We’ve constantly been sharing a list of our recent reads in our weekly emails for The Good Investors.
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But since our readership-audience for The Good Investors is wider than our subscriber base, we think sharing the reading list regularly on the blog itself can benefit even more people. The articles we share touch on a wide range of topics, including investing, business, and the world in general.
Here are the articles for the week ending 19 July 2026:
1. The Great Wave Has Arrived – Tang Jie
If there is one thing we have learned over the past twenty years, it is this:
The greatest commercial opportunities never lie in minor adjustments to products or business models. They arise when the ceiling of intelligence itself makes a leap…
…Whoever can push that ceiling even one inch higher will be able to redefine the boundaries of what thousands of industries are capable of achieving. That single inch is precisely what the new generation of AI companies grounded in first principles are competing to secure…
…We have a simple but demanding definition of AGI:
AGI is not the intelligence of a single genius. It is the aggregate of all human intelligence.
It should be capable of creating original knowledge on the level of the theory of relativity. That is the only standard by which we measure whether the true summit has been reached.
On the road toward that destination, several mountains must be crossed. They are also where today’s technological wave is surging most powerfully.
The First Mountain: Long-Horizon Task Capability
The most exciting breakthrough today is teaching models to complete extremely long tasks—not merely answering questions immediately, but planning and executing over weeks, months, or even years…
…The Second Mountain: Fully Autonomous Agent Systems
Building on long-horizon capabilities, groups of agents that can operate independently, collaborate with one another, and work around the clock will become a new form of productivity…
…The Third Mountain: Self-Evolution
This is the most difficult—and also the most compelling—mountain of all.
AI training AI is already taking shape. Models are beginning to write their own code, clean and synthesize their own data, and train themselves…
…What will happen after these three mountains have been crossed?
AI will begin to learn what the “self” is and what self-awareness means. Beyond that, it may begin to touch human emotion. Farther still lies consciousness itself.
From perception to cognition, from cognition to general intelligence, and from general intelligence toward artificial superintelligence, or ASI—the road has already been laid…
…When AGI arrives, today’s applications may all need to be rebuilt as AI-native systems—or may no longer be needed at all.
Operating systems themselves may be rewritten. In the future, when you turn on a computer, what you see may be an “LLM OS,” with every function generated on demand.
Going deeper still, this represents a challenge to the von Neumann architecture that has underpinned computing for the past eighty years…
…As the supply of high-quality human-generated data approaches exhaustion, we will turn computing power into fuel for evolution.
This means building factories for high-quality synthetic data, using AI-versus-AI competition through self-play to generate knowledge from scratch, and giving systems the ability to reconstruct their own code within secure sandboxes.
The goal is to free the pace of evolution from the physical limitations of human engineers…
…The more powerful AI becomes, the more robust its safety constraints must be.
From the very beginning, Zhipu established a guiding principle:
AI must serve human well-being and national strategic priorities.
The company rejects bolt-on safety patches. Instead, it seeks to encode human ethics, social norms, and national laws and regulations into the model’s value function as foundational axioms.
We plan to commit resources on the scale of tens of billions to advancing mechanistic interpretability—clarifying the neural logic behind model decisions and transforming black-box systems into transparent, explainable ones.
2. The Reverse Information Paradox – Satya Nadella
In the AI age, the buyer risks giving away knowledge, just in order to use what they bought.
You essentially pay for intelligence twice, once with money, and again with something even more valuable: the proprietary knowledge you must reveal to make that intelligence useful. The better you want the model to perform, the more of that knowledge you have to feed it!
Over time, the information asymmetry becomes increasingly skewed. The seller learns more and more about you as you use what you purchased, while you learn very little about what the seller is learning in return.
That is what I think of as the Reverse Information Paradox…
… That is why enterprises need a real trust boundary for their human capital and token capital to compound. It is where an organization’s data, traces, evals, adapted weights, and memory accumulate and improve together. And it is a hard boundary across which nothing crosses, not even the intelligence exhaust, without consent. Enterprises will demand the rights to use model outputs to fine tune and/or train their own models. I think of this as every firm’s right to align models to their enterprise accountability obligations.
3. Away From the Casino Tables: The Wildest Value Gap Since 1999 – Sam Ziff
The chart below splits global equities into four valuation buckets, running from deep value to extreme growth, with each cohort’s current valuation shown as a premium or discount to its own 40-year median…
…The cheapest bucket globally (“deep value”) trades at a 45% discount to its historical average, while extreme growth sits at a 40% premium. Shallow value outside the US remains meaningfully cheap. The further you move from US large-cap growth, the more likely you are to find something trading below its long-run valuation. This is the backdrop against which we invest…
…The last time markets were this concentrated and this expensive, the next decade belonged to the parts of the market the crowd had forgotten. From the end of 1999 to the end of 2009, the MSCI World and S&P 500 delivered close to a zero total return, while emerging markets doubled and value outperformed growth.
4. The 221-year-old company that reinvented itself — 4 times – Eric Markowitz
Meet Jean-Joseph D’Ieteren.
In 1805, the 14-year-old orphan took over a small workshop in Brussels and started making wheels. Over the next 220 years, the company D’Ieteren founded became a carriage maker, then a builder of custom car bodies, then an importer of American automobiles, then the exclusive Belgian distributor of Volkswagen, then the world leader in automotive glass repair, and then — in a move that raised eyebrows across Europe — the owner of Moleskine, the Italian notebook brand.
Today, the D’Ieteren Group operates in more than 40 countries, employs over 32,000 people, and generates annual revenues exceeding $8.2 billion…
…The D’Ieteren family maintains a private museum tucked inside an ordinary working building on the Rue du Mail — roughly 5,000 square feet that most people in Brussels don’t know exists. There’s no sign outside. You call ahead, state your reason for visiting, and wait to hear if you’ve been accepted. If you are, you walk up a ramp, down a long corridor, and through a large sliding door. Then the city disappears…
…The company had walked away from its core business four times, yet it had preserved, with extraordinary care, a museum’s worth of carriages, photographs, and tools. It wasn’t the behavior of a company that had escaped its past. It was the behavior of a company that understood its past so precisely that it knew exactly what to keep and what to release…
…For most of the 19th century, D’Ieteren was a carriage maker of growing renown. It won medals at international exhibitions, earned the title of Supplier to the Royal Court, and built some of the most celebrated horse-drawn vehicles in Europe…
…In 1898, while still producing horse-drawn carriages, they built bodywork for 12 electric vehicles commissioned by Camille Jenatzy, the Belgian race car driver who would soon become the first person to break the 62 mph (100 km/h) land speed record…
…After a fire destroyed the old workshops around 1903, the family rebuilt with modernized facilities capable of handling the new work on a greater scale. For the next two decades, horse-drawn and automotive production coexisted — in many cases with the same craftsmen serving the same clients, who often owned both kinds of vehicles…
…On the eve of the 1929 stock market crash, D’Ieteren employed nearly 500 craftsmen and was exporting 65% of its production to Argentina, Egypt, Spain, and the United States. A single car would cost approximately $468,700 in today’s dollars.
But Lucien had also spent 15 years sitting with an uncomfortable truth.
During World War I, while assigned to a military vehicle depot in Le Havre, he had watched American cars roll through by the thousands: Studebakers, Packards, Fords. Standardized, efficiently built, priced for ordinary people. He came home understanding that the future was volume, not bespoke cars…
…What the crash did for Lucien — what crisis so often does — was to remove the social and psychological costs of changing. The reputation, the pride, the loyalty to craftsmen: all of it was real, and all of it had kept him from acting on what he already knew. The crash stripped that away…
…Crisis has a way of burning off the unnecessary, leaving behind only what has always been true.
One of the most commonly asked questions is, “What do you do?”…
…The better question — the harder question — is not what we do, but who we are.
Consider D’Ieteren.
In a narrow technical sense, they were a carriage maker, then a carmaker, then a glass repair company. If you’d asked them in 1850 what they did, they’d have said carriages. But that wasn’t really who they were. Who they were was a tight-knit family obsessed with quality, committed to the long view, and devoted to finding better ways for people to move through the world. The carriage was just the expression of that deeper question at the time.
5. A Framework for Frontier AI and the Dawning of a New Age – Demis Hassabis
AGI cannot be compared to standard technological breakthroughs, not even ones as consequential as the internet or mobile – it is much more akin to the discovery of electricity or fire…
…The magnitude of this technology’s impact will be unprecedented, perhaps 10x of the Industrial Revolution at 10x the speed. It will help us solve some of the biggest problems society faces from accelerating drug discovery to developing new clean energy sources to creating novel advanced materials. We could even reach a point where resources are no longer the limiting factor for human progress, leading to an amazing new era of abundance…
…Urgent action is needed to address risks that might arise as we get closer to AGI. We’ve already seen the challenges frontier models pose for cybersecurity, and other threats including nuclear and bio risks may soon emerge as capabilities continue to advance. On the horizon, we will need robust safeguards to maintain control of increasingly agentic, recursively self-improving systems – and tackle unknown issues that will only become clearer over time…
…The rapid progress we’re seeing in AI requires a new approach to testing frontier AI model capabilities that is dynamic, adaptable, and rigorous. The US is well positioned, given its economic and technical standing, to take the first step in developing such a framework. It could establish a new Standards Body modelled on a federally overseen public-private partnership or self-regulatory organisation, much like the Financial Industry Regulatory Authority (FINRA), with a board that includes independent leading technical experts and open-source representatives…
…A model would qualify as ‘Frontier-class’ if it meets certain thresholds on a set of benchmarks determined by the Standards Body and regularly updated to keep pace with evolving AI capabilities. Organisations with ‘Frontier Models’ as defined by those benchmarks would be deemed ‘Frontier Labs’, and be encouraged to adopt best practices, such as publishing model cards with technical details, maintaining strong internal cybersecurity, vetting key personnel, and providing sufficient resourcing for safety and security research, and more.
Initially, Frontier Labs would voluntarily share models with the Standards Body for review up to 30 days before release. Once the assessment protocol is shown to be effective and robust, formalisation could quickly follow, meaning that Frontier Models would be required to pass it to be deployed in the US market…
…Model assessments should include rigorous scientific evaluations of capabilities in cybersecurity, biological threats and other high-risk domains…
…Even if we solve these hard technical challenges, there will be further complex economic and philosophical questions to tackle: what sorts of new economic models will be needed to help everyone thrive in a post-scarcity world? What values do we want to live by, what will meaning and purpose be, and how might even the human condition itself change? Resolving these questions obviously cannot and should not be left to technologists alone. It requires every part of society to come together to help define this new chapter.
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 (Demis Hassabis is the CEO of Google Deepmind, an Alphabet company) and Microsoft (Satya Nadella is the CEO of the company). Holdings are subject to change at any time.