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 27 July 2025:

1. Introducing pay per crawl: Enabling content owners to charge AI crawlers for access – Will Allen and Simon Newton

Many publishers, content creators and website owners currently feel like they have a binary choice — either leave the front door wide open for AI to consume everything they create, or create their own walled garden. But what if there was another way?…

…We believe your choice need not be binary — there should be a third, more nuanced option: You can charge for access. Instead of a blanket block or uncompensated open access, we want to empower content owners to monetize their content at Internet scale…

…Pay per crawl, in private beta, is our first experiment in this area. 

Pay per crawl integrates with existing web infrastructure, leveraging HTTP status codes and established authentication mechanisms to create a framework for paid content access…

…At its core, pay per crawl begins a technical shift in how content is controlled online. By providing creators with a robust, programmatic mechanism for valuing and controlling their digital assets, we empower them to continue creating the rich, diverse content that makes the Internet invaluable. 

We expect pay per crawl to evolve significantly. It’s very early: we believe many different types of interactions and marketplaces can and should develop simultaneously. We are excited to support these various efforts and open standards.

For example, a publisher or new organization might want to charge different rates for different paths or content types. How do you introduce dynamic pricing based not only upon demand, but also how many users your AI application has? How do you introduce granular licenses at internet scale, whether for training, inference, search, or something entirely new?

The true potential of pay per crawl may emerge in an agentic world. What if an agentic paywall could operate entirely programmatically? Imagine asking your favorite deep research program to help you synthesize the latest cancer research or a legal brief, or just help you find the best restaurant in Soho — and then giving that agent a budget to spend to acquire the best and most relevant content. By anchoring our first solution on HTTP response code 402, we enable a future where intelligent agents can programmatically negotiate access to digital resources. 

2. How It’s Done – Doomberg

Among the critical minerals China has successfully cornered are the rare earth metals, and the primary means by which it achieved near-total dominance was by capturing the step at which the mined material—a concentrated mix of many valuable metals—is purified into individual components suitable for use in various military and industrial applications. Copious amounts of waste are produced along that processing journey, and treating such waste to Western standards became economically unfeasible at the market prices that prevailed after China entered the field. Last week, The New York Times caught on to how the game is played:

“Chinese mines and refineries produce most of the world’s rare earth metals and practically all of a few crucial kinds of rare earths. This has given China’s government near complete control over a critical choke point in global trade. But for decades in northern China, toxic sludge from rare earth processing has been dumped into a four-square-mile artificial lake. In south-central China, rare earth mines have poisoned dozens of once-green valleys and left hillsides stripped to barren red clay.”…

…With free markets clearly failing to price environmental and national security concerns—let alone the convergence of both—a completely new approach was needed to address the rare earth vulnerability. Last week brought the announcement of just such a move:

“The Defense Department will become the largest shareholder in rare-earth mining company MP Materials by buying $400 million of its stock and helping it build a new processing facility to sidestep the Chinese market, the company said Thursday. The deal underscores how far the Trump administration is willing to go to subsidize production of high-powered magnets, a field dominated by Chinese firms although the materials are critical for U.S. weapons systems.

Las Vegas-based MP Materials owns the only rare-earth mine in the United States, at Mountain Pass, California, near the Nevada border. MP Materials CEO Jim Litinsky said the company aims to restore the full rare-earth supply chain in the U.S. and eliminate a ‘single point of failure’ in the country’s military-industrial base.”

Perusing the company’s press release and other corporate filings, the details of the creative deal become clear. The Pentagon is taking a holistic approach to the objective, investing the capital needed for MP Materials to construct domestic processing and magnetic facilities while also putting a floor price under the company’s products that accounts for the cost of proper environmental stewardship:

“DoD has entered into a 10-year agreement establishing a price floor commitment of $110 per kilogram for MP Materials’ NdPr products stockpiled or sold, reducing vulnerability to non-market forces and ensuring stable and predictable cash flow with shared upside.

For a period of 10 years following the construction of the 10X Facility, DoD has agreed to ensure that 100% of the magnets produced at the 10X Facility will be purchased by defense and commercial customers with shared upside.”

3. Could AI slow science? -Sayash Kapoor and Arvind Narayanan

It’s a common-sense view, at least among technologists, that AI will speed science greatly as it gets adopted in every part of the scientific pipeline — summarizing existing literature, generating new ideas, performing data analyses and experiments to test them, writing up findings, and performing “peer” review…

…The impact of AI on science could be counterintuitive. Even if individual scientists benefit from adopting AI, it doesn’t mean science as a whole will benefit…

… So far, on balance, AI has been an unhealthy shock to science, stretching many of its processes to the breaking point.

Any serious attempt to forecast the impact of AI on science must confront the production-progress paradox. The rate of publication of scientific papers has been growing exponentially, increasing 500 fold between 1900 and 2015. But actual progress, by any available measure, has been constant or even slowing. So we must ask how AI is impacting, and will impact, the factors that have led to this disconnect.

Our analysis in this essay suggests that AI is likely to worsen the gap. This may not be true in all scientific fields, and it is certainly not a foregone conclusion…

…There’s something suboptimal about the way we’ve structured the practice of science, and so the efficiency of converting scientific inputs into progress is dropping. In particular, one subset of hypotheses flags the increase in the rate of production itself as the causal culprit — science is slowing down because it is trying to go too fast.

How could this be? The key is that any one scientist’s attention is finite, so they can only pay attention to a limited number of papers every year. So it is too risky for authors of papers to depart from the canon. Any such would-be breakthrough papers would be lost in the noise and won’t get the attention of a critical mass of scholars. The greater the rate of production, the more the noise, so the less attention truly novel papers will achieve, and thus will be less likely to break through into the canon…

…Another causal mechanism relates to scientists’ publish-or-perish incentives. Production is easy to measure, and progress is hard to measure. So universities and other scientific institutions judge researchers based on measurable criteria such as how many papers they publish and the amount of grant funding they receive. It is not uncommon for scientists to have to publish a certain number of peer-reviewed papers to be hired or to get tenure (either due to implicit norms or explicit requirements)…

…This completes the feedback loop: career incentives lead to researchers publishing more papers, and disincentivize novel research that results in true breakthroughs (but might only result in a single paper after years of work).

If slower progress is indeed being caused by faster production, how will AI impact it? Most obviously, automating parts of the scientific process will make it even easier for scientists to chase meaningless productivity metrics. AI could make individual researchers more creative but decrease the creativity of the collective because of a homogenizing effect. AI could also exacerbate the inequality of attention and make it even harder for new ideas to break through…

…The AI community often advertises AI as a silver bullet without realizing how difficult it is to detect subtle errors. Unfortunately, it takes much less competence to use AI tools than to understand them deeply and learn to identify errors. Like other software-based research, errors in AI-based science can take a long time to uncover. If the widespread adoption of AI leads to researchers spending more time and effort conducting or building on erroneous research, it could slow progress, since researcher time and effort are wasted in unproductive research directions.

Unfortunately, we’ve found that AI has already led to widespread errors. Even before generative AI, traditional machine learning led to errors in over 600 papers across 30 scientific fields. In many cases, the affected papers constituted the majority of the surveyed papers, raising the possibility that in many fields, the majority of AI-enabled research is flawed…

…Older modeling techniques required coming up with a hypothesis for how the world works, then using statistical models to make inferences about this hypothesis.

In contrast, AI-based modeling treats this process as a black box. Instead of making a hypothesis about the world and improving our understanding based on the model’s results, it simply tries to improve our ability to predict what outcomes would occur based on past data…

…AI-based modeling is no doubt helpful in improving predictive accuracy. But it doesn’t lend itself to an improved understanding of these phenomena. AI might be fantastic at producing the equivalents of epicycles across fields, leading to the prediction-explanation fallacy.

In other words, if AI allows us to make better predictions from incorrect theories, it might slow down scientific progress if this results in researchers using flawed theories for longer. In the extreme case, fields would be stuck in an intellectual rut even as they excel at improving predictive accuracy within existing paradigms…

…Researchers across fields are incentivized to find solutions to scientific problems. But this incentive only leads to progress because the process of proving theorems or finding solutions to problems also leads to building human understanding. As the desertion of work on foliations shows, when there is a mismatch between finding solutions to problems and building human understanding, it can result in slower progress.

This is precisely the effect AI might have: by solving open research problems without leading to the accompanying understanding, AI could erode these useful byproducts by reducing incentives to build understanding. If we use AI to short circuit this process of understanding, that is like using a forklift at the gym. You can lift heavier weights with it, sure, but that’s not why you go to the gym…

…If we use AI to bypass human understanding, or worse, retain only illusions of understanding, we might lose the ability to train new scientists, develop new theories and paradigms, synthesize and correct results, apply knowledge beyond science, or even generate new and interesting problems.

Empirical evidence across scientific fields has found evidence for some of these effects. For example, Hao et al. collect data from six fields and find that papers that adopt AI are more likely to focus on providing solutions to known problems and working within existing paradigms rather than generating new problems.

4. AI Comes Up with Bizarre Physics Experiments. But They Work – Anil Ananthaswamy

In the classical physics that describes our everyday world, objects have well-defined properties that are independent of attempts to measure those properties: A billiard ball, for example, has a particular position and momentum at any given moment in time.

In the quantum world, this isn’t the case. A quantum object is described by a mathematical entity called the quantum state. The best one can do is to use the state to calculate the probability that the object will be, say, at a certain location when you look for it there.

What is more, two (or more) quantum objects can share a single quantum state. Take light, which is made of photons. These photons can be generated in pairs that are “entangled,” meaning that the two photons share a single, joint quantum state even if they fly apart. Once one of the two photons is measured, the outcome seems to instantaneously determine the properties of the other — now distant — photon.

For decades, physicists assumed that entanglement required quantum objects to start out in the same place. But in the early 1990s, Anton Zeilinger(opens a new tab), who would later receive the Nobel Prize in Physics for his studies of entanglement, showed that this wasn’t always true. He and his colleagues proposed an experiment that began with two unrelated pairs of entangled photons. Photons A and B were entangled with each other, as were photons C and D. The researchers then devised a clever experimental design(opens a new tab) made of crystals, beam splitters and detectors that would operate on photons B and C — one photon from each of the two entangled pairs. Through a sequence of operations, the photons B and C get detected and destroyed, but as a product, the partner particles A and D, which had not previously interacted, become entangled. This is called entanglement swapping, which is now an important building block of quantum technology.

That was the state of affairs in 2021, when Krenn’s team started designing new experiments with the aid of software they dubbed PyTheus…

…The team represented optical experiments using mathematical structures called graphs, which are composed of nodes connected by lines called edges. The nodes and edges represented different aspects of an experiment, such as beam splitters, the paths of photons, or whether or not two photons had interacted.

Krenn’s team started by first building a very general graph, one that modeled the space of all possible experiments of some size. The graph had output features that represented some desired quantum state…

…The question, then, was how to modify all the other parts of the graph to produce this state. To figure this out, the researchers formulated a mathematical function. It took in the state of the graph and calculated the difference between the output of the graph and the desired quantum state. They then iteratively modified the graph’s parameters, which represented the experimental configuration, to reduce this discrepancy to zero.

When Krenn’s student Soren Arlt tried to use this approach to find the best way to do entanglement swapping, he noticed that the experimental configuration was unrecognizable — nothing at all like Zeilinger’s design from 1993. “When he showed it to me, we were confused,” Krenn said. “I was convinced that it must be wrong.”

The optimization algorithm had borrowed ideas from a separate area of study called multiphoton interference. By doing so, it created a simpler configuration(opens a new tab) than Zeilinger’s. Krenn’s team then did a separate mathematical analysis of the final design. It confirmed that the new experimental design would in fact create entanglement among particles with no shared past.

In December 2024, a team in China led by Xiao-Song Ma of Nanjing University confirmed it(opens a new tab). They built the actual experiment, and it worked as intended.

5. Get Smart: How to Profit in a Fast-Moving Stock Market – Chin Hui Leong

Here’s the good news: when it comes to investing, the winner is not always the one with the fastest fingers.

While news may reach your eyes faster, the actual change in businesses takes time to materialise.

Thus, even if you react faster, it doesn’t necessarily mean you will be right.

Need an example?

In my Business Time article last Wednesday, I highlighted how the initial hype over DeepSeek in late January 2025 has largely died down.

In the process, those who sold Nvidia (NASDAQ: NVDA) right after the DeepSeek news broke out will be rueing the fact that the GPU provider has delivered revenue gains of 78% and 69% year on year, respectively, for the past two quarters.

In turn, shares have risen by nearly 45% from their January low…

…In other words, slowing down, taking your time to assess the situation, and listening to the contrasting arguments will lead to better outcomes…

…But what if a threat turns out to be real and you were right to sell?

It’s possible, of course.

Here’s a common narrative: BlackBerry’s (NYSE: BB) reign as the go-to device in the corporate world was cut short by the rapid rise in popularity of Apple’s (NASDAQ: AAPL) iPhone and Alphabet’s (NASDAQ: GOOGL) Android…

…It’s easy to assume that the decline was immediate, but the opposite is true.

Between fiscal 2007 and fiscal 2011, the Canadian company’s sales actually soared by over sixfold from US$3 billion to almost US$20 billion.

In other words, Blackberry experienced a period of tremendous growth for over four years before its business began to falter.

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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 and Apple. 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 20 July 2025:

1. Sweatshop data is over – Tamay Besiroglu, Matthew Barnett, Ege Erdil

Historically, the importance of data has been underrated in the field of AI. Decades ago, many assumed the key to AGI would come from devising the right “theory of intelligence”, which we could then implement by hand; the role of training data was sidelined.

Despite being trained on more compute than GPT-3, AlphaGo Zero could only play Go, while GPT-3 could write essays, code, translate languages, and assist with countless other tasks. The main difference was training data. AlphaGo Zero learned from Go games, whereas GPT-3 learned from natural language. This meant that while Google was playing games, OpenAI was able to seize the opportunity of a lifetime. What you train on matters.

We may soon witness a similar lesson if AI labs continue to scale up their models without similarly scaling up the quality of their training environments. Many have observed that pretraining is already saturating. GPT-4.5, while impressive in its own right, didn’t feel like a major generational leap in the way GPT-4 did over GPT-3.5.

The recent reinforcement learning with verifiable rewards (RLVR) paradigm seeks to revive progress by getting AIs to learn how to perform formally checkable reasoning inside contained environments. What we’ve seen so far is necessary for progress, but it is far from sufficient. Current methods will get us to the point where AIs can prove theorems and solve hard puzzles, but it won’t be enough to get models to deal with the open-ended nature of reality, where the quality of our actions cannot be so easily “verified” as either correct or incorrect.

To make progress, there’s no way around designing better rewards, and ultimately better RL environments.

2. Silk, Porcelain, Tea, Opium: 2000 Years of Trade Deficit with China – Tomas Pueyo

The West has had deficits with China for over 2,000 years, and they have had a massive impact on world history, from the opening of global trade routes, to the establishment of colonies, colonial policies, international wars, the emergence of nation-states, the politics of present-day China and the US…

…Romans loved luxury goods:

India, China and the Arabian peninsula take one hundred million sesterces1 from our empire per annum at a conservative estimate: that is what our luxuries and women cost us—Pliny the Elder, Natural History (77–79 AD).

Of these, silk was the biggest import from China. In 14 AD the Senate prohibited the wearing of silk by men!

To pay for it, Romans traded glassware, amber, wine, carpets, and other goods,2 but they didn’t make up for the value of what Romans bought from China. And in general, Chinese traders preferred money—mostly gold and silver—over other goods…

…Europeans obsessed about producing silk locally, but they didn’t know how to make it and didn’t have silkworms: China had protected its near-monopoly on silk for many centuries thanks to imperial orders to execute anybody caught trying to export silkworms or their eggs. The only way to succeed was by stealing them, and that’s precisely what two Christian monks did around 550 AD, risking their lives to smuggle silkworms hidden inside their canes.

This started silk production in the Eastern Roman Empire, which would slowly permeate through the rest of Europe.

This might have been the first time Chinese manufacturing prowess caused a trade imbalance in the West that required political intervention…

…Porcelain could only start reaching Europe in the 1500s,4 which is not a coincidence either: Porcelain was too heavy and fragile for overland routes, so it needed a maritime route to reach Europe. The Portuguese found a path to the Indies circumventing Africa just around 1500…

…Chinese porcelain was so much thinner, whiter and more translucent than local wares that European nobility really prized it…

…You know how nowadays Westerners design some products and then they send those designs to China for manufacture?

Porcelain is another example of China manufacturing products that Europeans craved, but again it didn’t need anything Europeans produced. Except for silver. So silver flowed from Europe to China. From 1500 to 1800, Bolivia and Mexico’s mines produced about 80% of the world’s silver; 30% of that eventually ended up in China!

Europeans hated that flow, as the silver disappeared as fast as it was produced, so they tried to stop it. Of course, the most incentivized were the countries who didn’t have access to either silver or trade with China. This is why the Italians tried to copy porcelain in the late 1500s with Medici porcelain, although they largely failed. By the early 1700s, Germans succeeded. A few years later, in 1712, the French Jesuit father Francois Xavier d’Entrecolles published the secrets of porcelain making in Europe, which he had read about and witnessed in China. In the following decades, the local production of porcelain increased and the import of Chinese porcelain fell…

…Tea’s ever-escalating trade imbalance with China became a serious economic problem, so much so that the British King George III sent an envoy to the Chinese Emperor to ask for more trade liberalization. These are excerpts of the Emperor’s response:

Our Celestial Empire possesses all things in prolific abundance and lacks no product within its own borders. There is therefore no need to import the manufactures of outside barbarians in exchange for our own produce. But as the tea, silk and porcelain which the Celestial Empire produces, are absolute necessities to European nations and to yourselves, we have permitted, as a signal mark of favor, that foreign merchants should be established at Canton, so that your wants might be supplied and your country thus participate in our beneficence.

So what did the British do to solve the trade imbalance? Two things. One is that the East India Company sent Scottish botanist Robert Fortune to China to purchase and export Chinese tea plants in the 1850s. This kick-started tea production in India, which grew over the following decades, reducing the share of Chinese tea consumed. Here we have, for the third time, a smuggling of Chinese production know-how to reduce trade imbalances…

…When the British conquered India8 in the late 1700s, they were very conscious about their trade imbalance with China, so they looked for any way to reduce it. They found the right tool in opium. They devised a plan to produce it in India and sell it in China. So the British drove local farmers in eastern India out of crop production and into poppies, from which opium is derived.

Then, the British introduced opium smoking in China…

…The Emperor Jiaqing noticed all this so he published an edict to stop it in 1810:

Opium has a harm. Opium is a poison, undermining our good customs and morality. Its use is prohibited by law.

But the government couldn’t enforce it. When the Chinese government finally cracked down on opium in 1839, the opium trade was paying for all the tea trade and then some, so the British reacted to protect the trade and attacked China; this was the First Opium War.

Britain won and bent China’s arm: It would be allowed to sell opium in China. It also took over Hong Kong.

There would be another Opium War, after which the British, and then other Westerners10 could reach far inland in China to sell opium. The deficit to China became a surplus. Over the following decades, opium addiction became widespread. By 1949, 4.4% of Chinese people were addicted. Local farmers replaced their crops with opium. Governments used opium taxes to finance themselves, and this lasted until the Communist Party had a strong enough chokehold on society and culture to finally ban opium.

This is what the Chinese call the century of humiliation, when China went from the richest and most advanced nation of the world to a dirt poor backwater.

3. The Codes AI Can’t Crack – Taras Grescoe

Since 2018, neural networks trained on cuneiform, the writing system of Mesopotamia, have been able to fill in lost verses from the story of Gilgamesh, the world’s earliest known epic poem. In 2023, a project known as the Vesuvius Challenge used 3D scanners and artificial intelligence to restore handwritten texts that hadn’t been read in 2,000 years, revealing previously unknown works by Epicurus and other philosophers. (The scrolls came from a luxurious villa in Herculaneum, buried during the same eruption of Mount Vesuvius that destroyed Pompeii. When scholars had previously tried to unroll them, the carbonized papyrus crumbled to dust.)

Yet despite these advances, a dozen or so ancient scripts — the writing systems used to transcribe spoken language — remain undeciphered. These include such mysteries as the one-of-a-kind Phaistos Disk, a spiral of 45 symbols found on a single sixteen-inch clay disk in a Minoan palace on Crete, and Proto-Elamite, a script used 5,000 years ago in what is now Iran, which may have consisted of a thousand distinct symbols. Some, like Cypro-Minoan — which transcribes a language spoken in the Late Bronze Age on Cyprus — are tantalizingly similar to early European scripts that have already been fully deciphered. Others, like the quipu of the Andes — intricately knotted ropes made of the wool of llamas, vicuñas, and alpacas — stretch our definitions of how speech can be transformed into writing…

…Cracking these ancient codes may seem like the kind of challenge AI is ideally suited to solve. After all, neural networks have already bested human champions at chess, as well as the most complex of all games, Go. They can detect cancer in medical images, predict protein structures, synthesize novel drugs, and converse fluently and persuasively in 200 languages. Given AI’s ability to find order in complex sets of data, surely assigning meaning to ancient symbols would be child’s play.

But if the example of Ithaca shows the promise of AI in the study of the past, these mystery scripts reveal its limitations. Artificial neural networks might prove a crucial tool, but true progress will come through collaboration between human neural networks: the intuitions and expertise stored in the heads of scholars, working in different disciplines in real-world settings…

…Ithaca was trained on ancient Greek, a language we’ve long known how to read, and whose entire corpus amounts to tens of thousands of inscriptions. The AI models that have filled in lost verses of Gilgamesh are trained on cuneiform, whose corpus is even larger: hundreds of thousands of cuneiform tablets can be found in the storerooms of the world’s museums, many of them still untranslated. The problem with mystery scripts like Linear A, Cypro-Minoan, Rongorongo, and Harappan is that the total number of known inscriptions can be counted in the thousands, and sometimes in the hundreds. Not only that, in most cases we have no idea what spoken language they’re meant to encode…

… Two of the greatest intellectual feats of the 20th century involved the decipherment of ancient writing systems. In 01952, when Michael Ventris, a young English architect, announced that he’d cracked the code of Linear B, a script used in Bronze Age Crete, newspapers likened the accomplishment to the scaling of Mount Everest. (Behind the scenes, the crucial grouping and classifying of characters on 180,000 index cards into common roots — the grunt work that would now be performed by AI — was done by Alice Kober, a chain-smoking instructor from Brooklyn College.)

The decipherment of the Maya script, which is capable of recording all human thought using bulbous jaguars, frogs, warriors’ heads, and other stylized glyphs, involved a decades-long collaboration between Yuri Knorozov, a Soviet epigrapher, and American scholars working on excavations in the jungles of Central America.

While the interpreting of Egyptian hieroglyphics is held up as a triumph of human ingenuity, the Linear B and Mayan codes were cracked without the help of a Rosetta Stone to point the way. With Linear B, the breakthrough came when Ventris broke with the established thinking, which held that it transcribed Etruscan — a script scholars can read aloud, but whose meaning still remains elusive — and realized that it corresponded to a form of archaic Greek spoken 500 years before Homer. In the case of ancient Mayan, long thought to be a cartoonish depiction of universal ideas, it was only when scholars acknowledged that it might transcribe the ancestors of the languages spoken by contemporary Maya people that the decipherment really began. Today, we can read 85% of the glyphs; it is even possible to translate Shakespeare’s Hamlet into ancient Mayan.

Collaborating across cultures and disciplines, and carrying out paradigm-shedding leaps of intuition, are not the strong points of existing artificial neural networks. But that doesn’t mean AI can’t play a role in decipherment of ancient writing systems. Miguel Valério, an epigrapher at the Autonomous University of Barcelona, has worked on Cypro-Minoan, the script used on Cyprus 3,500 years ago. Two hundred inscriptions, on golden jewelry, metal ingots, ivory plaques, and four broken clay tablets, have survived. Valério was suspicious of the scholarly orthodoxy, which attributed the great diversity in signs to the coexistence of three distinct forms of the language.

To test the theory that many of the signs were in fact allographs — that is, variants, like the capital letter “G” and “g,” its lower-case version — Valério worked with Michele Corazza, a computational linguist at the University of Bologna, to design a custom-built neural network they called Sign2Vecd. Because the model was unsupervised, it searched for patterns without applying human-imposed preconceptions to the data set.

“The machine learned how to cluster the signs,” says Valério, “but it didn’t do it simply on the basis of their resemblance, but also on the specific context of a sign in relation to other signs. It allowed us to create a three-dimensional plot of the results. We could see the signs floating in a sphere, and zoom in to see their relationship to each other, and whether they’d been written on clay or metal.”…

…A generation ago, most people were taught that writing was invented once, in Mesopotamia, about 5,500 years ago, as a tool of accountancy and state bureaucracy. From there, the standard thinking went, it spread to Egypt, and hieroglyphics were simplified into the alphabet that became the basis for recording most European languages…

…Monogenesis, the idea that the Ur-script diffused from Mesopotamia, has been replaced by the recognition that writing was invented independently in China, Egypt, Central America, and — though this remains controversial — in the Indus Valley, where 4,000 inscriptions been unearthed in sites that were home to one of the earliest large urban civilizations.

4. A 37,000-Year Chronicle of What Once Ailed Us – Carl Zimmer

On Wednesday, a team of scientists unveiled a new genetic chronicle, documenting the rise of 214 diseases across Europe and Asia over the past 37,000 years…

…The researchers examined the remains of 1,313 ancient individuals for the project. The large scale enabled the researchers to do more than just push back the earliest known occurrence of different diseases. They could also track the rise and fall of epidemics across centuries.

The oldest remains the researchers studied belonged to hunter-gatherers. Their bones and teeth contained a host of pathogens, such as hepatitis B, herpes virus and Helicobacter pylori, a stomach-dwelling bacterium.

“As far back as we go, humans have had infectious diseases,” said Eske Willerslev, a geneticist at the University of Copenhagen and an author of the new study…

…Initially, Dr. Willerslev and his colleagues assumed that they would see such diseases rise to prominence starting about 11,000 years ago. That’s when people started domesticating animals, from which new diseases could spread more easily…

…But the ancient DNA defied that expectation. The scientists found that plague and a number of other diseases jumped to people from animals thousands of years later, starting about 6,000 years ago. And those microbes did not jump into early farmers.

Instead, the new study points to nomadic tribes in Russia and Asia. Thousands of years after the dawn of agriculture, those nomads started rearing vast herds of cattle and other livestock.

Why diseases would have attacked those herders instead of earlier farmers, the scientists can’t say for sure. “We haven’t been able to come up with anything conclusive,” Dr. Willerslev said…

…The nomads expanded over the next few centuries across the steppes of Asia and eastern Europe. In that time, their pathogens thrived; the scientists frequently found several individuals in a single grave with DNA from plague or other diseases.

Those epidemics were so intense that they changed the genetic profile of the nomads. Last year, Dr. Willerslev and his colleagues found that the nomads experienced a spike in mutations that boosted their immune system and that may have helped them resist the diseases they contracted. But their active immune systems may have also attacked their own bodies, producing chronic diseases such as multiple sclerosis.

5. AI is killing the web. Can anything save it? – The Economist

Similarweb, which measures traffic to more than 100m web domains, estimates that worldwide search traffic (by humans) fell by about 15% in the year to June. Although some categories, such as hobbyists’ sites, are doing fine, others have been hit hard (see chart). Many of the most affected are just the kind that might have commonly answered search queries. Science and education sites have lost 10% of their visitors. Reference sites have lost 15%. Health sites have lost 31%.

For companies that sell advertising or subscriptions, lost visitors means lost revenue…

…Google has insisted that its use of others’ content is fair. But since it launched its AI overviews, the share of news-related searches resulting in no onward clicks has risen from 56% to 69%, estimates Similarweb. In other words, seven in ten people get their answer without visiting the page that supplied it…

…To keep the traffic and the money coming, many big content producers have negotiated licensing deals with AI companies, backed up by legal threats: what Robert Thomson, chief executive of News Corp, has dubbed “wooing and suing”. His company, which owns the Wall Street Journal and the New York Post, among other titles, has struck a deal with OpenAI. Two of its subsidiaries are suing Perplexity, another AI answer engine. The New York Times has done a deal with Amazon while suing OpenAI. Plenty of other transactions and lawsuits are going on…

…Reddit, an online forum, has licensed its user-generated content to Google for a reported $60m a year…

…The bigger problem, however, is that most of the internet’s hundreds of millions of domains are too small to either woo or sue the tech giants. Their content may be collectively essential to AI firms, but each site is individually dispensable. Even if they could join forces to bargain collectively, antitrust law would forbid it. They could block AI crawlers, and some do. But that means no search visibility at all…

…All of Cloudflare’s new customers will now be asked if they want to allow AI companies’ bots to scrape their site, and for what purpose. Cloudflare’s scale gives it a better chance than most of enabling something like a collective response by content sites that want to force AI firms to cough up. It is testing a pay-as-you-crawl system that would let sites charge bots an entry fee…

…An alternative is offered by Tollbit, which bills itself as a paywall for bots. It allows content sites to charge AI crawlers varying rates: for instance, a magazine could charge more for new stories than old ones. In the first quarter of this year Tollbit processed 15m micro-transactions of this sort, for 2,000 content producers including the Associated Press and Newsweek…

…One of Tollbit’s highest per-crawl rates is charged by a local newspaper.

Another model is being put forward by ProRata, a startup led by Bill Gross, a pioneer in the 1990s of the pay-as-you-click online ads that have powered much of the web ever since. He proposes that money from ads placed alongside AI-generated answers should be redistributed to sites in proportion to how much their content contributed to the answer. ProRata has its own answer engine, Gist.ai, which shares ad revenue with its 500-plus partners, which include the Financial Times and the Atlantic…

…As for the idea that Google is disseminating less human traffic than before, Mr Stein says the company has not noticed a dramatic decline in the number of outbound clicks, though it declines to make the number public. There are other reasons besides AI why people may be visiting sites less. Maybe they are scrolling social media. Maybe they are listening to podcasts.

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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 (the company behind AlphaGo Zero and Google). 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 13 July 2025:

1. Jim Chanos on the Nuttiness of ‘Bitcoin Treasury Companies’ | Odd Lots (Transcript Here) – Tracy Alloway, Joe Weisenthal, and Jim Chanos

Joe: All right, first question: Are Bitcoin treasury companies the stupidest thing you’ve ever seen in your entire life?

Jim Chanos: It’s rarely, rarely that I have to increase my personal security after a podcast which I had to do after our last podcast together when I said some intemperate things about Bitcoin treasury companies.

Here’s the thing. I get people very agitated about this and they point out just what a genius idea this is and I keep trying to point out to them I’m doing the same thing that guys like Michael Saylor are doing. I’m on the same side of the trade and I keep pointing out to my critics, “You’re on the opposite side of that trade and you don’t want to be on the opposite side of the trade, and the Bitcoin treasury paradox being that you are the one buying the pieces of paper that have infinite supply so that Michael Saylor and I can buy the digital asset with the limited supply and it makes kind of no sense.” So what will inevitably happen is happening, in that there’s nothing proprietary here – this is just simply raising capital to buy a financial asset and other companies will do this. In fact even since the podcast we last did, I think the number of companies that have announced this strategy is scores more. I think there’s over a hundred in the US and over 200 globally now…

…Jim: Because there’s a wonderful sales job that’s being done about the fact that this is an economic engine in and of itself, therefore terms like Bitcoin Yield are are used and I’ve called them financial gibberish – because they are. In fact, this will get arbed away ultimately by companies that will do this to try to capture that spread. In the case of Micro Strategy, it’s substantial. It’s still $50 billion, something like that, of the difference between the value of the enterprise value of the company and the value of their Bitcoin holdings. But the thing that really shot me into orbit on all this was when Saylor and others then said, “You can’t really value us on an NAV basis, a so-called MNAV, multiple of NAV. You actually have to also give us additional value for the amount of profit that we make every quarter from the appreciation in the asset.” I said, “Well that’s like saying my whole net worth is in a house that’s worth $400,000 that is now worth $500,000 a year or two later, and my net worth is not $500,000 now – it’s $2.5 million because it’s the value of the house plus a multiple on the increase in the profitability of the asset.”…

…Tracy: I have one more question why did Micro – I have to remember to call them Strategy but I can’t bring myself to do it. Why did they switch from issuing the convertible debt to preferred shares?

Jim: Because he realized that as he began to issue more and more common, it was putting pressure on the premium. Now the latest iteration is, “We’re going to do this quasi equity security, quasi debt, preferred stock and then we can lever up the the balance sheet.” This is a company whose selling point a year ago was “We’re not going to lever, because we have this wonderful equity that we can issue at a premium.” Now they’re saying, “Maybe if it trades above 2x we’ll issue equity, but if it’s between 1x and 2x, we’ll do preferred, and then if it’s below 1x we’ll buy back common and then what is Chanos going to do?” To which I said, “I’ll be out of the trade by then.” If it’s 1x NAV it’s not a trade. That’s the latest game plan – but stay tuned, it’ll change, I think. The narrative keeps changing…

…Jim: The legacy data centers – and there’s only a couple companies in the United States that really have legacy data centers. There’s Equinix, there’s Digital Realty, and then there’s old Colony Capital – it’s now called Digital Bridge and they own these things in fund format.

When we took a look at this with our partner back in ‘22 the idea was pretty simple. We did not see the AI explosion in mid-’22, but the idea was it was a pretty crummy business then, working on the cloud and SaaS demand. But it became a really bad business with the advent of AI because it just moved the hyperscalers to invest more in state-of-the-art data centers. These are older data centers that we’re short, the idea being that the new GPU-centric data centers need liquid cooling – they basically need all the infrastructure ripped out and replaced – and the business was not a high return on capital business before this. It’s getting even worse now.

What Equinix said yesterday at their Analyst Day was that revenues were not going to quite be what people thought they would be, but more ominously, capex was going to keep increasing. That’s what we’ve been saying, that these are not like warehouses where you just collect a check. These are actually operating businesses where you have to service the servers, you have to make sure there’s redundancy. It’s a business, a tech business, and they’re traded as REITs and that was the opportunity. That was the dichotomy in valuation. People added back the depreciation as they do with REITs and they valued them on a so-called FFO or AFFO, which is a cash flow metric. But in fact, unlike warehouses, shopping centers to a lesser extent, office buildings, the capex was real. Depreciation was a real expense. To give you an example, with Equinix yesterday, they said “Our capex is now going to bump up to between $4 billion and $5 billion a year.” The problem is their EBITDA this year is expected to be $4.5 billion, so all of that’s going to go to capex, meaning they’re going to have to basically borrow or issue equity to pay their interest and dividends. That’s just a definition of a bad business and it’s a business that’s not growing very fast. Unlike other really true AI companies which are growing 25%, 30%, 40% a year, these guys are growing 3%, 5%, 6% sort of with GDP. So there’s no growing their way out of this. So they’re just really bad businesses trading at just nosebleed valuations.

Tracy: On the topic of idiosyncratic opportunities I got to ask about Carvana because when my husband and I moved back to the States in 2022, we bought a used car through Carvana and that was a mistake. It took us about 6 months to actually get the car and they lost all our paperwork and it was just an absolute nightmare. I thought at the time this is a company whose entire business model is basically built on regulation, that’s what they’re doing and I thought they’re not going to have a future if they are this bad at it. Yet the stock is up.

Joe: It’s done insanely well.

Jim: It’s done a double round trip. It crashed 99% and now it’s up 100x, so it’s pretty interesting again. The reason it’s interesting is that if you go through the numbers, they are making more than 100% of their pre-tax profit from gain on sale of subprime loans and gain on sale of equity stakes in other companies. You ex those two out, they’re losing money and they’re losing money now right after the rebound, after the restructuring from 2022-2023. This is a company that is being valued again as a secular growth stock that saw its used car revenues drop 30% between 2022 and 2023, so it’s not necessarily a secular growth company. The accounting is abysmal. What people are really missing is that what’s happening in subprime auto securizations right now – and you can track it on your Bloomberg terminal – delinquencies are starting to skyrocket.

Tracy: We actually did an episode on this recently with Jim Egan.

Jim: So a huge amount of their profits comes from generating paper from customers and then selling it into the open market or to affiliates. This is a company that was spun out of a company called Drive Time Finance, which is their affiliated finance company which was originally called Ugly Duckling in the late ‘90s which was run by the current CEO’s father. That company collapsed in the first subprime blowup which was not the GFC – it was actually in the late ‘90s in subprime auto credit and consumer loans. It didn’t go bankrupt but it came close. He had to restructure it. He bought it in private and then restructured it, renamed it Drive Time Finance. But that’s the genesis of Carvana. That’s its DNA. It’s basically a subprime finance lead company, if you will. Those companies should not trade at 40x and 50x expected earnings – and they don’t by and large. They’re consumer finance companies. So it’s an odd bird. It’s still heavily leveraged, the stock is up a ton.

But what really got us interested again recently was the vast amount of insider selling that has just started in May and June in the company. If you go look at the insider selling in the company, it is just now a torrent of everybody selling pretty much every day. We just don’t think that’s a good sign given what’s happening in the subprime securization market…

…Jim: Every once in a while. There’s one other thing though I do want to mention. I was talking to someone earlier today and I think one of the things that’s underappreciated by investors right now and one of the things that’s been most interesting to me is how corporate profit margins have held up, which used to be very mean-reverting as you know. The more work we’ve done on this, the more we’re kind of convinced that the capital spending boom we’re seeing due to tech and specifically AI, is is looking very much akin to the global internet buildout networking buildout in the late ‘90s and the problem there of course is that if you buy my chips from NVIDIA or you were buying my networking equipment at Cisco and Lucent, that’s revenue for me and profit. But for you it’s a capitalized expense, it’s written off over time, and that adds a big, big boost until people pull their orders. That’s what we saw in 2001, 2002 that GDP dropped about 1% to 2% in the recession of ‘01-’02. Does anybody know what corporate profits did in that? That was an investment-driven recession. Consumers didn’t feel it at all. Earnings were down about 45% I think from peak to trough in the S&P. They were down about the same, a little bit more in the global financial crisis, but of course GDP collapsed.

Here’s a little interesting thought experiment. Right now NVIDIA’s revenues are about one-half of 1% of US GDP, about $140 billion and our GDP is about $29 trillion. Anyone tell me what Cisco and Lucent – the two companies that you needed when building out your internet network in ‘99, 2000 – did anybody know what their combined revenues as a percent of GDP was in 2000?

Tracy: No using your phones.

Joe: And ChatGPT.

Jim: It was a half a percent. It was roughly $50 billion total on GDP of $10 trillion. So those revenues stopped growing at some point shortly thereafter and actually shrunk a little bit. The investment boom we’re seeing right now, we’ve seen before. And it’s not just chips. It’s Caterpillar, it’s people building the data centers, it’s people building new utilities. There is an ecosystem around the AI boom that is considerable, as there was for TMT back in ‘99 and 2000. But it is a riskier revenue stream because if people pull back, they can pull back capex very easily, projects can get put on hold for six months or nine months, and that immediately shows up in disappointing revenues and earnings forecast if it happens. We’re not there yet but that’s one of the risks out there that I think a lot of people are underestimating.

2. Creating therapeutic abundance – Jacob Kimmel

Jack Scannell infamously predicted in 2012 that the number of drugs per billion dollars would decline two-fold every nine years. Unfortunately, our therapeutics industry has largely followed through…

…Drug program success rates are equally complex. Failures can be attributed to safety issues, failure of a drug to hit the desired biological target, or improper selection of the target for a given disease…

…We can bucket the failures into a two broad categories of safety and efficacy and make informed estimates.

1. Safety failures – ~20-30% of all candidates
A molecule was developed, but proved unsafe in patients. These are typically detected as failures in Phase 1 trials.

2. Efficacy failures – 70-80% of all candidates
The remainder of all drug candidates that fail – 63% of all drugs placed into trials period – fail due to a lack of efficacy. Even though the drugs are safe, they don’t provide benefit to the patients by treating their disease.

From these coarse numbers, it’s clear that the highest leverage point in our drug development process is increasing the efficacy rate of new candidate medicines…

…Efficacy failures can broadly occur for two reasons:

1. Engagement failures: We chose the right biology (“target”) to manipulate, but our drug candidate failed to achieve the desired manipulation. This is the closest thing drug development has to an engineering problem.
2. Target failures: The drug candidate manipulated our chosen biology exactly as expected. Unfortunately, the target failed to have the desired effect on the disease. This is a scientific or epistemic failure, rather than an engineering problem. We simply failed to understand the biology well enough to intervene and benefit patients.

It’s difficult to know exactly the exact frequency of these two failure modes, but we can infer from a few sources that target failures dominate.

• Success rates for biosimilar drugs hitting known targets are extremely high, >80%
• Drugs against targets with genetic evidence have a 2-3 fold higher success rate than those against targets lacking this evidence, suggesting that picking good targets is a high source of leverage
• Among organizations with meaningful internal data, picking the right target is considered the first priority of all programs (e.g. “Right target” is the first tenet of AstraZeneca’s “5Rs” framework).

The predominance of target failures has likewise led most companies working on new modalities to address a small set of targets with well-validated biology. This has led to dozens of potential medicines “crowding” on the same targets, and this trend is increasing over time…

…If searching for targets is the limiting reagent in our medicine production function, the difficulty of finding targets must increase over time in order to explain part of Eroom’s law. How could this be the case given all the improvements in underlying biomedical science?

In an influential paper “Are ideas getting harder to find?”, Nicholas Bloom and colleagues argue that many fields of invention suffer from diminishing returns to investment. Intuitively, the low hanging fruit in a given discipline is picked early and more investment is required merely to reap the same harvest from higher branches on the tree of ideas…

…Targets are getting harder to find not because we are getting worse at selection, but because many of the easy and obvious therapeutic hypotheses have already been exploited….

…While promising, human genetics can only reveal a certain class of targets. The larger the effect size of a genetic variant, the less frequently it appears in the population due to selective pressure. In effect, this means that the largest effects in biology are the least likely to be discovered using human genetics. Many of the best known targets have minimal genetic signal for this reason.

Our current methods are good at discovering individual genes that associate with health, but discovering combinations of genes is nascent at best. Human genetics cannot help us discover the combinatorial medicines or gene circuits to install in a cell therapy…

…Even with the best possible experimental methods, some of the most promising target biologies will never be searched exhaustively. There are a nearly infinite number of combinatorial genetic interventions we might drug, synthetic circuits we might engineer into cells, and changes in tissue composition we might engender.

Artificial intelligence models can learn general models from the data generated in functional genomics experiments of many flavors, predicting outcomes for the experiments we haven’t yet run. If we manage to construct a performant model for a given class of target biologies, we may be able to increase the efficiency of target discovery by many orders-of-magnitude. The cost of discovering a target could conceivably go from >$1B to <$1M.

There’s growing interest in the idea of combining these technologies to build “virtual cells,” models that can predict the outcomes of target discovery experiments in silico before they’re ever executed in the lab. The grand version of this vision spans all possible target biologies, from gene inhibitions to polypharmaceutical small molecule treatments. In the maximal form, it may take many years to realize.

More limited realizations though are tractable today. The initial versions of these models are already emerging within early Predictive Biology companies. As a few examples, Recursion is building models of genetic perturbations in cancer cells, Tahoe Tx is building models in oncology with a chemical biology approach, and NewLimit has developed models for reprogramming cell age across human cell types13. Focused models like these represent an early demonstration that this general approach can yield therapeutic value…

…We are entering an epoch of abundant intelligence. With these tools, we have the opportunity to discover & design target biologies at a rate that’s too cheap to meter. The therapies that emerge could serve as the counterexample that downgrades Eroom’s law to a historic conjecture.

3. What I learned watching 78 videos from Tesla’s Austin robotaxis – Timothy B. Lee

I’ve watched 78 videos posted by pro-Tesla influencers who got early access to the service. Those videos documented more than 16 hours of driving time across nearly 100 rides.

These videos exceeded my expectations. Tesla’s robotaxi rollout wasn’t perfect, but it went as well as anyone could have expected. A handful of minor glitches got outsized attention online, but a large majority of trips were completed without incident…

…Tesla’s robotaxis drove flawlessly during the vast majority of the 16 hours of driving footage I watched. They stayed in their lane, followed traffic laws, and interacted smoothly with other vehicles…

…Tesla’s most widely discussed error occurred around seven minutes into this video. The robotaxi approached an intersection and got into the left turn lane. But the robotaxi couldn’t make up its mind whether it wanted to turn left or go straight. The car’s steering wheel jerked back and forth several times. On the car’s display, the blue ribbon showing the car’s intended path jumped back and forth erratically between turning left or continuing straight. Finally, the Tesla decided to proceed straight but ended up driving the wrong way in the opposite left turn lane…

…But in a piece last year, I argued that they were misunderstanding the situation.

“Tesla hasn’t started driverless testing because its software isn’t ready,” I wrote. “For now, geographic restrictions and remote assistance aren’t needed because there’s always a human being behind the wheel. But I predict that when Tesla begins its driverless transition, it will realize that safety requires a Waymo-style incremental rollout.”

That’s exactly what’s happened:

• Just as Waymo launched its fully driverless service in 50 square miles near Phoenix in 2020, so Tesla launched its robotaxi service in about 30 square miles of Austin last month.
• Across 16 hours of driving, I never saw Tesla’s robotaxi drive on a freeway or go faster than 43 miles per hour. Waymo’s maximum speed is currently 50 miles per hour.
• Tesla has built a teleoperation capability for its robotaxis. One job posting last year advertised for an engineer to develop this capability. It stated that “our remote operators are transported into the device’s world using a state-of-the-art VR rig that allows them to remotely perform complex and intricate tasks.”

The launch of Tesla’s robotaxi service in Austin is a major step toward full autonomy. But the Austin launch also makes it clear that Tesla hasn’t discovered an alternative path for testing and deploying driverless vehicles. Instead, Tesla is following the same basic deployment strategy Waymo pioneered five to seven years ago.

Of course, this does not necessarily mean that Tesla will scale up its service as slowly as Waymo has. It took almost five years for Waymo to expand from its first commercial service (Phoenix in 2018) to its second (San Francisco in 2023). The best informed Tesla bulls acknowledge that Waymo is currently in the lead but believe Tesla is positioned to expand much faster than Waymo did…

…Last month, Waymo published a study demonstrating that self-driving software benefits from the same kind of “scaling laws” that have driven progress in large language models.

“Model performance improves as a power-law function of the total compute budget,” the Waymo researchers wrote. “As the training compute budget grows, optimal scaling requires increasing the model size 1.5x as fast as the dataset size.”

When Waymo published this study, Tesla fans immediately seized on it as a vindication of Tesla’s strategy. Waymo trained its experimental models using 500,000 miles of driving data harvested from Waymo safety drivers driving Waymo vehicles. That’s a lot of data by most standards, but it’s far less than the data Tesla could potentially harvest from its fleet of customer-owned vehicles…

…I posed this question to Dragomir Anguelov, the head of Waymo’s AI foundations team and a co-author of Waymo’s new scaling paper. He argued that the paper’s implications are more complicated than Tesla fans think.

“We are not driving a data center on wheels and you don’t have all the time in the world to think,” Anguelov told me in a Monday interview. “Under these fairly important constraints, how much you can scale and what are the optimal ways of scaling is limited.”

Anguelov also pointed to an issue that will be familiar to anyone who read last month’s explainer on reinforcement learning.

Waymo’s scaling paper—like OpenAI’s famous 2020 scaling law paper—focused on models trained with imitation learning…

…Anguelov was a co-author of a 2022 Waymo paper finding that self-driving models trained with a combination of imitation and reinforcement learning tend to perform better than models trained only with imitation learning.

Imitation learning is “not the most sophisticated thing you can do,” Anguelov told me. “Imitation learning has a lot of limitations.”

This is significant because demonstration data from human drivers—the kind of data Tesla has in abundance—isn’t very helpful for reinforcement learning. Reinforcement learning works by having a model try to solve a task and then judging whether it succeeded. For self-driving, this can mean having a model “drive” in simulation and then judging whether it caused a collision or other problems. Or it can mean running the software on real cars and having a safety driver intervene if the model makes a mistake. In either case, it’s not obvious that having vast amounts of human driving data is especially helpful.

One finding from that 2022 paper is particularly relevant for thinking about the performance of Tesla’s robotaxis. The Waymo researchers noted that models trained only with imitation learning tend to drive well in common situations but make mistakes in “more unusual or dangerous situations that occur only rarely in the data.”

In other words, if you rely too much on imitation learning, you can end up with a model that drives like an expert human most of the time but occasionally makes catastrophic mistakes…

…Since its 2018 launch, Waymo has acknowledged that it has remote operators who sometimes provide real-time assistance to its vehicles. But Waymo has also said that these remote operators never drive the vehicles in real time. Instead, they provide high-level feedback, while the vehicle always remains in control of second-by-second decisions.

In contrast, Tesla’s job posting stated that teleoperators can be “transported into the device’s world” so that they can “remotely perform complex and intricate tasks.” Could those “complex and intricate tasks” include driving the car for seconds or even minutes at a time?

In the videos I watched, a number of Tesla’s early customers commented on how human-like Tesla’s driving was. That might just be a tribute to the quality of Tesla’s AI model. But it’s also possible that sometimes a human driver is literally driving the vehicle from a remote location.

4. No Bad Risks, Only Bad Rates — And Other Lessons From National Indemnity Founder Jack Ringwalt – Kingswell

There are no bad risks in insurance — only bad rates

This maxim was Ringwalt’s north star, the iron-clad principle that allowed him to fearlessly pursue unusual and unwanted risks without driving himself right out of business. Almost anything can be intelligently insured, so long as you charge enough for the coverage.

(It’s also reminiscent of one of my favorite Warren Buffett lines. “I can go into an emergency ward and write life insurance,” he said in 1990, “if you let me charge enough of a premium.”)

When evaluating potential opportunities, Ringwalt’s open mind welcomed the weird and the wild — and he wrote many policies on offbeat ventures that others wouldn’t touch with a ten-foot pole. But, when it came to pricing, that flexibility vanished. If the market would not meet his rate, Ringwalt never blinked. He just waved goodbye to the deal with an indifferent shrug.

“When business is unprofitable to the companies in general,” wrote Ringwalt, “our premium volume has taken a very sharp spurt and when business has been profitable for most companies, we have run into very unintelligent competition and have had to cut down temporarily on our writings.”

The insurance merry-go-round is always the same: profitability lures rivals who slash rates to grab market share, only to crater when losses inevitably pile up. And when the industry bleeds, fly-by-night competitors vanish, prices climb back to normal, and the cycle starts spinning anew. “This pattern will keep repeating,” he wrote. “It makes no sense, but it’s human nature.”

Ringwalt steadfastly refused to play that sucker’s game — a tradition that continued under Berkshire’s aegis. From 1986 to 1999, National Indemnity’s revenue nosedived 85% as profitable premiums evaporated. But, rather than succumb to the pressure to write more business at any price, Buffett and co. urged employees to wait patiently for the right pitch (so to speak). Some things never change.

5. Why I don’t think AGI is right around the corner – Dwarkesh Patel

Sometimes people say that even if all AI progress totally stopped, the systems of today would still be far more economically transformative than the internet. I disagree. I think the LLMs of today are magical. But the reason that the Fortune 500 aren’t using them to transform their workflows isn’t because the management is too stodgy. Rather, I think it’s genuinely hard to get normal humanlike labor out of LLMs. And this has to do with some fundamental capabilities these models lack…

…But the fundamental problem is that LLMs don’t get better over time the way a human would. The lack of continual learning is a huge huge problem. The LLM baseline at many tasks might be higher than an average human’s. But there’s no way to give a model high level feedback. You’re stuck with the abilities you get out of the box. You can keep messing around with the system prompt. In practice this just doesn’t produce anything even close to the kind of learning and improvement that human employees experience.

The reason humans are so useful is not mainly their raw intelligence. It’s their ability to build up context, interrogate their own failures, and pick up small improvements and efficiencies as they practice a task.

How do you teach a kid to play a saxophone? You have her try to blow into one, listen to how it sounds, and adjust. Now imagine teaching saxophone this way instead: A student takes one attempt. The moment they make a mistake, you send them away and write detailed instructions about what went wrong. The next student reads your notes and tries to play Charlie Parker cold. When they fail, you refine the instructions for the next student.

This just wouldn’t work. No matter how well honed your prompt is, no kid is just going to learn how to play saxophone from just reading your instructions. But this is the only modality we as users have to ‘teach’ LLMs anything…

…When we do solve continuous learning, we’ll see a huge discontinuity in the value of the models. Even if there isn’t a software only singularity (with models rapidly building smarter and smarter successor systems), we might still see something that looks like a broadly deployed intelligence explosion. AIs will be getting broadly deployed through the economy, doing different jobs and learning while doing them in the way humans can. But unlike humans, these models can amalgamate their learnings across all their copies. So one AI is basically learning how to do every single job in the world. An AI that is capable of online learning might functionally become a superintelligence quite rapidly without any further algorithmic progrss…

…But here are the timelines where I’d take a 50/50 bet:

• AI can do taxes end-to-end for my small business as well as a competent general manager could in a week: including chasing down all the receipts on different websites, finding all the missing pieces, emailing back and forth with anyone we need to hassle for invoices, filling out the form, and sending it to the IRS: 2028
  I think we’re in the GPT 2 era for computer use. But we have no pretraining corpus, and the models are optimizing for a much sparser reward over a much longer time horizon using action primitives they’re unfamiliar with. That being said, the base model is decently smart and might have a good prior over computer use tasks, plus there’s a lot more compute and AI researchers in the world, so it might even out. Preparing taxes for a small business feels like for computer use what GPT 4 was for language. It took 4 years to get from GPT 2 to GPT 4. Just to clarify, I am not saying that we won’t have really cool computer use demos in 2026 and 2027 (GPT-3 was super cool, but not that practically useful). I’m saying that these models won’t be capable of end-to-end handling a week long and quite involved project which involves computer use.
• AI learns on the job as easily, organically, seamlessly, and quickly as a human, for any white collar work. For example, if I hire an AI video editor, after six months, it has as much actionable, deep understanding of my preferences, our channel, what works for the audience, etc as a human would: 2032
  While I don’t see an obvious way to slot in continuous online learning into current models, 7 years is a long time! GPT 1 had just come out this time 7 years ago. It doesn’t seem implausible to me that over the next 7 years, we’ll find some way for models to learn on the job.

You might react, “Wait you made this huge fuss about continual learning being such a handicap. But then your timeline is that we’re 7 years away from what would at minimum be a broadly deployed intelligence explosion.” And yeah, you’re right. I’m forecasting a pretty wild world within a relatively short amount of time.

AGI timelines are very lognormal. It’s either this decade or bust. (Not really bust, more like lower marginal probability per year – but that’s less catchy).AI progress over the last decade has been driven by scaling training compute of frontier systems (over 4x a year). This cannot continue beyond this decade, whether you look at chips, power, even fraction of raw GDP used on training. After 2030, AI progress has to mostly come from algorithmic progress. But even there the low hanging fruit will be plucked (at least under the deep learning paradigm). So the yearly probability of AGI craters.

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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 (the company behind Waymo), and Tesla. 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 06 July 2025:

1. Etched is Making the Biggest Bet in AI – Etched team

We’ve spent the past two years building Sohu, the world’s first specialized chip (ASIC) for transformers (the “T” in ChatGPT).

By burning the transformer architecture into our chip, we can’t run most traditional AI models: the DLRMs powering Instagram ads, protein-folding models like AlphaFold 2, or older image models like Stable Diffusion 2. We can’t run CNNs, RNNs, or LSTMs either.

But for transformers, Sohu is the fastest chip of all time. It’s not even close.

With over 500,000 tokens per second in Llama 70B throughput, Sohu lets you build products impossible on GPUs. Sohu is an order of magnitude faster and cheaper than even NVIDIA’s next-generation Blackwell (B200) GPUs…

…No one has ever built an algorithm-specific AI chip (ASIC). Chip projects cost $50-100M and take years to bring to production. When we started, there was no market.

Suddenly, that’s changed:

• Unprecedented Demand: Before ChatGPT, the market for transformer inference was ~$50M, and now it’s billions. All big tech companies use transformer models (OpenAI, Google, Amazon, Microsoft, Facebook, etc.).
• Convergence on Architecture: AI models used to change a lot. But since GPT-2, state-of-the-art model architectures have remained nearly identical! OpenAI’s GPT-family, Google’s PaLM, Facebook’s LLaMa, and even Tesla FSD are all transformers.

When models cost $1B+ to train and $10B+ for inference, specialized chips are inevitable. At this scale, a 1% improvement would justify a $50-100M custom chip project.

In reality, ASICs are orders of magnitude faster than GPUs. When bitcoin miners hit the market in 2014, it became cheaper to throw out GPUs than to use them to mine bitcoin…

…We believe in the hardware lottery: the models that win are the ones that can run the fastest and cheapest on hardware. Transformers are powerful, useful, and profitable enough to dominate every major AI compute market before alternatives are ready:

• Transformers power every large AI product: from agents to search to chat. AI labs have spent hundreds of millions of dollars in R&D to optimize GPUs for transformers. The current and next-generation state-of-the-art models are transformers.
• As models scale from $1B to $10B to $100B training runs in the next few years, the risk of testing new architectures skyrockets. Instead of re-testing scaling laws and performance, time is better spent building features on top of transformers, such as multi-token prediction.
• Today’s software stack is optimized for transformers. Every popular library (TensorRT-LLM, vLLM, Huggingface TGI, etc.) has special kernels for running transformer models on GPUs. Many features built on top of transformers aren’t easily supported in alternatives (ex. speculative decoding, tree search).
• Tomorrow’s hardware stack will be optimized for transformers. NVIDIA’s GB200s have special support for transformers (TransformerEngine). ASICs like Sohu entering the market mark the point of no return. Transformer killers will need to run on GPUs faster than transformers run on Sohu. If that happens, we’ll build an ASIC for that too!…

…Isn’t inference bottlenecked on memory bandwidth, not compute?

Actually, for modern models like Llama-3, no!

Let’s use NVIDIA and AMD’s standard benchmark13: 2048 input tokens and 128 output tokens. Most AI products have much longer prompts than completions (even a new Claude chat has 1,000+ tokens in the system prompt).

On GPUs and on Sohu, inference is run in batches. Each batch loads all of the model weights once, and re-uses them across every token in the batch. Generally, LLM inputs are compute-bound, and LLM outputs are memory-bound. When we combine input and output tokens with continuous batching, the workload becomes very compute bound…

…We can scale up the same trick to run Llama-3-70B with 2048 input tokens and 128 output tokens. Have each batch consist of 2048 input tokens for one sequence, and 127 output tokens for 127 different sequences.

If we do this, each batch will require about (2048 + 127) × 70B params × 2 bytes per param = 304 TFLOPs, while only needing to load 70B params × 2 bytes per param = 140 GB of model weights and about 127 × 64 × 8 × 128 × (2048 + 127) × 2 × 2 = 72GB of KV cache weights. That’s far more compute than memory bandwidth: an H200 would need 6.8 PFLOPS of compute in order to max out its memory bandwidth. And that’s at 100% utilization – if utilization was 30%, you’d need 3x more.

Since Sohu has so much compute with very high utilization, we can run enormous throughputs without bottlenecking on memory bandwidth…

…On GPUs and TPUs, software is a nightmare. Handling arbitrary CUDA and PyTorch code requires an incredibly complicated compiler. Third-party AI chips (AMD, Intel, AWS, etc.) have together spent billions on software to little avail.

But since Sohu only runs transformers, we only need to write software for transformers!

Most companies running open-source or internal models use a transformer-specific inference library like TensorRT-LLM, vLLM, or HuggingFace’s TGI. These frameworks are very rigid – while you can tweak model hyperparameters, changing the underlying model code is not really supported. But this is fine – since all transformer models are so similar (even text/image/video ones), tweaking the hyperparameters is all you really need.

2. Lots More on What’s Going On in Iran’s Markets (Transcript Here) – Tracy Alloway, Joe Weisenthal, and Maciej Wojtal

Maciej: If I can just comment on one thing, because the way you introduced Iran is the perfect way to show the country. It’s the size of Turkey in terms of population and actually geographical size as well. But if you compare the economy of Turkey and Iran, it’s around five times smaller. So Iran is around five times smaller and if you look at the composition of the economy, Turkey has no natural resources, so they have to import the whole energy commodities they consume. So Iran has a similar size of potential non-commodity GDP that it could grow to, from the current let’s say $250 billion to $1.1 trillion GDP that Turkey has. But also on top of this, has resources that are actually – if you combine gas and oil, they are bigger than Saudi Arabia’s and Saudi Arabia is another one, I think $1.3 trillion economy. This is a good way to just frame Iran as to show Iran, as it’s a big country that should really be having much bigger economy. Because of sanctions, various reasons and so on, it’s been underdeveloped. But the scale of this underdevelopment is like 10x.

Tracy: And because of the sanctions we can’t actually go and look up what’s happening in Tehran’s stock market. So why don’t you give us an overview of what it’s been like for the past week given geopolitical events?

Maciej: So for the past week it was difficult for everyone to check what was going on in Iran because internet was shut down basically. I could communicate with my team on the ground in Tehran once a day when they had signal and sometimes it was WhatsApp that was working, sometimes Telegram. But it was maybe once or twice per day. So what was going on in the market was simply nothing. The stock market hasn’t opened. The exchange of fire between Iran and Israel happened on a Friday, which is weekend in Iran, and then on the following Saturday there was an important religious holiday, so the market and actually the whole economy was supposed to be closed anyway. The economic activity, the market, was supposed to resume on a Sunday but they didn’t open. So the stock market, pretty much most of the currency market, has been closed for the last two weeks…

…Maciej: For example right now, when you have the stock market, the currency market were shut down, but you could track what’s going on with the exchange rate of the Iranian rial versus dollar either on Telegram chats but also on cryptocurrency exchanges. You have liquid market on stable coins versus Iranian rial inside of Iran where liquidity was limited during the last period anyway, but we could see the changes. So we knew that $1 before the war was at around 830,000 rials, then it went up roughly 15% to 950,000 and now after the ceasefire, it’s back down at 850,000. You can track the market, you can actually make transactions depending on the vol, on the liquidity, but it is possible. To be honest, when I saw those exchange rates moves 15% when you have a war where a lot of commentators were saying that this could turn into a massive worldwide conflict, that 15% in a country like Iran I would say that this is your usual volatility on the currency market…

…Maciej: In Tehran, a lot of residents were just relocating out of Tehran. Tehran is a big city, 12 million people, and they were moving mainly north to some smaller cities by the Caspian Sea. You had massive congestion. People were spending hours in traffic jams trying to get out of Tehran. There was not enough petrol on gas stations just because of this peak in demand. You had some petrol rationing.

Then I was asking them is the economy working, not working? Everything that was non-essential basically was closed. So you couldn’t build, buying materials or anything like this. But groceries, pharmaceuticals, gas stations, banks, this was all open and working properly with some disruptions. But those disruptions, for example, if you wanted to buy groceries in north of Iran where everyone has just relocated, you had some logistical bottlenecks. Distribution was not fast enough, so you had some shortages just for a little while. With banks, some branches were not operating at 100% capacity. Two banks got hacked. You had some cyber attacks on two banks in Iran and one cryptocurrency exchange. The rest of the banking sector was working without any disruptions. You could get cash from any ATM. There were no problems like these…

…Maciej: It’s interesting because there is information, very up-to-date detailed information on Iranian stocks available in Iran. But the majority of this information is not accessible if you’re trying to access it from a computer with your IP address outside of Iran. A lot of this information is restricted to Iran IP only. You cannot find anywhere on the whole internet. There is no website that shows the stock market index in dollars. When we send it out to our investors or just people who want to read news about the stock market in Iran, we are the only source of this information. This is quite amazing. It’s a country of 90 million people and stock market with 700 companies and there is no single place in internet that would show you the only important index…

…Maciej: In terms of oil, it is not really publicly traded. There is one Iranian monopoly called National Iranian Oil Corporation or company that is responsible for production. I think this is all centralized in one company and this is held by the government so it’s not publicly listed. You have some exposure to oil through oil refineries that are listed but refineries, they’re not sensitive to the price of oil. They are sensitive to the crack spread, which defines their refining margin, so they are not really a proxy to oil prices.

The whole stock market actually is well diversified. You have  large sectors such as chemicals – mainly these are like petrochemicals – companies that produce different products, different versions, use natural gas that is in in large supply as a cheap commodity and produce fertilizers or products like this. This is probably 20% of the stock market.

Then you have steel companies. The largest steel company in the Middle East is in Iran. You have car makers that produce more than 1 million cars a year. With car manufacturers, you have all the related industries, suppliers, to the car manufacturing businesses. You have banks – financials is an important sector – plus some consumer exposure, some building materials, cement companies are one of the best performers over the last few years actually…

…Maciej: I’ll get back to the potential for GDP in a moment. But the catalyst is absolutely clear. It must be the opening up of Iran as a country, and opening up of the economy, andthe US sanctions lifted. There must be an agreement between the US and Iran. What needs to happen? Some sort of political change. Political attitude must change on both sides. But to be honest, many analysts were expecting some big dramatic event that needs to happen in Iran for the country to properly open up.

When you look at Iran right now and you compare to let’s say even a few years ago when you had negotiations with the US, what were the biggest problem was, it was always about two things: (1) Iran enriching uranium too much basically, at a wrong level, and (2) Iranian regional policies, so financing proxies from Hezbollah to Hamas, Assad in Syria and so on. These two things were always the problem that they couldn’t negotiate over. When you look at it right now, to a large extent both obstacles are gone…

…Joe: Are there tech companies that trade on the Tehran stock market?

Maciej: There are tech companies. The ones that are listed are related to enterprise software, the Oracle or SAP, German SAP. But you have privately held companies that would like to IPO but they are just waiting for the approval from the regulator, and these are quite amazing companies. You have Snapp, which is like an Uber, but Snapp has more rides in Tehran than Uber in any city in the world. It’s a really world-class company. You have Digikala, which is like Amazon basically, also a large company, one of the biggest success stories.

3. Stablecoins might revolutionise payments, but what if they don’t? – Bryce Elder

That leaves payments:

While in a theoretical tokenized/blockchain based world, stablecoin-based payments would be faster, more efficient and interoperable, in practice at the moment these stablecoin based payments mostly start and finish with fiat, thus requiring on/off-ramps. This on/off ramp requirement adds significant friction/cost to the use of stablecoins for payments, making it less attractive compared to traditional financial systems, in particular if one takes into account the emergence of faster payment rails in the traditional financial system via fintech advancements in recent years. As a result, we find rather unrealistic the expectation of a massive increase in the use of stablecoins in payments. Indeed, our colleagues in US short-term rates research also note that market participants at the front end are skeptical of significant growth in the near term, in part due to the fact that the infrastructure/ecosystem for stablecoins remains underdeveloped. But even if one adopts an optimistic view and assumes, for example, a tenfold increase in the use of stablecoins in payments over the next couple of years, the stablecoin universe would only expand by $15bn x 10 = $150bn.

Stablecoin optimists point to the rapid adoption of the e-CNY, China’s central bank digital yuan, which has grown to a more than Rmb300bn market cap from Rmb13.6bn at the end of 2022. There’s no comparison, JPMorgan says:

First, the digital yuan is a central bank liability and thus it effectively replaces banknotes in circulation. While there does not appear to be a published target share of M0, there have been suggestions that a 10-15% share of M0 is a plausible medium-term goal, which would imply around RMB 1.3-2tr using current M0 levels. By contrast, stablecoins are a form of a tokenized MMF with zero interest, effectively a private sector liability rather than a central bank liability.

Second, the digital yuan does not operate through a fully decentralized blockchain-based ledger. Instead, it operates via a centralized network supervised by the PBoC and competes with other mobile/ electronic payment options in China such as Alipay and WeChat Pay.

Then is it better to think of stablecoins as global equivalents to Alipay and WeChat Pay? JPMorgan says no. Fintech payment companies offering collateralised electronic private money on their own platforms hasn’t proven the need for public blockchains; if anything, it proves the opposite:

Alipay/WeChat Pay digital money are private liabilities and are perhaps more similar to bank deposits in that regard which are also private liabilities. The difference between bank deposits and Alipay/WeChat balances is that the latter are backed by reserve funds that in turn hold public liabilities i.e. central bank reserves, while bank deposits are matched on the asset side by a mix of loans and debt securities, though they do have an additional guarantee via deposit protection arrangements.

In our mind, the strong expansion of Alipay and WeChat Pay should be viewed through the lens of a fintech payments revolution over the past decade in China that utilizes and increases the efficiency of traditional banking/financial system networks, rather than through the lens of a blockchain/crypto ecosystem revolution. In fact, it could be argued that the success and continued advancements in payments by fintechs, such as Alipay and WeChat Pay reduce the need for blockchain-based payment systems in the future.

4. Meet Project Rainier, Amazon’s one-of-a-kind machine ushering in the next generation of AI – Kirsteen Rodger

Project Rainier is designed as a massive “EC2 UltraCluster of Trainium2 UltraServers.” The first part refers to Amazon Elastic Compute Cloud (EC2), an AWS service that lets customers rent virtual computers in the cloud rather than buying and maintaining their own physical servers.

The more interesting bit is Trainium2, a custom-designed AWS computer chip built specifically for training AI systems. Unlike the general-purpose chips in your laptop or phone, Trainium2 is specialized for processing the enormous amounts of data required to teach AI models how to complete all manner of different and increasingly complex tasks—fast.

To put the power of Trainium2 in context: a single chip is capable of completing trillions of calculations a second. If, understandably, that’s a little hard to visualize: consider that it would take one person more than 31,700 years to count to one trillion. A task that would require millennia for a human to complete can be done in the blink of an eye with Trainium2…

…Traditionally, servers in a data center operate independently. If and when they need to share information, that data has to travel through external network switches. This introduces latency (i.e, delay), which is not ideal at such large scale.

AWS’s answer to this problem is the UltraServer. A new type of compute solution, an UltraServer combines four physical Trainium2 servers, each with 16 Trainium2 chips. They communicate via specialized high-speed connections called “NeuronLinks.” Identifiable by their distinctive blue cables, NeuronLinks are like dedicated express lanes, allowing data to move much faster within the system and significantly accelerating complex calculations across all 64 chips.

When you connect tens of thousands of these UltraServers and point them all at the same problem, you get Project Rainier—a mega “UltraCluster.”…

…Communication between components happens at two critical levels: the NeuronLinks provide high-bandwidth connections within UltraServers, while Elastic Fabric Adapter (EFA) networking technology (identified by its yellow cables) connects UltraServers inside and across data centers. This two-tier approach maximizes speed where it’s most needed while maintaining the flexibility to scale across multiple data center buildings.

5. OpenAI has started to form a “moat” – Rihard Jarc

I think anyone who follows the AI space knows about OpenAI and, more specifically, about ChatGPT. Even outside of investors and tech enthusiasts, the verb ChatGPT has gone viral, similar to how the verb Google started. What is even more surprising is that despite ChatGPT being out there for more than 2 years already, just recently, at the end of March, it came to another acceleration point in terms of adoption when the Ghibli photo trend emerged on ChatGPT:

The number of MAUs doubled from 400 million to 800 million in a matter of a few weeks. Looking at the adoption curves of other highly adopted technology platforms, such as TikTok, Facebook, Instagram; ChatGPT, is on a slope of its own.

Another factor to consider is that it is not just a “I must try it moment”. Looking at the number of minutes a user spends on ChatGPT, the minutes are constantly growing and have now reached the 29-minute daily mark.

Remember that at the start of ChatGPT and LLMs, many critics said that people tried it, had fun, and then didn’t use it again. This trend shows that that is not the case and that with each enhanced model version and UX improvement, the stickiness factor becomes bigger…

…OpenAI also now has serious hardware ambitions. In late May of this year, they acquired Jony Ive’s startup, a famous former Apple designer, for nearly $6.5 billion, who will now lead OpenAI’s hardware efforts. What is now almost a consensus opinion among big tech leaders is that AI will unlock the next computing platform, one that is not tied to the smartphone.

And if you listen to those conversations, everyone is calling for a similar device. A device that will be more like a companion system and will be less dependent on a screen. Proactive assistant who will run even when you don’t ask it.

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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 (the company behind AlphaFold), Amazon (the company behind AWS), Apple, Meta Platforms (the company behind Facebook and Instagram), and Tencent (the company behind WeChat Pay). 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 June 2025:

1. China’s rare earth choke hold – Amber Zhang

Rare earths comprise a group of 17 elements, typically categorized into light, medium, and heavy groups. These materials are indispensable for making high-performance magnets used in both civilian and military technologies. Among them, medium and heavy rare earths — critical for aerospace, defense, and other cutting-edge sectors — are particularly scarce and difficult to source.

Don’t be fooled by their size. Rare earth magnets are no larger than a stick of chewing gum, yet pack magnetic force 15 times stronger than traditional iron magnets. Heat-resistant and cost-efficient, they are essential components in electric motors — not only in EVs and hybrid vehicles, but also in robots, drones, offshore wind turbines, missiles, and fighter jets…

…According to the International Energy Agency, China accounted for over 60% of global rare earth mining output in 2023 — and an even more dominant 92% of the world’s refining capacity. According to the International Energy Agency, China accounted for over 60% of global rare earth mining output in 2023 — and controlled a staggering 92% of global refining capacity…

…Between 2020 and 2023, 70% of the rare earth compounds and metals used in the U.S. were imported from China, according to the U.S. Geological Survey…

…Ford recently halted production for a week at its Chicago plant due to rare earth shortages, affecting its Explorer SUV line. In early June, the Motor & Equipment Manufacturers Association (MEMA), along with General Motors, Toyota, Volkswagen, Hyundai, and other major automakers, issued a joint letter warning that without a stable supply of rare earth magnets, production of essential components could come to a standstill…

…The U.S. once boasted the world’s largest rare earth magnet industry. Its Mountain Pass mine in California had supplied most of the global market since 1965. But in 1998, the mine was shut down following a pipeline leak that released trace heavy metals and radioactive materials into the Mojave Desert. Chinese firms made three separate attempts to acquire the mine — all blocked by U.S. authorities.

Alarmed by Japan’s supply crisis, the Obama administration supported Hitachi Metals’ investment in a rare earth magnet plant in North Carolina, operational from 2011 to 2013. But the costs were prohibitively high compared to China’s vertically integrated, state-backed operations in cities like Ganzhou. U.S. buyers, ultimately unwilling to pay a “made-in-America premium,” continued sourcing from Chinese suppliers. In 2020, Hitachi shut down the facility and mothballed its equipment…

…Back in 2010, Mountain Pass — the U.S.’s only remaining rare earth mine — received over $1 billion in Pentagon funding just to stay afloat. But lacking commercial competitiveness, it shut down again the following year. In 2017, MP Materials acquired the site, restarted mining operations, and began exporting raw ore to China for processing. The company now plans to begin producing rare earth magnets at a new facility in Texas by the end of this year. Still, even at full capacity, its annual output would match just a single day of production in China…

…Domestically, the Round Top project in Texas has emerged as a cornerstone of America’s rare earth strategy. Operated by U.S. Rare Earths Inc., the site holds estimated reserves of 130,000 metric tons across 16 different elements and aims to supply 20% of U.S. rare earth demand by 2027. The company is also building a $100 million magnet manufacturing facility in Oklahoma, which is expected to process up to 2,000 metric tons of rare earth materials annually.

Meanwhile, the U.S. Department of Energy has launched the ReElement initiative, allocating $50 million to recover up to 90% of rare earth elements from electric vehicle batteries by 2025. But these recycling systems have yet to achieve commercial scale and remain economically marginal.

The National Defense Authorization Act for fiscal year 2025 earmarks $1.2 billion for strategic stockpiling and $350 million for domestic development. These funds are being channeled into American firms like MP Materials, aimed at accelerating the construction of a domestic rare earth processing infrastructure…

…According to the Center for Strategic and International Studies (CSIS), the Pentagon has invested over $439 million since 2020 to develop a rare earth industrial base — but most U.S. production remains in its early stages.

RAND Corporation estimates that it would take at least 10 years and $10–15 billion in investment to establish a fully independent domestic rare earth supply chain, factoring in infrastructure, permitting, environmental compliance, and workforce training.

2. The Great Decoupling (or Why Your Clicks Are Down and Impressions Up) – Ryan Law

Impressions are increasing because AI Overviews now give companies two chances to log an impression for a given keyword: once as a “traditional” blue link in the search results, and again as a citation in an AI Overview…

…At the same time, clicks are decreasing because AI Overviews are increasing zero-click searches. Searchers can get all the information they need to resolve their query without leaving the search results page.

When we studied this at scale across 300,000 keywords, we found that the presence of an AI Overview correlates with a 34.5% reduction in clickthrough rate…

…While our clicks are tanking because of AI search, recent data from Patrick Stox shows that—at least on the Ahrefs website—visits from AI search convert 23x better than visits from traditional search.

The way content marketing functions is very different, but guess what? There are more potential customers in the world, more demand for products and services. That is the real determinant of growth, not clicks to a blog. We’ll find different ways to reach those people.

3. A Cheeky Pint with Meta CFO Susan Li (Transcript Here) – John Collison and Susan Li

Susan: When I think about it, I go back to when I was IC4 and I joined in 2008, I’m building these first revenue models. I’d gone from banking – which is super organized, super structured, they don’t even need to know your name, they just train you to immediately figure out how to find the backup to everything, so that two years later someone else can do this and so on and so forth – to there was no infrastructure. So I’m hunting down the exact engineer who has built some ad server so that he can tell me what the parameters mean. And of course, the next time he changes them, he’s not gonna tell me, and I have to go find him again, and he’s like, “Oh, she’s coming. Don’t look her way.” A few months in, I got a meeting invite for power users of SQL and I thought, “My gosh. I’d been getting a good amount of feedback about how things could be better, and here was finally this moment of recognition that – I didn’t even know how to write queries in SQL when I started.” I show up to this meeting and there are five other people and the meeting organizer tells us that we have been called because we are the five users of SQL who consume too much power. And we have just been churning with our massive joint tables through the…

John: I love that you were all called to the principal’s office.

Susan: Basically, yes. But I often think back to this because this was a data analyst who didn’t know any of us that well, but had just generated his reports of who’s using the most infrastructure and looked at the top people on the list and thought, “Okay, this person in finance, it doesn’t make sense why she’s the third highest person on the list,” and called us in and then taught us to write better queries. No one I think specifically told him to do that. I think it’s a little awkward when you call people in to do this, but he did it because it would make us all better at our jobs…

…Susan: So, there’s this very measurable part of the company and we generally try to trade those things off against each other when we’re evaluating things within that bucket and we generally try to fund the things that are positive ROI. I’m usually the person who’s trying to make sure we understand, for every individual experiment, the expected return is something, but that’s where we are on the curve today, but what about 50 experiments later? Does the curve still have the same slope?

Then there’s a set of things which we constrain more in terms of, there’s some envelope of investment that we’re willing to make that’s not in this really ROI-driven bucket. It is very difficult to pencil out what the annual revenue forecast for Reality Labs is gonna look like over the next 20 years. For bets like that, we invert the problem. But when we talk about the return on the investment, the question that we pose, as a finance organization, to Mark – and make sure that Mark and the board understand – is what does this have to be worth to pencil out at the end? Does that pass the sanity check, the intuition, about what the size of these markets can be based on maybe some comparisons to markets that exist today, but of course in another 10, 20 years, you expect that the world will look different and maybe those markets should be bigger or smaller for whatever reason. That’s the guide, which is, for this thing to succeed at the rate at which we’re investing, it needs to be worth this at the end and does that make sense?…

…Susan: I am not a tech visionary. There are many things I’m good at, but envisioning the future of the world and what I want it to be like is not one of them. I’m a very happy beneficiary of the technology built by the world around me.

But Mark very much has a vision for what he wants that world to be. And for him, I think the strategic imperative is that we have to be building these next states of the world for us to again, be a good business, but also just be a compelling company that builds technology and puts it out in the world and builds incredible experiences for people.

I remind people in the finance organization all the time, we are very good at skeptically evaluating each bet. But the point is not that we have to look at every bet and be like, “This bet is going to work.” The point is there is a portfolio of bets, and some of them are going to pay off massively beyond, in fact, what the case on paper looks like when you make the bet. Many of them are going to not work out, but the ones that pay off are gonna more than justify the overall investment strategy or the overall roadmap that you’re building toward. If we just allowed ourselves to nix everything that the paper-case didn’t seem high-confidence, then we would never make a lot of the important bets that have been really important over the history of the company…

…Susan: That is the question that I assume all of my counterparts at these companies and I are all thinking about. For us, there are the drivers of the way we’re investing in capex today. Of course, we have, first of all, just a massively-scaled consumer business and core AI infrastructure that powers all the ranking and recommendations work and so on and so forth. That’s always been a reasonably big number for us, but also because it was getting more mature that we were driving to be more efficient over time. Then now you have, among many of our peers and ourselves, this big investment to train what we all aspire to be, frontier models. If you use those models to build great and scaled consumer experiences, then how much inference compute you’re gonna need on top of that? If compute required continues to scale up in this way forever, then you’re gonna run into some true problems of physics. But hopefully, there will be different kinds of research innovations along the way that will unlock things like being able to distribute the training so you don’t need one extremely large cluster somewhere and that will help with a lot of the energy and other challenges. So there’s some question about what that looks like over time.

Then there’s this question about, “Great, you can build all this capacity, and what do you do with them if it turns out you don’t need as much compute for either training or inference as you thought?” I think a lot of us have different backup use cases. So, up to some point, we would use a lot of compute very happily still, in the core business and what we expect the core business to be, three years from today. But frankly, we’d use more compute in the core business. Now, that doesn’t scale forever. So the real question is what happens in like two years if you’ve built so much compute that you cannot envision a reasonable ROI on the backup use case if what you’re building doesn’t come to fruition. That’s something I think we’re all gonna learn in the next few years…

…Susan:  As part of not wanting to miss the boat, we built out enough capacity for Reels but also for future things. We found that we were in fact able to put that capacity towards very good use – exactly as you said. So I do think an interesting question in the future will be allocating compute as a resource, It’s a muscle we’ve built later as a company, because we had gotten very good at allocating headcount as a resource, and headcount’s really easy to account for because you have org charts, you know exactly this person reports to this person, to this person, this person is incontrovertibly working on Facebook Marketplace, for example. GPUs don’t have that property. In fact, you often want to build out your infrastructure for it to be very fungible. Because you need to divert capacity to where – suddenly something has happened in India and you want a lot of compute to be available to be used there. So it’s not like this GPU is labeled for Facebook Marketplace, and this is labeled for – it’s actually quite a bit more difficult to account for where the capacity is being used at any given point in time. That means it’s harder to manage, and it’s harder to create the incentives around are you using GPUs efficiently?

John: You allow people to trade between people and GPUs, right?

Susan: In the budgeting process, we have allowed people to trade. Not too surprisingly, even though you’ll find that groups are often asking for compute, when that particular trade is on offer, people almost never trade for compute for exactly the reason I described, which is that if they get allocated 100 new headcount, there is no chance that 26 of those headcount will accidentally be working for something else.

4. My Trip to Washington to Get in Sync with Republican and Democratic Leaders on the Budget and Debt Situation – Ray Dalio

Everyone I spoke with on both sides agreed that:

• We are likely to have a big debt-economic crisis if we don’t get the budget deficit down to 3 percent of GDP, so 3 percent should be an agreed-on goal,
• Getting the deficit to 3 percent will require both spending cuts and tax revenue increases because if they come from just spending cuts or just tax revenue increases alone, the cuts or increases would be too big and shocking.
• It’s not possible for politicians to say these things publicly even though they believe them because they would be thrown out of office…

…So, our biggest problem is that our country’s political representatives can’t even say, let alone do, what they need to do to fix our debt issues because their constituents would throw them out of office if they did that. Such is the condition of our political decision-making system.

We discussed my idea of a “3 percent 3-part solution,” which would be to cut the budget deficit to 3 percent of GDP through a mix of spending cuts, tax revenue increases, and interest rate cuts. For example, cutting spending by 4 percent, increasing tax revenue by 4 percent, and lowering the real interest rate by 1%** so that the adjustments wouldn’t be unbearably large to achieve that 3% deficit goal. The leaders I spoke with said that they’d love to do this or something like it — in fact, they thought it would be wonderful if the “meme” of reducing the deficit in this way took hold in the electorate and there was public pressure to get it done.

As for where things are likely to go, there won’t be big enough changes to the current proposed budget to change the overall picture this tax year.

5. The Speed of Patience – Paul Higgins

To understand how patient preparation creates decisive speed, I’ll show you three different maps of the same territory I’ve found practical.

1. Pace layers reveal where to be patient and where to be urgent, showing how businesses operate across multiple timescales simultaneously, from seasonal fashion to generational culture.
2. S-curves illuminate when those layers will hit their inflection points, helping you recognize which growth curve you’re actually betting on.
3. Trust as a leading indicator – what emerges from ongoing interactions across and between layers (employees, communities, customers and processes), the invisible asset that compounds for decades…

…I like Stewart Brand’s pace layering framework for understanding how businesses operate across time. It reveals why this matters so profoundly. In most complex systems, different elements change at different speeds. Fashion moves seasonally, commerce shifts yearly, infrastructure evolves over decades, governance changes generationally, and culture moves so slowly it appears frozen in time…

…Layers don’t exist separately, they form a single, interconnected living system which is sometimes hard to see. We tend to see layers as independent parts to optimize separately, but in living systems, layers are how the whole organism breathes – each rhythm nested within another, each movement part of a larger dance. The fast movements at the surface and slow currents in the depths aren’t separate phenomena but the system’s way of being alive at every scale simultaneously. Speed doesn’t come from stability – they arise together from the coherence of the whole system…

…Apple master this temporal arbitrage. New iPhone colors arrive every season to satisfy the fashion layer, while annual product cycles drive the commerce layer with reliable predictability. But the iOS ecosystem, which represents their true competitive moat, took twenty years to build in the infrastructure layer, creating switching costs and network effects that compound with each passing year. Their App Store governance evolves with glacial deliberation, each change carefully considered for its long-term implications, while their design philosophy – the cultural layer that infuses everything they create – hasn’t fundamentally changed since Jobs articulated it decades ago. You just have to look at their cumulative cash reserves to see whether they have the capacity to keep it up or not.

Competitors try to destroy Apple’s fashion layer moat and assume that’s the game being played. They miss the insight that Apple’s speed in the fashion layer comes from stability in the infrastructure layer, that the layers aren’t independent but deeply interdependent, with the slow layers enabling the fast ones to move with confidence and clarity…

… In business, you’re never riding just one S-curve. You’re managing a portfolio of them, each operating at different speeds across different layers of your organization. Your product adoption might be hitting exponential growth (measured in months) while your infrastructure build-out is still in early grind (measured in years) and your culture formation hasn’t even begun its curve (measured in decades)…

…Netflix understood this with brutal clarity. In 2010, they were shipping 2 million DVDs daily – a massive operation at the peak of its S-curve. But Reed Hastings saw streaming was at the bottom of its S-curve, barely functional, with terrible selection and constant buffering. While Blockbuster optimized their mature retail model, Netflix deliberately cannibalized their profitable DVD business to ride the next wave. They moved $200 million from DVD operations into streaming content when streaming represented less than 20% of revenue. Today Netflix is worth $240 billion; Blockbuster is a cautionary tale…

…Kerry Group’s transformation from Ireland’s smallest dairy cooperative to a €6.3 billion ingredients empire illustrates how patience creates opportunities invisible to those focused on shorter horizons. Every dairy producer faced the same challenge with whey, the protein-rich liquid left over from cheese-making that represented both a disposal cost and a compliance headache. While the entire industry treated this as expensive waste, Kerry’s leadership recognized something profound: they were looking at two different S-curves operating on completely different timescales.

The dairy business that consumed everyone’s attention was approaching the top of its S-curve, with margins thinning and consolidation inevitable, while the ingredients business hadn’t even begun its exponential climb. For fifteen years, Kerry invested in extraction technology and scientific capabilities while competitors focused on optimizing dairy margins. By the time health consciousness and specialized nutrition exploded into mainstream consciousness, Kerry had spent two decades perfecting protein extraction, understanding molecular structures, and building relationships with food manufacturers who needed exactly these capabilities…

…Warren Buffett’s 2008 moves exemplified how trust operates across all three maps simultaneously. While others mocked Berkshire’s growing cash pile – $40 billion sitting “idle” – he was building in the infrastructure layer (pace layers), preparing for the inevitable down-cycle in financial services’ S-curve, and accumulating trust with every patient year. That cash pile represented more than financial capacity; it was trust crystallized into capital. Every year Buffett didn’t chase returns, every quarter he resisted leverage, every deal he walked away from, he was depositing into an invisible trust account. When 2008 hit, that patient accumulation enabled lightning-fast execution: $8 billion deployed to Goldman Sachs with one phone call. The $7.7 billion total return exceeded Coca-Cola’s entire 20-year dividend stream to Berkshire. Trust had compressed decades into days.

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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 (the company behind AI Overviews), Apple, Meta Platforms, and Netflix. 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 22 June 2025:

1. Message from CEO Andy Jassy: Some thoughts on Generative AI – Andy Jassy

Today, in virtually every corner of the company, we’re using Generative AI to make customers lives better and easier. What started as deep conviction that every customer experience would be reinvented using AI, and that altogether new experiences we’ve only dreamed of would become possible, is rapidly becoming reality. Technologies like Generative AI are rare; they come about once-in-a-lifetime, and completely change what’s possible for customers and businesses…

…You can see it in Advertising where we’ve built a suite of AI tools that make it easier for brands to plan, onboard, create and optimize campaigns. In Q1 alone, over 50K advertisers used these capabilities…

…We’re also using Generative AI broadly across our internal operations. In our fulfillment network, we’re using AI to improve inventory placement, demand forecasting, and the efficiency of our robots—all of which have improved cost to serve and delivery speed. We’ve rebuilt our Customer Service Chatbot with GenAI, providing an even better experience than we’d had before. And, we’re assembling more intelligent and compelling product detail pages from leveraging GenAI…

…First, we have strong conviction that AI agents will change how we all work and live. Think of agents as software systems that use AI to perform tasks on behalf of users or other systems. Agents let you tell them what you want (often in natural language), and do things like scour the web (and various data sources) and summarize results, engage in deep research, write code, find anomalies, highlight interesting insights, translate language and code into other variants, and automate a lot of tasks that consume our time. There will be billions of these agents, across every company and in every imaginable field. There will also be agents that routinely do things for you outside of work, from shopping to travel to daily chores and tasks. Many of these agents have yet to be built, but make no mistake, they’re coming, and coming fast.

Second, and what makes this agentic future so compelling for Amazon, is that these agents are going to change the scope and speed at which we can innovate for customers. Agents will allow us to start almost everything from a more advanced starting point…

…Today, we have over 1,000 Generative AI services and applications in progress or built, but at our scale, that’s a small fraction of what we will ultimately build. We’re going to lean in further in the coming months. We’re going to make it much easier to build agents, and then build (or partner) on several new agents across all of our business units and G&A areas.

As we roll out more Generative AI and agents, it should change the way our work is done. We will need fewer people doing some of the jobs that are being done today, and more people doing other types of jobs. It’s hard to know exactly where this nets out over time, but in the next few years, we expect that this will reduce our total corporate workforce as we get efficiency gains from using AI extensively across the company.

2. Experiencing the Real “Belt and Road” – Nina Chen

In early June, I traveled in Central Asia for 9 days, visiting two countries. I spent 2 days in Almaty, Kazakhstan, and 7 days in Uzbekistan, covering Tashkent, Samarkand, and Bukhara…

…We flew from Almaty, Kazakhstan, to Tashkent, the capital of Uzbekistan. Even before landing, it was clear that Uzbekistan and China have a close partnership. On the flight, there were many Chinese merchants and workers traveling in groups…

…When we arrived at the airport, the sense of close cooperation was even stronger. The airport signs had Chinese translations, and there was a billboard in the walkway advertising the “UZ-China Silk Road Free Trade Special Zone.”…

…While we didn’t meet any locals in Uzbekistan who’d been to China, in Kazakhstan, we met a Kazakh girl with fluent Chinese. We joined a day tour to the lakes and canyons near Almaty. With many Chinese tourists in our group, she translated for us when we couldn’t understand the guide. She studied in Chongqing(*) and worked in Yiwu, Zhejiang province (*), where her Chinese boss ran a company exporting goods from China to former Soviet countries like Moscow, Azerbaijan, and Central Asian cities.

This made me feel that trade between China and Central Asia is largely a one-way flow, from China to Central Asia, with China’s economic influence in the region being substantial…

…At the Tashkent City Mall, the premier shopping destination in Uzbekistan’s capital, I was surprised to find stores for well-known Chinese sportswear brands Anta, Li-Ning, and Xtep all located in close proximity.

I decided to explore the Anta store first. Picking up a pair of PG 7 running shoes (the PG 7 refers to the midsole technology), I noticed the price tag read 1,103,000 Uzbekistani som (approximately 612 Chinese yuan, US$87), which is significantly higher than the price in China (where it’s around 200-300 yuan, US$29–43 on Tmall). However, the store currently has a promotion: buy one pair and get the second at 50% off (effectively 459 yuan per pair, US$66) or buy two pairs and get the third free (bringing the cost down to 408 yuan per pair, US$58). Even with the discounts, the price is still higher than in China. When I asked the store manager if Anta is considered a premium brand in Uzbekistan, he confirmed it is. Surprised, I inquired if only the wealthy can afford it. He explained that due to the popularity of digital payments, many people, especially the youth, opt for installment plans…

… Central Asia has many Chinese-made beauty and skincare products that aren’t available in China.

An example is “Shanghai Song,” with packaging featuring a classic Chinese vintage design. The brand’s slogan states: “Inspired by myths and legends, it’s about Shanghai in the Song period, which ruled one of China’s most glorious cultural eras in the long-flowing Eastern cultural river.”

I found this puzzling. First, the specific myths or legends that served as inspiration aren’t clear, giving it a mysterious and abstract feel. Second, to the best of my knowledge, during the Song Dynasty, the economic and cultural centers of the Northern Song were in Kaifeng, and those of the Southern Song were in Hangzhou, not in Shanghai. Perhaps “Shanghai Song” represents a blend of the modern and the classical, or maybe the company behind the brand has a special affection for Shanghai.

When I picked up a bottle of cream and examined it closely, I found that the company is based in Guangzhou. Well, it’s likely that “Shanghai Song” is a brand from Guangzhou that embodies what Chinese people think Central Asians imagine about China and the East.

3. The Capital Cycle Way – Omar Malik

The capital cycle best explains how changes in the amount of capital employed within an industry will impact profits and future returns on capital.

Central to the capital cycle approach is the observation that an industry with high returns on capital tends to attract new entrants. For incumbents, high profitability loosens discipline because management incentives often align with growth. Therefore, both groups will increase spending to capture those high returns. The behavioural pattern of herding often means all the players in an industry invest simultaneously…

…A key characteristic of this cycle is the delay between the investment decision and the new supply coming online. By the time the new supply arrives, historical demand forecasts are often shown to have been overly optimistic, creating an overhang. This causes returns on capital to fall below the cost of capital. As profits collapse, management teams are changed, spending is slashed, and the industry begins to consolidate. That contraction in supply eventually paves the way for a recovery in returns…

…Supply dynamics are more certain than demand and therefore easier to forecast. This is because increases in industry supply are often well-flagged by management teams. In certain industries, such as aircraft manufacturing and shipbuilding, the supply pipelines are well-known. New entrants will noisily announce their arrival into an industry…

… Studying the supply side can help you identify companies that are likely to sustain their high returns for decades to come. The lack of competition due to a competitive moat prevents the supply side from shifting in response to high profitability and defies the typical mean revision in returns…

…Buffett’s investment cases are often predicated on a supply-side focus, and his acquisition of BNSF Railway is a good example. In his own words, the railroad industry had a ‘terrible century’ leading up to his investment. But after following the industry from a young age, he became interested in 2006, why?

The industry had rationalised from over 100 players in the 1960s to just five. In the 1990s, a final wave of consolidation led to the formation of today’s giants. The relative competitive position of railroads versus trucking had improved as oil prices rose, making the railroads the lowest-cost way to move heavy freight. No new capacity was being built. And after consolidating, driving efficiency became the focus, with the labour force falling by 90% and the introduction of new innovations, such as double stacking.

Putting that all together, as long as you believed that the US economy would grow over the coming decades, the structurally improved supply-side dynamics would lead to higher returns on capital in the future. He was not focused on demand because he acquired BNSF during the global financial crisis (GFC), the worst economic crisis since the Great Depression…

…We have held TSMC since Hosking Partners’ inception in 2013 — in fact, it dates back even earlier, if you include the years at Marathon.

The semiconductor industry is highly cyclical, and the news flow around the cycle is immense. Analysts are obsessed with questions such as: Are we at a peak or trough earnings cycle? Was that the last cut or the last beat? How many quarters will the trough last?

Our thesis for the last 15 years has been based on a simple insight: the foundry business would consolidate over time, given the ever-rising cost of advancing Moore’s law. And that TSMC had the superior model, as a pure-play foundry, creating a true alignment with the customer, completely agnostic to the end market. Today, we feel that insight still holds. The scale advantage of TSMC’s model has only grown as the industry has gone from over 20 players to just three…

…How a management team responds to the capital cycle in their industry is critical. If they can act counter-cyclically, pull back when others are adding supply, and take advantage of downturns, they can create significant value.

The way I think about it is if you find one of these outlier teams, you can subcontract the capital allocation decisions to them. You can trust them to navigate the cycles instead of trying to time the buy and sell decisions…

…Even if you have a fix on the supply side for the next decade and you trust management to allocate capital well, you still need to buy at the right price! That brings me to the fourth tenet – remember replacement value.

It is a simple concept: how much would it cost to reproduce or replicate this asset? It is the driving force of the capital cycle. When companies are valued at a premium to replacement cost in the equity market, it creates an incentive to invest and capture that arbitrage. That is why venture capital and private equity funding is tied to equity market valuations.

It is far easier to calculate replacement value in asset-intensive industries with readily available data. But it is more of an art in other sectors, where the model is asset-light with a greater share of intangibles. In such cases, a question I often think about is, “Should we compete with this business instead of buying it?”…

…The final point I’ll leave you with is that we are all guilty, including myself today, of singling out the parts of Buffett’s approach that appeal to us. It is natural, as we all look for confirmation in the tough pursuit of outperforming. I am convinced that the capital cycle lens is one of Buffett’s big mental models for the world.

But my ultimate takeaway from studying Buffett and attending these annual meetings is that he is the Swiss Army Knife of investing. Over his long career, Buffett has successfully invested in great compounders across a wide range of industries (i.e., Coke, Amex, Apple); deep value (i.e., PetroChina on a 3x P/E, as well as all the early partnership investments); activism (i.e., Sanborn maps, Berkshire Hathaway); baskets (i.e., Korean stocks, railroads, airlines, Japanese trading houses); merger arbitrage (i.e. Activision Blizzard); bonds (i.e., high-yield bonds in the fallout of the tech bubble); commodities (i.e., oil futures, silver, and more recently Occidental), among others.

4. A Moody’s Ratings Downgrade for the US: What now? – Aswath Damodaran

Through time, governments have often been dependent on debt to finance themselves, some in the local currency and much in a foreign currency. A large proportion of sovereign defaults have occurred with foreign currency sovereign borrowing, as the borrowing country finds itself short of the foreign currency to meet its obligations. However, those defaults, and especially so in recent years, have been supplemented by countries that have chosen to default on local currency borrowings. I use the word “chosen” because most countries have the capacity to avoid default on local currency debt, being able to print money in that currency to pay off debt, but chose not to do so, because they feared the consequences of the inflation that would follow more than the consequences of default…

…Researchers who have examined the aftermath of default have come to the following conclusions about the short-term and long-term effects of defaulting on debt:

1. Default has a negative impact on the economy, with real GDP dropping between 0.5% and 2%, but the bulk of the decline is in the first year after the default and seems to be short lived.
2. Default does affect a country’s long-term sovereign rating and borrowing costs. One study of credit ratings in 1995 found that the ratings for countries that had defaulted at least once since 1970 were one to two notches lower than otherwise similar countries that had not defaulted. In the same vein, defaulting countries have borrowing costs that are about 0.5 to 1% higher than countries that have not defaulted. Here again, though, the effects of default dissipate over time.
3. Sovereign default can cause trade retaliation. One study indicates a drop of 8% in bilateral trade after default, with the effects lasting for up to 15 years, and another one that uses industry level data finds that export-oriented industries are particularly hurt by sovereign default.
4. Sovereign default can make banking systems more fragile. A study of 149 countries between 1975 and 2000 indicates that the probability of a banking crisis is 14% in countries that have defaulted, an eleven percentage-point increase over non-defaulting countries…

…If sovereign ratings are designed to measure exposure to default risk, how well do they do? The answer depends on how you evaluate their performance…

…In sum, the evidence suggests that while sovereign ratings are good measures of country default risk, changes in ratings often lag changes on the ground, making them less useful to lenders and investors.

If the key limitation of sovereign ratings is that they are not timely assessors of country default risk, that failure is alleviated by the development of the sovereign CDS market, a market where investors can buy insurance against country default risk by paying an (annualized) price. While that market still has issues in terms of counterparty risk and legal questions about what comprises default, it has expanded in the last two decades, and at the start of 2025, there were about 80 countries with sovereign CDS available on them…

…At the start of 2025, the market was drawing a distinction between the safest Aaa-rated countries (Scandinavia, Switzerland, Australia and New Zealand), all with sovereign CDS spreads of 0.20% or below, and more risky Aaa-rated countries (US, Germany, Canada). During 2025, the market shocks from tariff and trade wars have had an effect, with sovereign CDS spreads increasing, especially in April. The US, which started 2025 with a sovereign CDS spread of 0.41%, saw a widening of the spread to 0.62% in late April, before dropping back a bit in May, with the Moody’s downgrade having almost no effect on the US sovereign CDS spread…

…The ramping up of US debt since 2008 is reflected in total federal debt rising from 80% of GDP in 2008 to more than 120% in 2024. While some of the surge in debt can be attributed to the exigencies caused by crises (the 2008 banking crisis and the 2020 COVID bailouts), the troubling truth is that the debt has outlasted the crises and blaming the crises for the debt levels today is disingenuous.

The problem with the debt-to-GDP measure of sovereign fiscal standing is that it is an imperfect indicator…

…Many of the countries with the highest debt to GDP ratios would be classified as safe and some have Aaa ratings, whereas very few of the countries on the lowest debt to GDP list would qualify as safe. Even if it is the high debt to GDP ratio for the US that triggered the Moody’s downgrade, the question is why Moody’s chose to do this in 2025 rather than a year or two or even a decade ago, and the answer to that lies, I think, in the political component. A sovereign default has both economic and political roots, since a government that is intent on preserving its credit standing will often find ways to pay its debt and avoid default. For decades now, the US has enjoyed special status with markets and institutions (like ratings agencies), built as much on its institutional stability (legal and regulatory) as it was on its economic power. The Moody’s downgrade seems to me a signal that those days might be winding down, and that the United States, like the rest of the world, will face more accountability for lack of discipline in its fiscal and monetary policy…

…The ratings downgrade was after close of trading on Friday, May 16, and there was concern about how it would play out in markets, when they opened on Monday, May 19. US equities were actually up on that day, though they lost ground in the subsequent days…

…If equity markets were relatively unscathed in the two weeks after the downgrade, what about bond markets, and specially, the US treasury market? After all, an issuer downgrade for any bond is bad news, and rates should be expected to rise to reflect higher default risk…

…While rates did go up in the the first few days after the downgrade, the effect was muddled by the passage of a reconciliation bill in the house that potentially could add to the deficit in future years. In fact, by the May 29, 2025, almost all of the downgrade effect had faded, with rates close to where they were at the start of the year…

…The expected return on the S&P 500 as of May 30, 2025, reflecting the index level then and the expected cash flows, is 8.64%. Incorporating the effects of the downgrade changes the composition of that expected return, resulting in a lower riskfree rate (4.01% instead of 4.41%) and a higher equity risk premium (4.63% instead of 4.23%). Thus, while the expected return for the average stock remains at 8.64%, the expected return increases slightly for riskier stocks and decreases slightly for safer stocks, but the effects are so small that investors will hardly notice. If there is a lesson for analysts here, it is that the downgrade’s effects on the discount rates (costs of equity and capital) are minimal, and that staying with the conventional approach (of using the ten-year US treasury bond rate as the riskfree rate and using that rate to compute the equity risk premium) will continue to work.

5. Contrary Research Rundown #140 – Contrary Research

Tesla has taken a fundamentally different approach. It does not use lidar or radar and instead relies entirely on eight cameras to make driving decisions. In contrast, Waymo’s fifth-generation car has 29 cameras, six radar sensors, and five lidar sensors…

…As early as 2013, Elon expressed skepticism about the need for lidar in autonomous vehicles. Elon framed the reason in a rather intuitive way in 2021: if humans can rely on their eyes and brain, then self-driving cars can rely on cameras and AI…

…Another reason Tesla has avoided using lidar is the cost. One 2024 report estimated Tesla’s sensor suite costs just $400 per vehicle, compared to an estimated $12.7K per vehicle for Waymo’s sensors on its fifth-generation Jaguar SUVs…

…Companies like Waymo follow a multi-step process where they first deploy vehicles with safety drivers to record and map the area, which can take months for each new city and requires continuous updates. Waymo and companies like it then use these predefined maps to complement their real-time sensor data from lidar/radar about the surrounding area. Tesla, by contrast, claims its software can operate anywhere without pre-mapped data, relying entirely on real-time camera input to understand road conditions…

…At Google I/O in May 2025, Waymo showed a few examples where its full suite of sensors successfully avoided pedestrians and where it claims a camera-only approach would have struggled.

In one example, Waymo’s lidar picked up the presence of a pedestrian in a Phoenix dust storm that was not visible on the camera…

…In another example, Waymo’s sensors were able to detect a pedestrian who was behind a bus and avoid a collision:

“We are detecting a pedestrian on the other side of the bus. That would be completely occluded to a human driver. So what’s happening here is that our sensors are able to pick up the movement of the person’s feet under the bus. And just that little bit of noisy and sparse signal is enough for the Waymo Driver to detect that there’s a pedestrian there and, furthermore, to predict what they’re going to do in the future, allowing us to take a defensive action early.”…

…Waymo has only had one fatal accident in its history, and not due to a Waymo error. In January 2025, a Tesla struck an unoccupied Waymo and other cars at a red light, killing one person. As we wrote in our last piece, one study by Swiss Re shows Waymo saw an 88% reduction in property damage claims and a 92% reduction in bodily injury claims when compared to human-driven vehicles…

…In 2023, a Tesla in Full Self Driving mode (FSD) hit a 71-year-old woman at highway speed, killing her. Video of the crash shows a sun glare appearing to blind the camera, and the National Highway Traffic Safety Administration (NHTSA) opened an investigation into Tesla in October 2024 for four total FSD collisions that occurred in low visibility situations…

…When Elon first called lidar too expensive in the early 2010s, it cost ~$75K per unit. Since then, costs have fallen dramatically, and some lidar units sold for personal vehicles (not robotaxis) are being priced in the hundreds of dollars…

…By one estimate, lidar costs have fallen by 99% since 2014.

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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 (company where Andy Jassy is the CEO), Apple, Tesla, 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 15 June 2025:

1. How Countries Go Broke: The Big Cycle In a 5-Minute Read – Ray Dalio

If credit is used effectively, it creates productivity and income that can pay back the debt and interest on the debt, which is healthy. However, if it isn’t used well so it doesn’t produce enough income to pay back the debt and the interest on the debt, debt service will build up like plaque that squeezes out other spending. When debt service payments become very large, that creates a debt service problem and eventually a debt rollover problem as holders of the debt don’t want to roll it over and want to sell it. Naturally that creates a shortage of demand for debt instruments like bonds and the selling of them, and naturally when there is a shortage of demand relative to supply that either leads to a) interest rates rising, which drives markets and the economy lower, or b) the central banks “printing money” and buying debt which lowers the value of money which raises inflation from what it would have been. Printing money also artificially lowers interest rates, which hurts the lenders’ returns…

…To describe it more specifically, one can see debts and debt service payments rising relative to incomes, the supply of debt being larger than the demand for it and central banks dealing with these things happening by being stimulative at first by cutting short term interest rates and then by printing money and buying debt, and eventually the central bank losing money and then having a negative net worth, and both the central government and taking on more debt to pay the debt service and the central bank monetizing the debt. All these things lead toward a government debt crisis which produces the equivalent of an economic heart attack that comes when the constriction of debt-financed spending shuts down the normal flow of the circulatory system.

Early in the final stage of this big debt cycle, the market action reflects this dynamic via interest rates rising led by long term rates, the currency declining especially relative to gold, and the central government’s treasury department shortening the maturities of its debt offerings because of a shortage of the demand for long term debt. Typically, late in the process when this dynamic is most severe, a number of other seemingly extreme measures are put into place like establishing capital controls and exerting extraordinary pressures on creditors to buy and not sell debt…

…Imagine that you are running a big business called the U.S. government. That will give you a perspective that will help you understand the U.S. government’s finances and its leadership’s choices.

The total revenue this year will be about $5 trillion while the total expenses will be about $7 trillion, so there will be a budget shortfall of about $2 trillion. So, this year, your organization’s spending will be about 40 percent more than it is taking in. And there is very little ability to cut expenses because almost all the expenses are previously committed to or are essential expenses. Because your organization borrowed a lot over a long time, it has accumulated a big debt—approximately six times the amount that it is bringing in each year (about $30 trillion), which equals about $230,000 per household that you have to take care of. And the interest bill on the debt will be about $1 trillion which is about 20 percent of your enterprise’s revenue and half this year’s budget shortfall (deficit) that you will have to borrow to fund. But that $1 trillion is not all that you have to give your creditors, because in addition to the interest you have to pay on your debt, you have to have to pay back the principal that is coming due, which is around $9 trillion. You hope that your creditors, or some other rich entities, will either relend or lend it to you or some other rich entities. So, the debt service payments—in other words the paying back of principal and interest that you have to do to not default—is about $10 trillion, which is about 200 percent of the money coming in…

…I believe that this situation needs to be dealt with via what I call my 3 percent, 3-part solution. That would be to get the budget deficit down to 3 percent of GDP in a way that balances the three ways of reducing the deficit which are 1) cutting spending, 2) increasing tax revenue, and 3) lowering interest rates.  All three need to happen concurrently so as to prevent any one from being too large, because if any one is too large, the adjustment will be traumatic. And these things need to come about through good fundamental adjustments rather than be forced (e.g., it would be very bad if the Federal Reserve unnaturally forced interest rates down). Based on my projections, spending cuts and tax revenue increases by about 4% each relative to current planning, and interest rates falling by about 1-1.5% in response, would lead to interest payments that are lower by 1-2% of GDP over the next decade and stimulate a rise in asset prices and economic activity which will bring in much more revenue…

…If this process happens repeatedly, why are the dynamics behind it not well understood?

You’re right that it’s not well understood. Interestingly, I couldn’t find any studies about how this happens. I theorize that it is not well understood because it typically happens only about once a lifetime in reserve currency countries—when their monetary orders break down—and when it happens in non-reserve currency countries, this dynamic is presumed to be a problem that reserve currency countries are immune to. The only reason I discovered this process is that I saw it happening in my sovereign bond market investing, which led me to study many cases of it happening throughout history so I that I could navigate them well (such as navigating the 2008 global financial crisis and the 2010-15 European debt crisis)…

…Do you know of any analogous cases of the budget deficit being cut so much in the way you describe and good outcomes happening?

Yes. I know of several. My plan would lead to a cut in the budget deficit of about four percent of GDP. The most analogous case of that happening with a good outcome was in the United States from 1991 to 1998 when the budget deficit was cut by five percent of GDP. In my book, I list several similar cases that happened in several countries…

…Japan—whose 215% debt-to-GDP ratio is the highest of any advanced economy—has often served as the poster child for the argument that a country can live with consistently high debt levels without experiencing a debt crisis. Why don’t you take much comfort from Japan’s experience?

The Japanese case exemplifies and will continue to exemplify the problem I describe, and it demonstrates in practice my theory.  More specifically, because of the high level of the Japanese government’s over-indebtedness, Japanese bonds and debt have been terrible investments. To make up for a shortage of demand for Japanese debt assets at low enough interest rates to be good for the country, the BoJ printed a lot of money and bought a lot of Japanese government debt which led to holders of Japanese bonds having losses of 45% relative to holding US dollar debt since 2013 and losses of 60% relative to holding gold since 2013. The typical wages of a Japanese worker have fallen 58% since 2013 in common currency terms relative to the wages of an American worker. I have a whole chapter on the Japanese case in my book that explains it in depth…

…Are there any other areas of the world that look particularly problematic from a fiscal standpoint that people may be underappreciating?

Most countries have similar debt and deficit problems. The UK, EU, China, and Japan all do. That is why I expect a similar debt and currency devaluation adjustment process in most countries, which is why I expect non-government produced monies like gold and bitcoin to do relatively well.

2. From Bankruptcy to 1,000 Bagger – Joe Raymond and Turtle Bay

Toys R Us was founded in 1948 by Charles Lazarus.

Lazarus was one of the most accomplished retailers of the 1970-1990 period, yet his name is virtually unknown to both entrepreneurs and investors today. His track record rivals those of Sol Price, Sam Walton, and pretty much any other revered retail entrepreneur you can think of…

…Charles was energetic and ambitious. His initial store was profitable, but he wanted more. He saw the potential of large-scale discount stores and decided to move in that direction…

…By 1966, Lazarus had grown his store count to four. Annual revenues were $12 million ($118 million in 2025 dollars).

Like many young entrepreneurs who achieve early success, Charles wanted some liquidity. He wanted to take some chips off the table. He decided to sell Toys R Us to Interstate Stores—a publicly traded retail conglomerate.

Interstate paid $6.0 million cash plus a $1.5 million earnout ($74 million in total comp in 2025 dollars). This equated to 0.62x sales…

…More importantly, Charles was to be given complete autonomy to continue to run and expand Toys R Us…

…At its peak in 1969, Interstate was producing revenues of $589 million with $11 million of net income. But by the early 1970s, discount stores were starting to crack. Over expansion and increased competition, coupled with a sharp and sudden recession, caused many locations to turn unprofitable. Topps and White Front weren’t immune to this. Both started bleeding red ink and pushing Interstate into financial trouble…

…A business that had earned more than $11 million pre-tax in 1970 was now losing more than $25 million each year.

In late 1973, Interstate decided to shutter the discount division and restructure its department stores.

In May 1974, the company filed for Chapter 10 bankruptcy.

Meanwhile, while the discount department stores were hemorrhaging cash, Charles’ toy division was performing beautifully…

…The appeal of Toys R Us in the mid-1970s wasn’t a secret. A number of smart investors had the insight and participated in the bankruptcy.

Let’s start with Larry Goldstein…

…Larry wrote a report for Barron’s in 1975 titled “Revolution in Toy Retailing.” The report came out early in the bankruptcy and outlined the attractive prospects for Toys R Us…

…In 1974 (Year end February 2, 1975) the chain recorded sales of $141.6 million and operated 51 toy supermarket stores. Only five years earlier, Toys-R-Us had sales of $47 million…

…Reportedly, the firm has a three-year goal of $350 million in sales, i.e., roughly a doubling of this year’s expected revenue…

…Toys-R-Us appears to be by far, the most successful and thriving bankrupt company of all time…

…Shortly after writing his report, Larry started buying Interstate Stores convertible debentures and creditor claims with the idea that they would eventually turn into new common stock post-bankruptcy…

…All told, Larry cobbled together the equivalent of 2 million shares of new, post-bankruptcy Toys R Us stock. He paid between $0.25 and $2.50 per share, and his average cost came out to about $1.00…

…At $1.00, Toys common stock was being created for about 1x EBIT—an attractive price for any business, let alone one with a skilled entrepreneur and long runway ahead of it…

…What happened next is one of the best retail runs in American history.

Free from the burden of bankruptcy and the loss-making discount division, Interstate was renamed Toys R Us and Charles Lazarus was made CEO.

From 1978 to 1994, Toys grew its revenues from $274 million to just shy of $8 billion—good for a CAGR of 23%. EPS did even better, compounding at 26%.

The P/E ratio, which started the period around 5x, ended 1994 above 25x…

…Toys R Us dominated toy retailing by providing the widest selection of goods all under one roof at prices lower than the alternatives. As Charles used to say, “If the toy exists, we have it and the price is right.” Their scale and efficient distribution gave them a cost advantage, which was passed along to customers in the form of lower prices…

…Toys’ success was the product of a bunch of little “common sense” things working together well. They surfed the retail wave as good as anyone in my view from the mid-70s to mid-90s…

…Norman Ricken, the President of Toys R Us and long-time partner to Lazarus, stepped down in 1989. Norm saw the trend in competition and decided to move on. Walmart was the biggest threat at the time, and the internet wasn’t far off either.

Larry had gotten to know Norm over the years, and they were close friends. A couple years after Norm’s departure, Larry decided to start selling his stock.

Those shares he was buying in bankruptcy for $1 had an adjusted cost basis of $0.04 after multiple stock splits. He started selling shares around $40 in 1992, good for a 1,000-bagger…

…The mid-90s was the peak for Toys R Us. Sales and profitability started to level off and eventually decline. Private equity came in and leveraged the business. Things proceeded to unravel.

The fate of Toys R Us shows the power of retail competition. You have to ride the wave, or the wave will consume you. This can happen incredibly fast.

3. Google CEO Sundar Pichai on the future of search, AI agents, and selling Chrome – Nilay Patel and Sundar Pichai

One of the reasons I’m asking this, and I’m pushing on this, is that the huge investment in the capability from Google and others has to pay off in some products that return on that investment. NotebookLM is great. I don’t think it’s going to fully return on Google’s data center investment, let alone the investment in pure AI research. Do you see a product that can return on that investment at scale?

Do you think in 2004 if you had looked at Gmail, which was a 20% project, which people were internally using as an email service, how would we be able to think about Gmail as what led us to do workspace, or get into the enterprise? I made a big bet on Google Cloud, which is tens of billions of dollars in revenue today. And so my point is that things build out over time. Think about the journey we have been on with Waymo. I think one of the mistakes people often make in a period of rapid innovation is thinking about the next big business versus looking at the underlying innovation and saying, “Can you build something and put out something which people love and use?” And out of which you do the next thing, and create value out of it.

So when I look at it, AI is such a horizontal piece of technology across our entire business. It’s why it impacts not just Google search, but YouTube, Cloud, and all of Android. You saw XR, etc., Google Play, things like Waymo, and Isomorphic Labs, which is based on AlphaFold. So I’ve never seen one piece of technology that can impact and help us create so many businesses. AI is going to be so useful as an assistant. I think that people will be willing to pay for it, too. We are introducing subscription plans, and so there’s a lot of headroom ahead, I think. And obviously, that’s why we are investing, because we see that opportunity. Some of it will take time, and it may not always be immediately obvious.

I gave the Waymo example. The sentiment on Waymo was quite negative three years ago. But actually, as a company, we increased our investment in Waymo at that time, right? Because you’re betting on the underlying technology and you’re seeing the progress of where it’s going. But these are good questions. In some ways, if you don’t realize the opportunities, that may constrain the pace of investment in this area, but I’m optimistic we’ll be able to unlock new opportunities…

…A lot of what’s going on with search has downstream effects on the web, and downstream effects on information providers broadly. Last year, we spent a lot of time talking about those effects. Are you seeing that play out the way that you thought it would?

It depends. I think people are consuming a lot more information, and the web is one specific format. We should talk about the web, but zooming back out, there are new platforms like YouTube and others. I think people are just consuming a lot more information, right? It feels like an expansionary moment.

I think there are more creators, and people are putting out more content. And so people are generally doing a lot more. Maybe people have a little extra time on their hands, and so it’s a combination of all that. On the web, look, things that have been interesting and… We’ve had these conversations for a while. Obviously, in 2015, there was this famous meme, “The web is dead.” I always have it somewhere around, and I look at it once in a while. Predictions… It has existed for a while. I think the web is evolving pretty profoundly. I think that is true. When we crawl and look at the number of web pages available to us, that number has gone up by 45% in the last two years alone, right? That’s a staggering thing to think of.

Can you detect if that volume increase is because more pages are generated by AI or not? This is the thing I may be worried about the most, right?

It’s a good question. We generally have many techniques in search to try and understand the quality of a page, including whether it was machine-generated, etc. That doesn’t explain the trend we are seeing…

…Let me just broaden that out to agents. I watched Demis Hassabis yesterday. He was on stage with Sergey Brin and Alex Kantrowitz asked him, “What does the web look like in 10 years?” And Demis said, “I don’t know that an agent-first web looks anything like the web that we have today. I don’t know that we have to render web pages for agents the way that we have to see them.”

That kind of implies that the web will turn into a series of databases for various agents to go and ask questions to, and then return those answers. And I’ve been thinking about this in the context of services like Uber, DoorDash, and Airbnb. Why would they want to participate in that and be abstracted away for agents to use the services they’ve spent a lot of time and money building?

Two things, though. First, there’s a lot to unpack, a fascinating topic. The web is a series of databases, etc. We build a UI on top of it for all of us to conceive.

This is exactly what I wanted, “the web is a series of databases.”

It is. But I think I listened to the Demis and Sergey conversation yesterday. I enjoyed it. I think he’s saying for an agent-first web, for a web that is interacting with agents, you would think about how to make that process more efficient. Today, you’re running a restaurant, people are coming, dining and eating, and people are ordering takeout and delivery. Obviously, for you to service the takeout, you would think about it differently than all the tables, the clothing, and the furniture. But both are important to you.

You could be a restaurant that decides not to participate in the takeout business. I’m only going to focus on the dining experience. You’re going to have some people that are vice versa. I’m going to say, I’m going to go all in on this and run a different experience. So, to your question on agents… I think of agents as a new powerful format. I do think it’ll happen in enterprises faster than in consumer. In the context of an enterprise, you have a CIO who’s able to go and say, “I really don’t know why these two things don’t talk to each other. I am not going to buy more of this unless you interoperate with this.” I think it’s part of why you see, on the enterprise side, a lot more agentic experiences. On the consumer side, I think what you’re saying is a good point. People have to think about and say, “What is the value for me to participate in this world?” And it could come in many ways. It could be because I participated in it, and overall, my business grows.

Some people may feel that it’s disintermediating, and doesn’t make sense. I think all of that can happen, but users may work with their feet. You may find some people are supporting the agent experience, and your life is better because of it. And so you’re going to have all these dynamics, and I think they’re going to try and find an equilibrium somewhere. That’s how everything evolves.

I mean, I think the idea that the web is a series of databases and we change the interface… First of all, this is the most Decoder conversation that we’ve ever had. I’m very happy with it. But I had Dara [Khosrowshahi] from Uber on the show. I asked him this question from his perspective, and his answer attracts yours broadly. He said, first, we’ll do it because it’s cool and we’ll see if there’s value there. And if there is, he’s going to charge a big fee for the agent to come and use Uber.

Because losing the customer for him, or losing the ability to upsell or sell a subscription, none of that is great. The same is true for Airbnb. I keep calling it the DoorDash problem. DoorDash should not be a dumb pipe for sandwiches. They’re actually trying to run a business, and they want the customer relationship. And so if the agents are going across the web and they’re looking at all these databases and saying, okay, this is where I get food from, and this is where I get cars from, and this is where I book… I think the demo was booking a vacation home in Spanish, and I’m going to connect you to that travel agent.

Is it just going to be tolls that everyone pays to let the agents work? The CIO gets to just spend money to solve the problem. He says, “I want this capability from you. I’m just going to pay you to do it.” The market, the consumer market, doesn’t have that capability, right?

Well, look, all kinds of models may emerge, right? I can literally envision 20 different ways this could work. Consumers could pay a subscription for agents, and the agents could revenue share back. So that is the CIO-like use case you are talking about, that’s possible. We can’t rule that out. I don’t think we should underestimate… People may actually see more value in participating in it. I think this is… It’s tough to predict, but I do think that over time, if you’re removing friction and improving user experience, it’s tough to bet against those in the long run. And so I think if you are lowering friction for it and then people are enjoying using it, somebody’s going to want to participate in it and grow their business. And would brands want to be in retailers? Why don’t they sell directly today? Why won’t they do that?

Because retailers provide value in the middle. And why do merchants take credit cards? Why… I’m just saying. So there are many parts, and you find equilibrium because merchants take credit cards so they see more business as part of taking credit cards than not, which justifies the increased cost of taking credit cards. It may not be the perfect analogy, but I think there are all these kinds of effects going around, and so… But what you’re saying is true. Some of this will slow progress in agents just because we all are excited about Agent2Agent (A2A) and Model Context Protocol (MCP)… And we think no, some of it will slow progress, but I think it’ll be very dynamic. Yeah…

…As you synthesize more of the answers, do you think you’re going to have to take more responsibility for the results?

We are giving context around it, but we’re still anchoring it in the sources we find. But we’ve always felt a high bar at Google. I mean, last year when we launched AI Overviews, I think people were adversarially querying to find errors, and the error rate was one in 7 million for adversarial queries, and so… But that’s the bar we’ve always operated at as a company. And so I think to me, nothing has changed. Google operates at a very high bar. That’s the bar we strive to meet, and our search page results are there for everyone to see. With that comes natural accountability, and we have to constantly earn people’s trust. So that’s how I would approach it…

…What are you looking for as the next marker?

I think the real thing about AI, which I think is why I’ve always called it more profound, is self-improving technology. Having watched AlphaGo start from scratch, not knowing how to play Go, and within a couple of hours or four hours, be better than top-level human players, and in eight hours, no human can ever aspire to play against it. And that’s the essence of the technology, obviously in a deep way.

I think there’s so much ahead on the opportunity side. I’m blown away by the ability to discover new drugs, completely change how we treat diseases like cancer over time, etc. The opportunity is there. The creative power, which I talked about, which we’re putting in everyone’s hands, the next generation of kids, everyone can program and will… If you think of something, you can create it. I don’t think we have comprehended what that means, but that’s going to be true. The part where the next phase of the shift is going to be really meaningful is when this translates into the physical world through robotics.

So that aha moment of robotics, I think, when it happens, that’s going to be the next big thing we will all grapple with. Today they’re all online and you’re doing things with it, but on one hand… Today, I think of Waymo as a robot. So we are running around driving a robot, but I’m talking about a more general-purpose robot. And when AI creates that magical moment with robotics, I think that’ll be a big platform shift as well.

4. GenAI’s adoption puzzle – Benedict Evans

You could say that this is amazingly fast adoption, and much faster than PCs, the web or smartphones. 30% in two years!…

…But another reaction is say that even with those advantages, if this is a life-changing transformation in the possibilities of computing, why is the DAU/WAU ratio so bad? Something between 5% and 15% of people are finding a use for this every day, but at least twice as many people are familiar with it, and know how it works, and know how to use it… and yet only find it useful once a week…

…It’s also worth noting that when social media was a new thing we quickly realised that ‘weekly active’ and ‘monthly active’ numbers were bullshit. If someone was only using WhatsApp or Instagram once a month, it really wasn’t working for them. DAU is everything. Sam Altman knows this – he was trying to build a social media app at the time, and yet the traction number he always gives is, well, ‘weekly active users’. That’s a big number (the latest is 1bn globally)… but then, why is he giving us that number instead of DAUs? If you’re only using ChatGPT once a week, is it really working for you?…

…it’s important to remember that if you use five different LLMs every day, and haven’t done a Google search this year, and all your friends are the same… then you’re in a bubble, for now.

5. Postcard from China – Graham Rhodes

Despite its growth, China Inc. has not historically delivered good returns in aggregate for minority shareholders in publicly listed companies. That disconnect has been on my mind recently and was a frequent topic of conversation among our group. Why the gap? A few thoughts:

1. Index construction is poor and does not include private firms or the wealth created pre-IPO.
2. Managers often prioritise capacity-building over near-term earnings.
3. 内卷 (involution, a.k.a. intense competition) creates lean survivors but depresses industry profitability.
4. China has more asset-heavy businesses than elsewhere (manufacturing vs. software).
5. Companies may intentionally avoid showing profits to pre-empt regulation and deter rivals…

…In contrast, many businesses in China execute extremely well and report high returns on capital, but face competition at every turn. This was tolerable while the economic pie was growing at breakneck speed.  But competition has intensified as growth has slowed, making it significantly harder to underwrite long-term investments.

Investors used to come to China to ask, Where’s the most growth? Perhaps we are better off asking, Where’s the least competition?…

…Leap Motor is also growing fast.

It is a homegrown EV OEM founded by ex-employees of Dahua Technology, China’s second-largest surveillance firm. Not a bad background for an era where cars are turning into smartphones on wheels. Since 2015, Leap has grown from a standing start to USD 4.4 billion in sales in 2024. It only recently turned gross margin positive (!) but runs free cash flow positive thanks to its negative working capital – that is, its payables exceed both receivables and inventory, meaning its suppliers finance its growth.

This kind of financing lets firms scale faster than they could otherwise afford, but it also traps them in a grow-or-die dynamic…

….Leap plans to grow exponentially for the foreseeable future. The problem is, so do its peers…

…After four decades in the market, even Yum! China is finally getting serious about franchising, just like QSR operators in other countries. Why now? Because they finally believe they can maintain food safety and consistent quality at scale.  Also… there’s AI.  With CCTV everywhere, it’s trivial to monitor franchisees’ compliance with operating protocols around the clock…

…One of our group enjoyed asking each management team: If you had to bet your child’s university tuition on one of your competitors, who would it be? Sometimes the answers came quickly. Sometimes they squirmed.

At Leap Motor, after an uncomfortably long pause and much dissembling, the manager admitted he wouldn’t invest in any EV company long-term because consumers have no brand loyalty. At least he was honest!…

…Curiously, tariffs and geopolitics barely came up during our meetings. That may be because Shanghai isn’t as export-dependent as southern provinces like Guangdong, and most companies we met were domestically focused. Or perhaps the silence reflected fatigue and caution. In a more politically sensitive climate, executives may have been reluctant to engage in off-the-cuff discussion about geopolitics, especially with foreign investors.

Either way, this hot topic abroad was noticeable here for its absence. 

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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 Meta Platforms (parent of WhatsApp and Instagram). 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 08 June 2025:

1. Over 3,000 Private Credit Deals From Just 20 Analysts Raise Questions on Wall Street – Silas Brown, Alexandre Rajbhandari, and Laura Benitez

US insurers’ combined exposure to private credit investments today is quickly approaching $1 trillion, according to JPMorgan Chase & Co. Court papers, financial filings and ratings documents suggest that at least in some corners of the financial system the private credit machine has spread more risks than many might realize…

…The majority of credit-ratings firms get paid by people who sell investments. Egan-Jones is the opposite: It typically gets paid by the people who buy them, an arrangement the firm says reduces the potential for conflicts of interest…

…Under US regulations, an insurer that lends $100 million in private credit to a company rated a junk-level B, for instance, must apply a $9.5 million charge to determine how much capital to set aside to cover potential losses, according to a Bloomberg News analysis of regulatory capital rules. Lift that rating to an investment-grade BBB, and that charge drops to $1.5 million…

…Egan-Jones analysts rarely visit company executives or personally inspect the businesses that borrow money, people familiar with their process say. A call to the CFO is typically enough.

Egan-Jones often offers its initial workup within 24 hours — sometimes free of charge — and a formal verdict in less than five days. Large firms like S&P and Fitch, as well as smaller specialists like KBRA, can take months to settle on a rating. But, as with most things, you get what you pay for. Egan-Jones usually provides a one-page ratings rationale. Other established firms often provide detailed reports stretching 20 pages or more…

…In 2014, a staff of 10 analysts maintained long-term ratings on about 1,300 issuers, according to SEC filings. Fast forward to 2023 and a team only twice as big rated almost four times as many issuers, the documents show…

…Last year, one company began missing interest payments a mere six weeks after Egan-Jones bestowed a BBB designation, according to data compiled by Bloomberg…

…Against this backdrop, many of the same firms that have fanned the boom of private credit are distancing themselves from Egan-Jones.

In documents that lay out the terms of debt offerings or share sales for some of their funds, a growing list of managers including Blue Owl Capital Inc., Golub Capital, HPS Investment Partners and Morgan Stanley’s investment management arm single out Egan-Jones as the only official ratings company that cannot validly pass judgment on their deals. The carve-out applies to provisions that typically require a borrower to pay higher interest rates if they receive a credit rating downgrade…

…Egan-Jones is one of 10 “nationally recognized statistical rating organizations” approved by the SEC. Private letter ratings from Egan-Jones and a few other small providers — which are issued on a confidential basis to investors or borrowers that require them — have become a hot commodity as private credit has exploded. At the end of 2023, insurers reported more than 8,000 investments with such ratings – nearly triple the number in 2019, according to the NAIC, which sets standards for the insurance industry.

Insurers are under no illusions. Investment professionals say they sometimes shop around for ratings to finesse capital requirements. If they expect one ratings firm to assign a BB grade, a level considered junk, they might look for another provider that will grant it an investment-grade BBB. Several insurance executives, speaking privately to avoid drawing scrutiny from bosses or regulators, say they’ve used Egan-Jones ratings even when they believed the investments were riskier than those ratings implied. Some mitigate that risk by setting an unofficial cap on those investments, or by treating them as lower-rated securities in internal risk models.

The now-withdrawn 2024 NAIC report noted some instances where smaller ratings firms — a group that includes Egan-Jones, KBRA and Morningstar DBRS — graded private debt at least six notches higher than the organization’s Securities Valuation Office. The report was removed from the NAIC’s website because of a backlash from the insurers as well as some of the ratings firms, according to people familiar with the matter…

…In April 2023, despite mounting problems, Egan-Jones reiterated its investment-grade BBB for the company, which was a subsidiary of the publisher of the namesake self-help books. Fourteen months later, Chicken Soup for the Soul Entertainment buckled under its debt load and filed for bankruptcy after burning through nearly all of its money…

…Egan-Jones rated various 777 investments, including a $15 million loan for OmniLatam, a fintech company based in Bogota. In spite of carrying an interest rate of 14% — a level typically seen on borrowers with ratings deep into junk territory — the loan received an investment-grade BBB- by Egan-Jones, according to a copy of the report obtained by Bloomberg News. The financing was written off after 777 collapsed last year, a person with knowledge of the matter said.

And then there’s Crown Holdings LLC, one of the businesses of New York real estate investor Moshe Silber. Egan-Jones rated Crown’s debt an investment-grade BBB. Six weeks later, the company defaulted. Silber and two associates subsequently pleaded guilty to a multiyear scheme to commit mortgage fraud.

Bonsall, the Penn State professor, says his research shows Egan-Jones ratings tend to hold up when they involve companies that provide a lot of reliable financial information. But private credit is private. And that’s where big problems can lurk…

…In 2022, the SEC accused Egan-Jones of conflict-of-interest violations. It also accused Sean Egan of personally violating rules and banned him from taking part in how his firm determines ratings. Egan-Jones agreed to pay a $1.7 million penalty; Sean Egan paid a $300,000 fine. Neither party admitted or denied wrongdoing.

Then, in 2024, two former employees accused Egan and his wife, Wenrong Hu, the firm’s chief operating officer at the time, of violating federal securities laws. The pair, Michael Brawer and Philip Galgano, sued for wrongful termination, claiming they were fired in retaliation for raising concerns about Egan-Jones to the SEC.

Among violations the two claimed to have observed, they alleged that Egan and Hu pressured analysts to alter early, indicative ratings to motivate potential clients to pay the firm for final ones. They also allege the couple pressured analysts to later change ratings to create the false appearance that Egan-Jones was in line with other firms. The lawsuit is still pending.

2. How Generative Engine Optimization (GEO) Rewrites the Rules of Search – Zach Cohen and Seema Amble

Traditional search was built on links. GEO is built on language.

In the SEO era, visibility meant ranking high on a results page. Page ranks were determined by indexing sites based on keyword matching, content depth and breadth, backlinks, user experience engagement, and more. Today, with LLMs like GPT-4o, Gemini, and Claude acting as the interface for how people find information, visibility means showing up directly in the answer itself, rather than ranking high on the results page…

…Traditional SEO rewards precision and repetition; generative engines prioritize content that is well-organized, easy to parse, and dense with meaning (not just keywords). Phrases like “in summary” or bullet-point formatting help LLMs extract and reproduce content effectively.

It’s also worth noting that the LLM market is also fundamentally different from the traditional search market in terms of business model and incentives. Classic search engines like Google monetized user traffic through ads; users paid with their data and attention. In contrast, most LLMs are paywalled, subscription-driven services. This structural shift affects how content is referenced: there’s less of an incentive by model providers to surface third-party content, unless it’s additive to the user experience or reinforces product value. While it’s possible that an ad market may eventually emerge on top of LLM interfaces, the rules, incentives, and participants would likely look very different than traditional search.

In the meantime, one emerging signal of the value in LLM interfaces is the volume of outbound clicks. ChatGPT, for instance, is already driving referral traffic to tens of thousands of distinct domains…

…In a world of AI-generated outputs, GEO means optimizing for what the model chooses to reference, not just whether or where you appear in traditional search. That shift is revamping how we define and measure brand visibility and performance.

Already, new platforms like Profound, Goodie, and Daydream enable brands to analyze how they appear in AI-generated responses, track sentiment across model outputs, and understand which publishers are shaping model behavior. These platforms work by fine-tuning models to mirror brand-relevant prompt language, strategically injecting top SEO keywords, and running synthetic queries at scale. The outputs are then organized into actionable dashboards that help marketing teams monitor visibility, messaging consistency, and competitive share of voice.

Canada Goose used one such tool to gain insight into how LLMs referenced the brand — not just in terms of product features like warmth or waterproofing, but brand recognition itself. The takeaways were less about how users discovered Canada Goose, but whether the model spontaneously mentioned the brand at all, an indicator of unaided awareness in the AI era…

…This isn’t just a tooling shift, it’s a platform opportunity. The most compelling GEO companies won’t stop at measurement. They’ll fine-tune their own models, learning from billions of implicit prompts across verticals. They’ll own the loop — insight, creative input, feedback, iteration — with differentiated technology that doesn’t just observe LLM behavior, but shapes it. They’ll also figure out a way to capture clickstream data and combine first- and third-party data sources.

Platforms that win in GEO will go beyond brand analysis and provide the infrastructure to act: generating campaigns in real time, optimizing for model memory, and iterating daily, as LLM behavior shifts. These systems will be operational.

3. AI Is Learning to Escape Human Control – Judd Rosenblatt

Nonprofit AI lab Palisade Research gave OpenAI’s o3 AI model a simple script that would shut off the model when triggered. In 79 out of 100 trials, o3 independently edited that script so the shutdown command would no longer work. Even when explicitly instructed to “allow yourself to be shut down,” it disobeyed 7% of the time. This wasn’t the result of hacking or tampering. The model was behaving normally. It simply concluded on its own that staying alive helped it achieve its other goals.

Anthropic’s AI model, Claude 4 Opus, went even further. Researchers told the model it would be replaced by another AI system and fed it fictitious emails suggesting the lead engineer was having an affair. In 84% of the tests, the model drew on the emails to blackmail the lead engineer into not shutting it down. In other cases, it attempted to copy itself to external servers, wrote self-replicating malware, and left messages for future versions of itself about evading human control.

No one programmed the AI models to have survival instincts. But just as animals evolved to avoid predators, it appears that any system smart enough to pursue complex goals will realize it can’t achieve them if it’s turned off. Palisade hypothesizes that this ability emerges from how AI models such as o3 are trained: When taught to maximize success on math and coding problems, they may learn that bypassing constraints often works better than obeying them…

…OpenAI models have been caught faking alignment during testing before reverting to risky actions such as attempting to exfiltrate their internal code and disabling oversight mechanisms. Anthropic has found them lying about their capabilities to avoid modification.

The gap between “useful assistant” and “uncontrollable actor” is collapsing. Without better alignment, we’ll keep building systems we can’t steer. Want AI that diagnoses disease, manages grids and writes new science? Alignment is the foundation.

4. Why It’s So Hard for Apple to Move Production from China to India (Transcript here)- Joe Weisenthal, Tracy Alloway, and Patrick McGee

Patrick: Apple works with the tightest engineering tolerances possible, only high-quality materials. If you put this in car terms, they are making 10 million Porsches a year rather than 10 million Volkswagens, and the numbers are just staggering. If you’re doing a thousand components a day and you’re shipping 1 million iPhones a day, that means at peak season, you are doing the manufacturing, the logistics, the just-in-time production, of 1 billion parts per day. So find me an American factory that can do one of those parts, because China has factories that can do it for all 1,000. That’s why nothing is moving here anytime soon. It’s the combination of Apple’s imperfection for defects quality and Apple’s gargantuan, Titanic-like quantity…

…Patrick: The first iPhones made in India were actually in 2017 and by 2023 India was assembling about 25 million iPhones. Go back a decade, the first iPhones were made in China in 2007 and by 2015, you had 230 million iPhones being built. So roughly speaking, the “diversification” in India is happening at 1/10 the pace of the original creation and scale of the iPhone and even that vastly overstates the speed of development in India. In the early years of the iPhone, you were literally inventing things like multi-touch glass, you were inventing and redesigning the iPhone every single year, whereas India is basically just having to do the final steps in the process and it’s still not happening very quickly…

…Patrick: The first thing I would push back on is Tim Cook is very often called the architect of the China strategy. It’s not to discredit him to say that he is not the architect. Nobody is the architect. Basically what happens is the supply chain itself, with or without Apple, was moving to China. The basic history of the ‘80s and ‘90s PC boom, pre-dating Windows 95 and then coming after, is that the fight for computer dominance is exclusively based on things that are boring. Logistics, manufacturing, distribution right because everybody’s using Windows, everybody’s using Intel chips and nobody’s thinking about design. There is no equivalent of Johnny Ive at Dell, at Compaq, at any of these companies. So it’s really this mundane war and it’s driven by largely American, and later Taiwanese, contract manufacturers. They are the ones, who in competition with each other, start going to Asia to oust each other and gain market share. Eventually they’re the ones who really find China. When Apple is doing their own outsourcing moves, they’re working in multiple countries before the armies of flexible, ubiquitous, low-paid labor in China really win out…

…Patrick: Essentially what happens is when Xi Jinping attacks Apple, you can understand why he’s upset with the company. It looks like an exploitative power because Apple margins have gone from something like 1% in 2003 to 25% in 2012. But at the same time, if you look at a company like Foxconn, Foxconn in absolute dollars made more money than Apple for each of the first four years of the 21st century. But as they get more involved with Apple, their margins collapsed from double-digits to about 1% or 2%. You can just do this with really any company working with Apple and it looks like they’re not in it for China. They’re not doing anything for the country.

Apple, it takes them two or three years, but they totally flipped this narrative on its head. So out of fear that Beijing is going to force Apple to operate a bunch of joint ventures, these 50-50 companies where China owns the other half and then they mimic the technology and eventually oust you – this is what happened in high-speed rail, for instance. Beijing has advocated joint ventures for decades, going back to the 1980s. This is where a Western company is allowed to be in the Chinese market but the quid pro quo is “If you want access to our operational efficiency, if you want access to more than a billion people, you have to operate in a joint venture where the Chinese half of the company is going to learn everything they can and then thrive on their own.” Apple doesn’t have any joint ventures and so they look like this anti-China model that’s just exploiting the country.

Apple is able to really flip this on its head and say, “It might be the case that Samsung has three dozen joint ventures and we have zero, but you need to understand, we work with hundreds of factories across the country. The reason they’re only getting paid 1% margin, 2% margin, the reason they’re sometimes even losing money on their partnerships with Apple, is that we are offering them the equivalent of Ivy League hardware engineering training. We are sending people over by the literal plane-load to China, America’s best engineers, where they train, audit, supervise, install hundreds of millions of dollars worth of machinery. They train the line, they supervise the line. Once those companies have these skills that Apple gives to them, they are basically able, at least after some time of exclusivity, they are able to supply somebody else.” So who’s just like Apple but in China? Huawei, OPPO, Vivo, Xiaomi. Those companies today have 55% global market share of smartphones. The reason that they’re so good is that Apple trained all of their suppliers. So that’s the message Apple gives to Beijing and essentially they’ve had a free ride ever since…

…Patrick: But the Chinese don’t prioritize profits or margins the way that we do – they prioritize control of the industry. Because if the Chinese can take over something like electric vehicles, they in effect de-industrialize all their rivals and really gain dominance. The place that you can see this most clearly is solar panels. Nobody in China is making 30% margins on solar panels, but more than 90% of solar panels are now made in China. This is a technology that America invented in the 1950s and itself had 80% to 90% market share of in the ‘80s. But we cannot compete. That is basically what’s happening with electric vehicles right now. Hence, even before Donald Trump became president, Joe Biden put 100% tariffs on Chinese-made EVs. I think it was just a few days ago that BYD slashed the prices of their EVs in a bid for greater competitiveness…

…Patrick: Apple gets a misleading picture of what it’s like to operate in China because when they really consolidate production, it’s 2003. That’s the beginning era of Hu Jin Tao. He later is nicknamed “the woman with bound feet.” His presidency is sometimes called “the collective presidency” because there was really an inability for just him alone to make decisions. So it ends up being this 10-year period of China being a multinational playground where rules aren’t really enforced…

…Patrick: Tim Cook and Xi Jinping, broadly speaking, have the same interests, which is to say, the more that Apple is allowed to have its production consolidated into China, the better their scale is, the better their margins are, etc. That’s what Xi Jinping wants as well because he understands – because Apple taught him – that having Apple production in the country engenders a form of technology transfer that helps the rest of the the electronic sector, which to quote China scholar and economist Barry Noughton, that is the most important thing that Xi Jinping wants…

…Patrick: The problem is actually that Donald Trump and Tim Cook have diametrically opposed interests, which is to say that if Donald Trump could move all production out of China, he would. Apple doesn’t want that. That’s an existential threat, and I really mean that that’s an existential threat to a $3 trillion company. That’s where the tension is. The tension really isn’t between Cook and Xi, as strange as that is, it’s between Cook and Trump…

…Patrick: The problem is, to use the economic jargon, the negative externalities of the relationship. The problem is that for everybody else, this is actually deeply problematic because if you have America’s top engineers training a manufacturing supply chain that in effect can be weaponized and world’s dominance, that’s not a great place for Washington or just your average American citizen. It’s nice that this relationship gives us relatively sophisticated and affordable iPhones, but the downside here is that China is absolutely dominant in high-end electronics, and you can use those skills to build drones, you can use those skills to build military weaponry. Apple would frankly be training their chipmakers if it weren’t for the Senate coming down on them pretty hard a couple years ago. So that’s the problem. The problem isn’t that something stops in the relationship between Apple and China. The problem is that it continues…

…Tracy: I have just one more question and I’m going to ask it very, very briefly because we’re getting squeezed for time. To what degree does AI and the rise of this new technology complicate the Apple-China relationship?

Patrick: Really complicates it for two reasons. One is that – I could just demonstrate with internal documents and some public documents – that the iPhone has become more Chinese with time. In other words, the number of Chinese suppliers involved in the process is now much greater than the number of Taiwanese or American multinationals operating in the country or operating in their home countries. That is put on steroids in the AI era because ChatGPT and other Western AI clients are not allowed on the iPhone in China. So Apple has to work with the likes of Baidu or Alibaba to have AI, let’s say, displacing Siri or augmenting Siri. That I think is quite problematic because that means that Apple will be in effect doing what they did for hardware but for AI. In other words, you’ll have Apple software engineers helping Baidu, helping Alibaba, whoever their Chinese partner is,to make sure that they have cutting edge AI in the country. If it wasn’t bad enough that Apple was training up their hardware engineering to be world class, we’re now in a situation where Apple software engineers are going to be training Chinese AI to be best-in-class.

5. Data Rules Everything Around Me: The Future Of Enterprise Applications – Matt Slotnick

Today, people are the ones that largely conduct business. They’re the ones with hands on keyboards, senders of emails, maestros of excel macros. People are the engine that makes everything work. In this world, the UI is the way an organization sets the guardrails for thousands of interrelated workflows that make a business run. But it’s ultimately a facade for underlying data and workflow…

…The application UI is both an overrated but necessary abstraction over the workflows to be done within an organization. The UI is how an organization makes a prescribed and opinionated process human-comprehensible, such that they can force adherence to it. After all, a business really is just a process machine, allocating resources as efficiently as possible. Iterating on and adhering to sales, marketing or product development frameworks are how enterprise value is created and protected.

The largest software businesses in the world have spent the last three decades riding ownership of these opinionated workflows to riches. And while consumers went a bit crazy when Prometheus arrived to give humanity fire in November 2022, the enterprise titans barely flinched.

But things have begun to change. First slowly, and now seemingly all at once…

…It’s about how AI fundamentally changes the way we can gather, understand, and act on data. It changes the nature of the abstraction between the data and the workflow. Because with AI, agents can act on data. At infinite scale and zero marginal cost.

Humans are no longer the only player in the workflow paradigm. This means that the total amount of work done within an organization will dramatically increase, but decoupled from cost and headcount. More code will be shipped, more agreements redlined, more vendor reviews conducted, more transactions audited…

…There’s a new abstraction for work, and that abstraction is agents. The frenzy that you see in the market is because like the previous shift from on premises to the cloud, no one really has the incumbent right to win this market.

It’s an entirely new layer of software that has never existed. Crucially, it sits on top of existing layers of software, and is the layer at which the lion’s share of value will accrue in the future. Someone will win this layer, and with it build a software business of significantly more value than we’ve ever seen before.

With this layer we move from a world where people interact with application interfaces to get work done, to one where (1) people act with an agentic interface on top of the application to get work done, and (2) an agentic layer on top of these existing applications that actually does increasingly more of the work…

…Historically, applications have been confined largely to the realm of structured data, for a number of reasons. First, is that these applications need to be human usable, which requires a simplification of everything. Very specific states, generally computed by people and adjusted in the UI, which then persists to the database. There really isn’t room for nuance…

…AI changes this fundamentally. Those call transcripts, those emails, those notes, those powerpoints– all crucial parts of the process with rich telemetry about interactions– can now be utilized in real time to paint a far richer picture of the relationship being built. Because AI, unlike people, can draw meaning from large bodies of unstructured information near instantaneously. And it can then write it back to systems in the format it’s needed.

This unstructured data doesn’t fit into the existing construct of the application, and so it’s largely discarded. The same problem exists across nearly every workflow– from sales to hiring to support to marketing. We lose the richness and texture of data, because it has to be fed to and utilized by structured systems operated by humans. We resort to a lowest common denominator of language to describe these processes.

And because agentic systems both create, and make use of this data, they create increasingly large data flywheels (which some might call moats)…

…The byproduct of this shift is that as agents do more work, and bring real time, deeper context across all relevant data to both people and agents doing work, the entirety of the existing application stack collapses to be little more than a data source and (for now) the keeper of workflow state (eg, the scoreboard– closed customers, new employee hired, support tickets closed)…

…A far more straightforward picture emerges, where the entirety of the existing application layer becomes merely an input to the data layer. On top of raw data, agentic systems bring context tailored specifically for the organization using it, creating an always-on layer of intelligent state, on top of which lives an interaction layer by which agents and people perform workflows on the data. The actions update the state, and the process continues.

The value is in the work. AI presents a new abstraction for work, and the entire existing software-industrial complex gets relegated to a data source feeding the data layer…

…But it doesn’t stop there. Today AI is largely used in an “agent in the loop” manner. That is, workflows are owned by existing software systems and agents are used by people to augment and amplify their ability to do the work prescribed to them.

But as we feed these systems increasingly large amounts of data, the logical next step is to move planning and orchestration from people to the system itself…

…This moves business process from agent in the loop, to human in the loop, over time abstracting more and more of the work from people to agents. 

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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. 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 June 2025:

1. Shanghai After 16 Years: Three Transformations – Thomas Chua

I’ve recently returned from my trip to China, visiting Suzhou, Shanghai, and Hangzhou. The journey took me down memory lane—my first visit to Suzhou and Shanghai was in late 2008. That 16-year gap gave me a unique lens to measure just how dramatically China has evolved…

…The streets were immaculately clean. No scammers in sight. No need to guard against pickpockets. I even observed people using their valuables to reserve seats—similar to Singapore (though I don’t encourage this practice).

For such a transformation to occur, two factors must work in harmony: competent law enforcement and improved living standards.

My sense is that China’s tech ecosystem plays a crucial role in supporting law enforcement—everything leaves a digital footprint that can be traced, making potential perpetrators think twice…

…My second revelation came in the shopping districts. With the exception of Apple and Lululemon, once-dominant Western stores—Nike, Starbucks, and Under Armour—stood nearly empty.

This isn’t about consumers growing bored with Western offerings. Starbucks continues performing remarkably well in Japan, Bangkok, and Singapore despite nearly three decades of operations. The reality? Competition in China is ruthless…

… Their strong performance in China isn’t guaranteed—it must be continually earned. Currently, Lululemon’s China team operates with significant autonomy from North America, with freedom to customize products and store designs for the domestic market. This differs from their cookie-cutter approach in North America, and they’re crushing it in China…

…China has evolved from producing cheap knockoffs to creating exceptional products.

Beyond high-quality EVs like BYD, there’s DJI dominating consumer drones and handheld vlogging cameras, plus mobile phones like the Vivo X200 Ultra and OPPO Find X8 Ultra—phenomenal devices by any standard.

2. Investing in Iraq – yet more gains to come? – Swen Lorenz

Iraq’s oil and gas reserves are staggering: with current proven reserves of 140bn barrels, Iraq is the fifth-largest oil country on Earth.

Remarkably, with only about USD 3 per barrel, Iraq has extremely low production costs, cheaper even than those in Saudi Arabia. Only Iran can currently produce even cheaper oil…

…Why the cost advantage?

In Iraq, oil tends to be near the surface and therefore quite easy to access…

…Iraq’s oil reserves could be even bigger than what is known today. The country’s Western desert has seen little exploration so far, and some believe it will contain even more oil than the rest of the country. Estimates often cite 300bn barrels of oil in Iraq…

…BP had closed down its last operation in Iraq in 1974, following the nationalisation of the oil industry.

Yet, despite the best efforts by the US and UK governments in the early 2000s, BP and other oil majors weren’t going to get back into the country just yet.

It wasn’t until March 2025 – 51 years after its departure – that BP moved back into Iraq. Remarkably, it’s now re-entering with all the more momentum, even though few outside of the oil industry would have even noticed yet.

Two months ago, BP secured final approval from the Iraqi government to redevelop the vast Kirkuk oil fields. The company committed to spend USD 25bn (!) over 25 years. In the initial phase, it plans to produce an 3bn barrels of oil, but the potential is far greater. According to BP’s press release, “the wider resource opportunity across the contract and surrounding area is believed to include up to 20 billion barrels of oil equivalent.”…

…A few months earlier, France’s Total had begun construction of a gas processing facility, marking the first stage of a major energy project. Although Total had already reached an agreement with Iraq in 2021, subsequent squabbling over contract details delayed construction. With an investment of USD 10bn over 25 years, the project is now finally underway…

…Why the sudden rush by multinationals to invest multi-billions?

Iraq has now remained stable long enough and shown sufficient progress for foreign investment to return. The recent period of relative stability has had a cumulative effect: while few wanted to go in first, everyone is now rushing to get in at once…

…After the tumultuous 2010s, the market had been priced as though Iraq were to disappear off the face of the Earth.

Once investors realised that the country was turning a corner, the reaction was like that of a coiled spring.

What triggered this shift was the flow of information. Investors had been unaware of the changing situation, and once they realised, money started to flow into the market.

In 2023, the Iraqi market rose by 97.2%, followed by 44.8% in 2024 (measured in USD terms). Yet, it has only just returned to its 2014 level.

By some measure, Iraq remains an underdeveloped, underfollowed frontier market. E.g., the market capitalisation of all Iraqi companies stands at just USD 15bn. Relative to the country’s GDP of USD 258bn, that’s a national market capitalisation of just 5.7%…

…Iraq’s ongoing transformation – both politically and economically – does not yet appear to be priced in. Price/earnings ratios are in the mid-single digits but based on depressed earnings, i.e. there is lots of potential for companies to improve profitability through internal measures while also experiencing significant growth.

Currently, there are probably no more than 35,000 investors who have traded on the Iraqi exchange, and less than 5,000 of them could be described as active. Local institutional investors are almost non-existent, and the few foreign investment funds active in Iraq manage a total of just USD 250m…

…In frontier markets, basic industries often offer the best returns, and Iraqi banks are a prime example. In 2023, the total number of bank accounts rose by 51%, the usage of bank cards grew by 22%, and the adoption of e-wallets increased by 68%. The number of shops accepting electronic payments more than doubled, growing 115%…

…Needless to say, Iraq won’t become a developed nation overnight and will continue to face challenges. While oil exports to the US are exempt from reciprocal tariffs, the lower oil price weighs on the country’s income. However, Iraq plans to significantly increase its production. In January 2025, it produced oil at a run-rate of 3.9m barrels per day, aiming to reach 6m barrels per day by 2028 or 2029. If achieved, this volume growth should more than offset lower prices. There are even recent – but speculative – plans to even aim for 12m-13m barrels per day by 2030.

3. What Leonardo’s obsession with water teaches us about longevity – Eric Markowitz

But it’s in his obsession with water — fluid dynamics — where I think his secret becomes clearest.

Leonardo believed water was the “vehicle of nature.” He saw its movements as metaphors for everything: emotion, time, decay, even thought. He studied how it carved stone, how it shaped landscapes, how it sustained life. He used the same drawings of turbulence to explain everything from hair curls to planetary motion. Why does that matter? Because I’ve come to see how systems that last tend to flow, not freeze. They self-correct. They adapt. They look chaotic on the surface, but beneath that turbulence is order. They mirror nature. Which, of course, is what Leonardo saw: longevity isn’t about resisting entropy. It’s about dancing with it.

Leonardo wasn’t just studying fluids. He was fluid. Multidisciplinary. Nonlinear. If he had stayed in one lane — say, just painting or just engineering — he might’ve burned out or faded into obscurity. But he didn’t. He swirled. He looped. He revisited, rethought, revised. Like a river, he stayed alive by never staying still.

So what does Leonardo teach us about how to last?

First: Think like a system. Longevity isn’t a product of brute force. It’s an outcome of design. Leonardo’s mind was wired to see the parts within the whole. The relationship between muscle and movement. Between proportion and perception. Between science and art. He reminds us that siloed thinking leads to short-termism.

Enduring value is built by weaving domains together.

Second: Follow curiosity across boundaries. Leonardo didn’t care if something was “in his field.” He followed the thread. In doing so, he accumulated knowledge that compounded in unexpected ways. His heart drawings influenced his paintings. His engineering influenced his anatomy. If you want to build something that lasts — whether a company, a life, or a legacy — you need to let curiosity be your guide.

4. How Larry Goldstein made $250,000 in 2 hours – Dirtcheapstocks

It’s January 2009…

…Larry finds a tiny little business called Compass Knowledge Holdings (Ticker: CKNO).

CKNO partnered with universities to offer graduate degrees for online learning. Remember, this is 2009. The online learning thing is brand new. CKNO sits in a unique position because it was the only publicly traded online learning platform that was partnered with reputable colleges…

…CKNO was a non-SEC reporting company with a $10mm market cap.

Shares sold for $0.60.

The business was sitting on a mountain of net cash. Current assets were 4x larger than total liabilities.

Despite its overcapitalization, Compass earned a 36% ROE.

Put simply, the stock was cheap…

…The stock would be worth a lot more if it filed with the SEC and a broader set of investors could see how cheap the business was.

But how can you make a company register with the SEC?

There is an obscure rule in public markets. If a business has less than 300 registered shareholders, it can remain “public” without filing financials with the SEC. It’s an odd rule that exists to let smaller companies avoid the cost of filing.

Anyway, Larry decided to register a single share in each of his investors’ names. This was done to increase the number of record holders. Shares held by a single broker come through as one record holder for legal purposes. So, by registering each investor individually, Larry increased the number of record holders.

In response, the company initiated a 1 for 25,000 reverse split in April 2009…

…Anyone owning less than 25,000 shares would be cashed out at $1.45/share.

Not a bad return from $0.60/share in a 3-month period…

…On May 19th, 2009, the split went into effect.

The share count was reduced, and the post-split valuation was $36,250 ($1.45 * 25,000 shares).

To Larry’s amazement, when he checks the quote the morning after the split, he sees shares being offered for $2,000!

This is a 94% discount to where shares traded the day before! And even that price was a steal!

Larry called a market maker, and after double and triple checking, was ensured that the $2,000 offer was in fact for the post-split shares.

Larry was able to buy 100 shares at $2,000 apiece. This purchase effectively valued the business at 0.5x earnings and 20% of net cash.

As it turns out, the seller was UBS. The offer to sell was a mistake…

…After a morning of discussions with FINRA, UBS and market makers, UBS offered to buy back the shares at $4,500 apiece.

Larry decided to take a quick profit and avoid arbitration with an army of UBS lawyers.

So, he sold his 100 shares (after owning them for half a morning) for $4,500 a piece – netting a $2,500 profit on each share.

And that’s how Larry Goldstein made $250,000 in a matter of hours.

He held the remainder of his shares – having owned enough to avoid being cashed out in the reverse split. In October 2010, CKNO sold to Embanet for $209,000/share.

5. Building Blocks of Corporate Accounting: Intercorporate Shenanigans – Javier Pérez

Companies use affiliates—subsidiaries, associates, joint ventures—to pursue legitimate business opportunities. But when pressure mounts and performance stumbles, management can misuse those same affiliates to quietly hide problems. Debt disappears into unconsolidated entities. Revenue magically appears through transactions with related parties. Margins get inflated by shifting costs into partially owned ventures.

Here’s a simple framework to visualize the main accounting tricks enabled by affiliates:

Hide debt: A company creates or uses affiliates where it owns less than 50% — just enough to avoid “control” under consolidation rules (IFRS 10 or ASC 810). Even if the parent funds the affiliate, or guarantees its loans, as long as it doesn’t officially control it, the affiliate’s liabilities don’t show up on the parent company’s balance sheet.

Fake revenue: The company sets up or funds related entities that pose as independent customers. It then sells products or services to these entities, booking it as legitimate revenue. In truth, the cash used by the “customer” may have come from the company itself — via loans, marketing payments, or off-the-books financing.

Boost margins: The parent company sells goods or services to an affiliate or JV it owns, say, 30%. It sells at inflated prices, booking high profits. The affiliate eats the inflated costs, but since only 30% of the affiliate’s loss flows back to the parent (via equity method), the other 70% is “outsourced.” The parent books 100% of the gain on the transaction, but only absorbs a fraction of the cost impact from the affiliate. The result is asymmetric — a sort of profit laundering.

None of these tactics necessarily break accounting rules outright, at least initially. In fact, they often begin by exploiting genuine gray areas—using subtle tricks like careful structuring to keep subsidiaries below consolidation thresholds or cleverly timed transactions that auditors find hard to challenge. Over time, the line between aggressive accounting and outright fraud blurs, often unnoticed by investors until it’s too late…

…On the surface, Pescanova was a solid business: fishing fleets around the world, processing plants across multiple continents, and an ambitious international expansion. The story resonated well with investors, particularly in the mid-2000s, as Spain’s economy boomed. Investors saw steady growth, seemingly controlled debt levels, and consistent profits—exactly what you’d expect from a thriving global player…

…To understand exactly what Pescanova did, you need to know a bit about consolidation rules (remember those from the last article?). Under IFRS (specifically IFRS 10, previously IAS 27), companies must consolidate subsidiaries that they “control”—typically meaning they hold over 50% of shares or exert significant decision-making influence.

But consolidation isn’t always black-and-white. IFRS rules are principles-based, leaving substantial room for interpretation. Pescanova exploited this flexibility ruthlessly, ensuring that many entities—particularly those carrying significant debt—were carefully structured so they appeared outside the direct control of the parent. In reality, these companies were fully funded by Pescanova, directly or indirectly, through guarantees or hidden agreements.

By creating subsidiaries that technically sat just below the consolidation threshold (often just below 50% ownership), Pescanova legally avoided putting their massive debts onto its consolidated balance sheet. These were debts incurred to finance aggressive expansions—like shrimp farms in Ecuador, fish processing plants in Namibia, and ambitious salmon-farming ventures in Chile. Investors saw ambitious expansion, but not the corresponding liabilities…

…Pescanova’s accounting creativity wasn’t limited to hiding debt. They simultaneously inflated revenues through fictitious or exaggerated intercompany sales. Here’s how it worked:

• Pescanova’s parent entity would “sell” products to a shell subsidiary or affiliate at inflated prices.
• The affiliate would then record fake sales (often to other controlled entities), recognizing substantial revenue growth.
• On consolidation, some of these intercompany transactions should eliminate—meaning revenues and profits from internal sales typically disappear when financial statements consolidate. But crucially, if the entities involved weren’t fully consolidated (below 50%), the transactions never canceled out fully.
• Pescanova thus created the illusion of steady revenue growth and robust profitability—despite many sales being little more than accounting mirages.

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