on track to make around 750 gigawatts of solar cells this year, despite only needing 220 gigawatts domestically in 2023. And it is expected to account for 80% of the world’s new supply this year in basic chemicals such as ethylene and propylene, used to make garbage bags, toys and cosmetics—even though prices in China have been falling for 19 months, a sign of oversupply.

At the same time, output of steel, one of China’s “old” industries, increased last year despite waning domestic demand due to the continuing property crisis. Industry executives say Beijing has been prodding them to invest more in upgrading steel production through clean technologies and other means…

…China has suffered from persistent overcapacity in the past, at times raising ire from its trading partners for depressing global prices for steel and other goods.

In 2015, Xi entrusted his economic czar at the time, Liu He, to implement reforms that led to closures of many small and privately owned steel mills and other businesses. For a while, it seemed as if Xi and his economic team were ready to finally tackle overproduction.

But as tensions with the U.S. escalated in recent years, and China’s economy weakened, Xi’s views changed, Chinese policy advisers say. He grew more concerned about ensuring China could produce everything it needed in the event of a conflict with the U.S., and became less sympathetic to Western complaints.

5. What is behind China’s perplexing bond-market intervention? – The Economist

Many governments live in fear of bond-market “vigilantes”, investors who punish errant policies by aggressively selling the sovereign’s debt, driving down its price and thereby pushing up its yield. Financial regulators also worry about bond-market malfunctions, such as unsettled trades, when one party to a transaction fails to honour its promises. These mishaps can send ripples of anxiety through an entire financial system.

Such fears do not seem to apply to China’s financial authorities. On August 9th regulators in the southern province of Jiangxi ordered several rural banks not to settle their recent purchases of government bonds, according to Bloomberg, a news service. Similar lenders elsewhere have also been reported to the People’s Bank of China (PBoC), the country’s central bank, for using their own accounts to buy bonds on behalf of others. Rural banks have been instructed to stick to their main business of lending to local enterprises, rather than to the central government.

The measures are part of an attempt by the central bank to stem a relentless rally in the government’s bonds. Earlier this month yields dropped below 2.1% on ten-year securities, down from almost 2.6% at the start of the year. The causes are clear: China’s economy has slowed, borrowers have retreated and inflation has vanished. Nonetheless, officials have been warning since April that yields would not stay low for ever. In July the PBoC unveiled plans to sell government securities borrowed from other financial institutions if required. The central bank was, in other words, “preparing to short its own government’s bonds”, as Adam Wolfe of Absolute Strategy Research, a consultancy, put it. In the end, the bank left the vigilantism to other members of its posse. On August 5th state-owned banks sold bonds heavily, driving the price down and the yield back up a notch…

…In the long run, the best way to lift yields is to warm up the economy, which is likely to require more borrowing and spending from the central government. Its fiscal stimulus would be more powerful if the central bank supports spending with further interest-rate cuts. In other words, yields may have to fall before they can rise. If China’s government is to succeed in reflating the economy, the PBoC will need to act like an accomplice, not a vigilante.

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Disclaimer: The Good Investors is the personal investing blog of two simple guys who are passionate about educating Singaporeans about stock market investing. By using this Site, you specifically agree that none of the information provided constitutes financial, investment, or other professional advice. It is only intended to provide education. Speak with a professional before making important decisions about your money, your professional life, or even your personal life. We currently have a vested interest in Alphabet (parent of Google), Meta Platforms, Microsoft, and Salesforce. Holdings are subject to change at any time.

A topic I’ve noticed that is buzzing among financial market participants lately is what would happen to the US stock market if and when the Federal Reserve, the US’s central bank, cuts interest rates later this year. 

There is a high likelihood of a rate cut coming, although there is more uncertainty around the timing and the extent of any cut. In a speech last week, the central bank’s chair, Jerome Powell, said (emphases are mine):

“The time has come for policy to adjust. The direction of travel is clear, and the timing and pace of rate cuts will depend on incoming data, the evolving outlook, and the balance of risks.”

I have no crystal ball, but I do have historical context. Josh Brown, CEO of Ritholtz Wealth Management, a US-based investment firm, recently shared fantastic data on how US stocks have performed in the past when the Federal Reserve lowered rates. His data, in the form of a chart, goes back to 1957 and I reproduced them in tabular format in Table 1; it shows how US stocks did in the next 12 months following a rate cut, as well as whether a recession occurred in the same window:

I also split the data in Table 1 according to whether a recession had occurred shortly after a rate cut, since eight of the 21 past rate-cut cycles from the Federal Reserve since 1957 took place without an impending recession. Table 2 shows the same data as Table 1 but for rate cuts with a recession; Table 3 is for rate cuts without a recession.

With all the data found in Tables 1, 2, and 3, here are my takeaways:

• US stocks have historically done well, on average, in the 12 months following a rate-cut. The overall record, seen in Table 1, is an average 12-month forward return of 9%. When a recession happened shortly after a rate-cut, the average 12-month forward return is 8%; when a recession did not happen shortly after a rate-cut, the average 12-month forward return is 12%.
• Drawdowns – the maximum peak-to-trough decline in stocks over a given time period – have occurred nearly all the time following a rate-cut. This is not surprising. It’s a feature of the stock market that you would often have to endure a sharp shorter-term fall in stock prices in order to earn a positive longer-term return.
• A recession is not necessarily bad for stocks. As Table 2 shows, US stocks have historically delivered an average return of 8% over the next 12 months after rate cuts that came with impending recessions. 
• It’s not a guarantee that stocks will produce good returns in the 12 months after a rate cut even if a recession does not occur, as can be seen from the August 1976 episode in Table 3.
• My most important takeaway is that a rate-cut is not guaranteed to be a good or bad event for stocks. One-factor analysis in the financial markets  – “if A happens, then B will occur” – should be largely avoided because clear-cut relationships are rarely seen.

It’s worth bearing in mind that it’s not a certainty that the Federal Reserve will be cutting rates in the near future. Anything can happen in the financial markets. And even if a rate cut does happen, no one knows for sure how the US stock market would perform. History is not a perfect indicator of the future and the best it can do is to give us context for the upcoming possibilities. 

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Disclaimer: The Good Investors is the personal investing blog of two simple guys who are passionate about educating Singaporeans about stock market investing. By using this Site, you specifically agree that none of the information provided constitutes financial, investment, or other professional advice. It is only intended to provide education. Speak with a professional before making important decisions about your money, your professional life, or even your personal life. I have no vested interest in any companies mentioned. Holdings are subject to change at any time.

Editor’s note: We’re testing out a new series for the blog, the “Company Notes Series”, where we periodically share our notes on companies we’ve studied in the recent past but currently have no vested interest in (we may invest in or sell shares in the companies mentioned at any time). The notes are raw and not updated, and the “as of” date for the data is given at the start of the notes. Please give us your thoughts on the new series through the “Contact Us” page; your feedback will determine if we continue with it. Thanks in advance!

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Start of notes for Natural Resource Partners

Data as of 9 January 2024

Background on company

• Company name: Natural Resource Partners LP
• Ticker: NYSE: NRP
• Structure: Publicly traded Delaware limited partnership formed in 2002
• Natural Resource Partners LP’s operations are conducted through Opco and its operating assets are owned by its subsidiaries, where Opco refers to NRP (Operating) LLC, a wholly owned subsidiary of Natural Resource Partners LP.  NRP (GP) LP is the general partner and has sole responsibility for conducting Natural Resource Partners LP’s business and for managing its operations. Because NRP (GP) LP is a limited partnership, its general partner, GP Natural Resource Partners LLC, conducts its business and operations; the Board of Directors and officers of GP Natural Resource Partners LLC also makes the decisions for Natural Resource Partners LP. Robertson Coal Management LLC, a company wholly owned by Corbin Robertson, Jr., owns all of the membership interests in GP Natural Resource Partners LLC. 
• The senior executives who manage Natural Resource Partners LP are employees of Western Pocahontas Properties Limited Partnership or Quintana Minerals Corporation, which are both controlled by Corbin Robertson Jr.
• Neither GP Natural Resource Partners LLC nor any of its affiliates receive any management fee or other compensation in connection with the management of Natural Resource Partners LP apart from reimbursement for all direct and indirect expenses incurred on the behalf of Natural Resource Partners LP. 

Business

• Natural Resource Partners LP has two segments: Mineral Rights, and Soda Ash
• In 9M 2023, Natural Resource Partners LP’s total revenue was US$275.9 million and 79% was from Mineral Rights (US$217.3 million) and 22% was from Soda Ash (US$58.6 million). In 2022, Natural Resource Partners LP’s total revenue was US$389.0 million and 85% was from Mineral Rights (US$329.2 million) and 15% was from Soda Ash (US$59.8 million)

Business – Mineral Rights segment

• The Mineral Rights segment consists of 13 million acres of mineral interests and other subsurface rights – including coal and other natural resources – across the US; if combined in a single tract, the ownership would cover roughly 20,000 square miles. The ownership provides critical inputs for the manufacturing of steel, electricity, and basic building materials, as well as opportunities for carbon sequestration and renewable energy. Natural Resource Partners is working to strategically redefine its business as a key player in the transitional energy economy in the years to come. Figure 1 below shows Natural Resource Partners LP’s geographic distribution of its ownership. 

• Under the Mineral Rights segment, Natural Resource Partners LP does not mine, drill, or produce minerals. Instead, the limited partnership leases its acreage to companies engaged in the extraction of minerals in exchange for royalties and various other fees. The royalties are generally a percentage of the gross revenue received by lessees (the companies that extract the minerals), and are typically supported by a floor price and minimum payment obligation that protects Natural Resource Partners LP during significant price or demand declines. The majority of revenue from the Mineral Rights segment revenues come from royalties related to the sale of coal. Of the Mineral Rights segment’s US$217.3 million in revenue in 9M 2023, US$170.8 million came from Coal Royalty revenue, so Coal Royalty Revenue was 62% of Natural Resource Partners LP’s total revenue in 9M 2023; of the Mineral Rights segment’s US$329.2 million in revenue, in 2022, US$227.0 million came from Coal Royalty revenue, so Coal Royalty Revenue was 58% of Natural Resource Partners LP’s total revenue in 2022.  Natural Resource Partners LP’s coal is primarily located in the Appalachia Basin, the Illinois Basin, and the Northern Powder River Basin. Natural Resource Partners LP’s coal-related leases are typically long-term in nature – at end-2022, two-thirds of royalty-based leases have initial terms of 5 to 40 years, with substantially all lessees having the option to extend the lease for additional terms. Leases include the right to renegotiate royalties and minimum payments for the additional terms. 
• Figure 2 below shows all the other revenue sources for the Mineral Rights segment in 9M 2023 and 9M 2022:

• There are two kinds of coal, and Natural Resource Partners LP participates in both in its Mineral Rights segment:
  • Metallurgical coal, or met coal, is used to fuel blast furnaces that forge steel and is the primary driver of Natural Resource Partners LP’s long-term cash flows. Met coal is a high-quality, cleaner coal that generates exceptionally high temperatures when burned and is an essential element in the steel manufacturing process. Natural Resource Partners LP’s met coal is located in the Northern, Central and Southern Appalachian regions of the United States.
  • Thermal coal, sometimes referred to as steam coal, is used in the production of electricity. The amount of thermal coal produced in the US has been falling over the last decade as energy providers shift to natural gas and to a lesser extent, alternative energy sources such as geothermal, wind, and solar. Management believes thermal coal’s long-term secular decline will continue. This, together with the long-term strength of the met coal business and Natural Resource Partners LP’s carbon neutral initiatives mean that thermal coal will be a diminishing contributor to Natural Resource Partners LP’s business in the future. The vast majority of the limited partnership’s thermal coal sales are located in Illinois and its operations are some of the most cost-efficient mines east of the Mississippi River. The remainder of Natural Resource Partners LP’s thermal coal is located in Montana, the Gulf Coast and Appalachia.
  • Met coal tends to be priced higher than thermal coal.
  • In 2022, 70% of Natural Resource Partners LP’s Coal Royalty revenues and approximately 45% of coal royalty sales volumes were derived from metallurgical coal.
  • Figure 3 shows the types of coal production of Natural Resource Partners LP from various properties in 2022, and Figure 4 shows the limited partnership’s significant coal royalty properties in 2022.

• Under the Mineral Rights segment, Natural Resource Partners LP also participates in the sequestration of carbon dioxide underground. Similar to its Coal Royalty business, Natural Resource Partners LP only plans to lease acreage to companies that will conduct carbon dioxide sequestration. Natural Resource Partners LP owns approximately 3.5 million acres of specifically reserved subsurface rights in the southern US with the potential for permanent sequestration of greenhouse gases. The carbon capture utilization and storage industry is in its infancy but a few facts are clear. A sequestration project requires acreage possessing unique geologic characteristics, close proximity to sources of industrial-scale greenhouse gas emissions, and the appropriate form of legal title that grants the acreage owner the right to sequester emissions in the subsurface. Although carbon sequestration rights and ownership continue to evolve, management believes that Natural Resource Partners LP owns one of the largest acreages in the USA with potential for carbon sequestration activities. In 2022 Q1, Natural Resource Partners LP leased its first acreages (75,000 acres) for subsurface carbon dioxide sequestration in underground pore space in southwest Alabama, with the potential to store over 300 million metric tons of carbon dioxide; in October of 2022, the second subsurface carbon dioxide sequestration lease was signed, this time for 65,000 acres of pore space near southeast Texas, with an estimated storage capacity of at least 500 million metric tons of carbon dioxide. At end-2022, Natural Resource Partners LP had 140,000 acres of pore space under lease for carbon dioxide sequestration, with estimated carbon dioxide storage capacity of 800 million metric tons.

Business – Soda Ash segment

• The Soda Ash segment consists of 49% non-controlling equity interest in Sisecam Wyoming, a trona ore mining and soda ash production business located in the Green River Basin of Wyoming. Sisecam Wyoming mines trona and processes it into soda ash that is sold both in the USA and internationally into the glass and chemicals industries.
• Sisecam Resources LP runs Sisecam Wyoming and owns the other 51%. Natural Resource Partners LP is not involved in the day-to-day operation of Sisecam Wyoming, although Natural Resource Partners LP is able to appoint – and has appointed – 3 of the 7 members of Sisecam Wyoming’s Board of Managers.
  • In December 2021, Sisecam Resources LP changed majority-owners. Before this, Sisecam Wyoming was named Ciner Wyoming, and Sisecam Resources LP was named Ciner Resources LP. Under the terms of the transaction, Ciner Enterprises Inc, which controls 74% of Ciner Resources LP, effectively sold 60% of its interests in Ciner Resources LP to Sisecam Chemicals USA Inc, an indirect subsidiary of Turkish conglomerate Türkiye Şişe ve Cam Fabrikalari A.Ş. Ciner Resources LP subsequently changed its name to Sisecam Resources LP. 
  • In February 2023, Sisecam Resources LP announced that it would be fully acquired by Sisecam Chemicals Resources LLC. Sisecam Chemicals Resources LLC is in turn, 60% owned by Sisecam Chemicals USA Inc. The acquisition price of Sisecam Resources LP is US$25 per unit for all the units of Sisecam Resources LP that were not controlled by Sisecam Chemicals USA Inc (from the above, Sisecam Chemicals USA Inc already controlled 60% of Sisecam Resources LP – see Appendix for more). Sisecam Resources LP’s total unit count as of 31 March 2023 was 19.8 million, so Sisecam Resources LP was valued by Sisecam Chemicals USA Inc at US$495 million. Sisecam Resources LP’s only business interest is its 51% stake in Sisecam Wyoming; so if Sisecam Resources LP was valued at US$495 million, the entire Sisecam Wyoming is worth US$971 million, and Natural Resources LP’s 49% stake in Sisecam Wyoming is worth US$476 million.
• Sisecam Wyoming is one of the largest and lowest cost producers of soda ash in the world, serving a global market from its facility located in the Green River Basin of Wyoming. The Green River Basin geological formation holds the largest, and one of the highest purity, known deposits of trona ore in the world, in fact the vast majority of the world’s accessible trona is located in the Green River Basin. Trona is a naturally occurring soft mineral and is also known as sodium sesquicarbonate. Trona consists primarily of sodium carbonate (or soda ash), sodium bicarbonate, and water. Sisecam Wyoming processes trona ore into soda ash, which is an essential raw material in flat glass, container glass, detergents, chemicals, paper and other consumer and industrial products.
• Around 30% of global soda ash is produced by processing trona, with the remainder being produced synthetically through chemical processes. Synthetic production of soda ash is more expensive than the costs for mining trona for trona-based production. In addition, trona-based production consumes less energy and produces fewer undesirable by-products than synthetic production.
• Sisecam Wyoming’s Green River Basin surface operations are situated on approximately 2,360 acres in Wyoming (of which, 880 acres are owned by Sisecam Wyoming), and its mining operations consist of approximately 24,000 acres of leased and licensed subsurface mining area. 

Business – Customers

• There is customer concentration for the whole of Natural Resource Partners LP, and also for the Soda Ash segment.
• Natural Resource Partners LP’s revenue from (1) Alpha Metallurgical Resources was US$102.4 million in 2022, which accounted for 37% of the year’s total revenue and (2) Foresight Energy Resources was US$65.6 million, which accounted for 24% of the year’s total revenue.
• For the Soda Ash segment, the two largest customers of Sisecam Wyoming are distributors in its export network that collectively made up 26% of its total gross revenue.

Business – Commodity prices

• Even though Natural Resource Partners LP’s royalty fees are typically supported by a floor price and minimum payment obligation that protects Natural Resource Partners LP during significant price or demand declines, the limited partnership is still affected by price swings in commodity prices.
• In 2022, met coal and thermal coal prices both reached record highs in 2022; met coal prices was the primary driver of Natural Resource Partners LP’s strong Mineral Rights segment performance in 2022. See Table 1 below for Mineral Rights segment performance in 2022.
• In 9M 2023, met coal and thermal coal prices were both below record highs seen in 2022 – the Mineral Rights segment saw a dip in performance in 9M 2023, as shown in Table 1.

Management

• Corbin Robertson, Jr, 75, has served as CEO and Chairman of the Board of Directors of GP Natural Resource Partners LLC since 2002; GP Natural Resources LLC has managed Natural Resource Partners LP since its formation and listing in 2002.
• 2015 was a tough year for Natural Resource Partners LP as commodity prices crashed and it had too much debt. Since then, Natural Resource Partners LP has dramatically improved its financial health. See Figures 5, 6, and 7.

Valuation

• Unit price of Natural Resource Partners LP: US$96.93
• Market cap of Natural Resource Partners LP: US$1.225 billion
• Enterprise value of Natural Resource Partners LP: US$1.41 billion
• Value of Natural Resource Partners LP’s stake in Sisecam Wyoming is US$476 million, so the market is assigning a value of US$938 million for the Mineral Rights segment
• Trailing free cash flow as of 30 Sep 2023 is US$304 million (lion’s share comes from the Mineral Rights segment since most of net income is from the segment), so the Mineral Rights segment is valued at just 3x FCF. Worth noting that Natural Resource Partners LP’s FCF has been relatively stable since 2015 – see Figure 8
• In Figure 6 above, it is worth noting that Natural Resource Partners LP’s aim is to “retire all permanent debt, redeem all the 12% preferred equity, and eliminate all outstanding warrants, all of which will require approximately US$325 million.” 
• On the 12% preferred equity, Natural Resource Partners LP issued US$250 million of the preferred equity units in March 2017 at a price of US$1,000 per preferred equity unit. The preferred equity is convertible to common units, but Natural Resource Partners LP can choose to redeem the preferred equity for cash. The outstanding balance of the preferred equity as of 30 September 2023 is US$72 million. Once all the preferred equity is cleared, Natural Resource Partners LP can save US$30 million in annual coupon payments (based on US$250 million issue), and this adds directly to free cash flow; if the US$72 million outstanding balance is fully cleared, Natural Resource Partners LP can save US$8.6 million in annual coupon payments.

 

Appendix

Chart showing Sisecam Wyoming and Sisecam Resources LP’s ownership structure before and after the February 2023 announcement of the acquisition by Sisecam Chemicals USA

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