Author: admin

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 28 June 2026:

1. The State of the AI Economy – Azeem Azhar, William Gildea, Hannah Petrovic, Nathan Warren and Marija Gavrilov

$110bn trailing 12-month revenues – now at a $175bn pace…

…AI is scaling three times faster than any IT wave…

…AI demand is reigniting a moribund US power sector

1950-2008: +6 TWh/month annual growth

2008-2024: ±0 growth

2024-today: +9 TWh/month annual growth…

…Against GDP, AI revenue is still a rounding error

Still tiny: AI revenue is equivalent to 0.42% of US GDP (vs IT sector’s 9.4%)…

…Seven in ten GenAI claims focus on cost savings or efficiency

Claimed AI outcomes 

S&P 500, Q4 2022 – Q1 2026

Revenue gain: 6%

Conversion improvement: 7%

Quality improvement: 18%

Throughput increase: 22%

Time savings: 23%

Cost reduction: 25%…

…Revenues cover the ongoing expense, not yet the cumulative bill

Q4 2025: Quarterly revenues first exceed CapEx depreciation…

…Still ~half-covered: cumulative revenue has nearly covered cumulative depreciation, but still has to cover the expected headroom…

…AI infra revenue now just clears today’s depreciation hurdle

GenAI revenues now cover the quarterly depreciation of AI infrastructure. Q1 26 headroom reached 19% for hyperscaler/neocloud revenues and 32% across all GenAI revenues.

Coverage remains thin. Depreciation absorbs roughly 81% of hyperscaler/neocloud GenAI revenue and 68% of total GenAI revenue before additional costs.

The next test is incremental coverage. As committed AI capex enters service, the depreciation base will rise. Revenue growth, utilization and pricing must continue to compound or headroom will compress again…

…Gross rental yields suggest useful lives extend past six years

Older GPUs earn yields long beyond their six-year depreciation life…

…This efficiency is increasing monetization per GW of capacity while revenues per token fall

Revenue per trillion tokens has fallen since its 2023 peak, mirroring price declines.

Efficiency gains drive lower token prices, which are more than offset by higher demand…

…AI demand is more revenue-validated than any prior platform shift. The investment case comes down to whether falling prices can move enough token volume to earn a return on CapEx.

2. Morgan Stanley Pitches Clients on a New Market for Data Center Loans – Dakin Campbell

Over the last few months, Morgan Stanley has suggested to clients that the next time they need to raise money for data center projects, they consider the leveraged loan market rather than the bond market, according to a person familiar with the matter who asked for anonymity to discuss private conversations.

Leveraged loans are those made to companies that don’t have investment-grade credit ratings, typically because they don’t have businesses that throw off lots of cash or they already have lots of debt. Such borrowers could include AI firms like OpenAI or new cloud providers such as CoreWeave…

…Leveraged loans are typically underwritten by an investment bank like Morgan Stanley. Most are then sold to financiers that bundle the loans into a single pool. That pool is then sliced up and resold to other investors based on their risk tolerance. These pools are known as collateralized loan obligations…

…Last month, Morgan Stanley brought the first AI-linked offering to the leveraged loan market when it sold $3.1 billion of notes on behalf of CoreWeave, which said it would use the proceeds to buy chips for OpenAI and Cohere. Investors placed more than $19 billion of orders, Bloomberg reported…

…Until now, most data center financing has been done via the bond market, either as junk bonds or—in the case of cash-rich tech firms like Google—less expensive investment-grade bonds…

…Other questions include the identity of the company actually leasing the space in the data center, and whether loans to finance chips get paid down on a schedule parallel to the chips’ expected useful life.

CLOs are a type of structured credit product, similar to the collateralized debt obligations that bundled mortgage loans and derivatives in the run-up to the financial crisis—debts that then suffered tens of billions of dollars in losses. CLOs haven’t experienced a similar blow-up, but many industry watchers worry that they contribute to financial instability by spreading the risk into corners of the financial system that can be hard to track.

3. China’s tribute system and the new world order – Ray Dalio

China is earning huge amounts of money from its exports, so Chinese companies and banks are building up large capital surpluses and accumulating buying power. This is exerting upward pressure on the Chinese renminbi relative to the US dollar and leading to its increased use for trade and capital transactions. Chinese investors and capital markets are emerging as competitors to their American counterparts…

…The tribute system was informed by Confucian values — in particular the idea that order comes from having clearly defined hierarchical roles. Relations within it are not between equals, but between superiors and subordinates that recognise their relative positions. The more powerful ones in the hierarchy should treat the less powerful well, and the less powerful should treat the more powerful well, so that there is harmony. If a lesser power treats the greater power inappropriately, the more powerful one punishes it, typically not violently but through pressure and deception. As Sun Tzu wrote in The Art of War, “to subdue the enemy without fighting is the acme of skill”…

…A military blockade that stops chip exports is just one of many potential pressure-points that China can exploit, but it is notable because the Chinese have a plan to be self-sufficient in chip production by late 2028, while the rest of the world will remain dependent on Taiwan.

Given these circumstances, China could put the US into the awkward position of needing to choose between fighting or not fighting, with each choice not to engage leading to the perception of diminished American power, so that China can gain ground by simply making threats. 

4. Is Ray Dalio correct that China is reviving the tribute system? – Arnaud Bertrand

China’s ancient tribute system – called 朝贡 (cháogòng) in Chinese – is typically very misunderstood in the West: we typically think it involved tributary states paying some form of “tribute” to China in exchange for protection – the way medieval vassals would pay fealty to a lord in Europe…

…The system was basically a quid-pro-quo where China would get “得名” (dé míng, literally “getting name/prestige”) while tributary states would get “得实” (dé shí, literally “getting substance/material benefit”) in exchange. It was about China paying huge amounts of money and other material benefits for the recognition of its centrality…

…Very concretely the way it worked is that tributary states would pay largely symbolic tribute to China (like local specialties and curiosities, the system codified that tribute should be “easy to obtain and not costly”, 必易得而不贵) and they would in exchange receive 3 layers of economic benefits:

Immediate payback in the form of money and expensive goods (silk, brocade, porcelain, tea, silver, etc.), which value was typically dozens of times the value of the tribute received by the emperor The right to trade during their tribute visit: the envoys’ entourage could trade with specially licensed Chinese merchants at the Huìtóngguǎn (会同馆, the official guesthouse in the capital) Most importantly, and that’s where the real money was, they would be granted the right to trade at Chinese ports. Under the Ming maritime prohibition, tributary status was the only legal entry point into the Chinese economy…

…He is however wrong to describe the tribute system as one fundamentally based on pressure and intimidation. As we’ve just seen, it was pretty much the opposite: the basic idea was to be so generous that everyone wants in (to the extent that countries would literally fight to be tributaries), not so threatening that nobody dares leave…

…That being said, he is ironically correct – I think – that there is some form of revival of a tribute-like system but not in the way he understands it: China will (and does) use trade – its “generosity” – as a gravitational force to pull countries into its orbit. Not by threatening to cut them off, but by making the relationship too valuable to walk away from. THAT is much closer to how the actual Chaogong system worked…

…Which, incidentally, is why you can be extremely confident that China will go to enormous lengths to develop its internal market, and why the current situation where China runs huge trade surpluses is facing mounting pressure to change from within China itself. If countries don’t feel they’re benefiting enough from trade with China, the entire logic collapses. That’s why developing domestic demand isn’t some target China sets itself to assuage Western demands, as some claim: it’s genuinely a strategic imperative.

It’s also why it’s ironic that the West is so keen on pushing China to boost domestic consumption: in effect, it means we’re already in a de-facto Chaogong-like system and they’re asking that the carrot be bigger.

5. Oil Prices Make a Stunning Retreat to Prewar Levels. Where Do We Go From Here? – Collin Eaton and Benoît Morenne

The U.S. war with Iran—and the economic war the latter waged in return—was supposed to be an apocalyptic moment for the oil market. Instead, oil prices are on the cusp of falling back to their prewar levels.

Their stunning round trip, just 11 days after President Trump reached a 60-day deal to reopen the Strait of Hormuz, has disrupted widespread expectations that the global oil market’s recovery would take months, at minimum…

…Tankers loaded with crude are leaving the waterway in droves; gulf countries are racing to resume crude exports; and some of the largest buyers of crude on the planet are proceeding without using as much oil. Analysts at JPMorgan Chase said this week that global energy flows had shifted in ways they hadn’t expected.

“The market has rebalanced through a meaningfully different mix of demand losses and inventory withdrawals than we initially assumed,” they said.

The reprieve could be short-lived. Some oil analysts are warning that the sinking prices don’t fully reflect how tight the market remains after months of draws on global oil inventories, which are now flirting with operational limits…

…Tanker traffic through the strait has climbed swiftly since the U.S. and Iran struck an accord on June 14. A postwar record of 78 tankers sailed through the waterway on Wednesday, up from a previous high of 49, according to S&P Global. That represents 57% of prewar traffic levels…

…Oil demand in China, the world’s largest importer of crude, appears to have fallen faster than JPMorgan analysts anticipated, implying that its economy might be adapting to higher energy prices more efficiently than experience would indicate, they said…

…Whether China picks up new purchases in the coming weeks will have a huge influence on the markets. Analysts said the country might not want to reduce its strategic reserves further…

…Over the past three weeks, roughly 2 million barrels of oil a day has come back on to the market, with Iran pumping out barrels faster than Saudi Arabia and the U.A.E., according to the research firm Rystad Energy. But it will likely take until October for Iraq, Kuwait and other gulf countries that had to slash production to pump oil at full speed, analysts said.

These barrels of oil aren’t immediately available to stocks around the world, which are still being depleted.  

---

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 no vested interest in any company mentioned. Holdings are subject to change at any time. 

We’ve constantly been sharing a list of our recent reads in our weekly emails for The Good Investors.

Do subscribe for our weekly updates through the orange box in the blog (it’s on the side if you’re using a computer, and all the way at the bottom if you’re using mobile) – it’s free!

But since our readership-audience for The Good Investors is wider than our subscriber base, we think sharing the reading list regularly on the blog itself can benefit even more people. The articles we share touch on a wide range of topics, including investing, business, and the world in general. 

Here are the articles for the week ending 21 June 2026:

1. Tenneco Automotive: Charlie Munger’s $80 Million Bargain, Part 1 – Tim Isgro

The story of Charlie Munger’s investment in Tenneco Automotive is a fascinating one. And as far as I can tell, it’s only been told in a cursory way before now.

Munger made the investment in 2001 and it likely returned to him somewhere between 4 1/2 to 7 times his money and an annualized return over three years of 65% to 93%…

…What remained at the end was Tenneco’s automotive business, which sold emissions products (exhaust systems) and ride control products (like shocks and struts). It was this business, Tenneco Automotive Inc., that Munger was considering in 2001.

To say the above series of transactions dramatically changed the nature of Tenneco’s business is an understatement. The company went from being a large, diversified conglomerate with $13.2 billion in revenue in 1993 to a smaller single-line automotive businesses with just $3.5 billion in revenue in 2000…

…After all these spinoff transactions were finished, Tenneco was left with approximately $1.5 billion of long-term debt. After reading the history of Tenneco above, I suspect the automotive business was a victim of circumstance with respect to its debt load, being the last business standing after management spun off or sold five others…

…Focus for a moment on the company’s Operating Income (EBIT) and Interest Expense. Prior to the spinoff of Pactiv, the company was doing well, earning $633mn in EBIT in 1998 and spending $240mn in interest payments, but after the spinoff, the company was earning only $115mn in EBIT and spending $186mn in interest payments…

…To add insult to injury, Tenneco’s revenues were also suffering from the 2001 recession.

Tenneco served two broad sets of customers, original equipment manufacturers (auto makers) and the aftermarket (auto repair shops). Both were suffering lower sales…

…Not only were the interest payments on Tenneco’s debt too much for the company to handle in the years after completing its spinoff, but, on top of that, principal payments were starting to come due in 2001. The annual report from 2000 lists those upcoming maturities as $54 million, $109 million, and $99 million for 2001, 2002, and 2003, respectively. From the perspective of a casual analyst or observer, it was not clear at all how Tenneco could make those payments or how likely they were to work with their lenders on renegotiating terms.

2. Tenneco Automotive: Charlie Munger’s $80 Million Bargain, Part 2 – Tim Isgro

Tenneco produced auto products in two business segments: Emission Control and Ride Control. In both of those business segments, it had brand names with an excellent reputation and market share…

…Moreover, Tenneco’s list of original equipment manufacturers was large, including just about every major auto maker in the world. And the largest automaker (GM) accounted for only 16.6% of the company’s sales, indicating the sales were nicely diversified…

…Munger understood the great reputation of Tenneco’s products, as indicated by his brief comments at the Daily Journal meeting, when he stated:

I kind of knew based on experience how sticky some of that auto secondary market was, and how many old cars needed Monroe shock absorbers.

I think this point is critical to understanding Munger’s willingness to purchase these securities. Since customers loved and needed Tenneco’s products, the company still had fundamental value as an ongoing concern…

… Importantly, and perhaps underappreciated by the market, Tenneco was still in the midst of a major transition in its business. It had gone through five major spinoffs or sales of business units since 1993, it was facing its first recession since that time, and it was coping with all the debt it was saddled with after those spinoffs.

But digging in a bit to the company’s annual and quarterly reports makes it clear that management was keenly focused on right-sizing company expenses and running a more efficient organization…

…So, as of the end of 2000, management expected to generate a total of $92 million of savings by right-sizing its workforce and by adopting more efficient processes and practices.

In fact, it was already becoming evident by Q3 of 2001 that those efforts were working better than expected. Figure 10 below (which is Figure 8 reproduced) shows annualized operating costs (plus DD&A) that were $104mn lower than those for the year 2000. And those lower operating costs boosted EBIT by 32% to $152mn…

…I think these positive points are ultimately what caused Munger to believe that the company’s bonds, around a price of 35, and the company’s stock, around a price of $1.55 per share and a market cap of only $59mn, were way too cheap. For reference, the entire enterprise value of the company was $1.1bn, a figure I arrive at by conservatively assuming that the company’s debt is valued at par (apart from the 11 5/8% bonds, which I value at 35 cents).

I think Munger saw a very difficult financial situation for the company, and he probably acknowledged that a further, prolonged downturn in the economy and/or a group of unfriendly bank lenders could have pushed the company into bankruptcy. And in bankruptcy, in the wrong economic environment, it was quite possible that his debt and equity got wiped out.

The fact that Munger did not invest fully in Tenneco’s equity, which was more likely to be wiped out in bankruptcy, and chose to split his investment between bonds and equity, shows that he realized this was a possibility…

…I think Munger likely reasoned about how a potential bankruptcy might play out, and I think this was the most important point of all, prompting him to make his investment.

If Tenneco was forced into bankruptcy, its lenders would then have to decide on the best course of action that might get them a full recovery on their lending amounts. The total amount of long-term debt outstanding was $989mn plus the $500mn of subordinate bonds which Munger would invest in.

Tenneco’s lenders would rightly ask themselves: How are we best off to recover our $989mn?

1. We could force a liquidation of the business and attempt to be paid in full. That would involve a few years of wind-down work, staggered employee layoffs and plant and equipment sales, along with the severance and interim operating costs that come along with it. Plus, we would also need to engage in a process to sell the valuable Walker and Monroe brands, two of the most valuable assets the company had.

Or…

2. We could effectively realize the value of those brands by recapitalizing the company and operating as usual. One way to do that might be to forgive Tenneco’s debt completely, take an equity stake in the new company without debt, and then sell the equity in the new company to make ourselves whole on the lending amounts…

…We also know that Munger made “$80mn” on the investment. But we don’t know specifically how much he invested in either of the securities…

…Assuming Munger invested somewhere between 25% and 75% in Tenneco’s bonds (and stock), he likely made anywhere from 4.5 times to 7.2 times on his investment in three years, from December 2001 to the time the bonds were called in December 2004 (and when he likely sold his stock as well). Those returns imply an annualized return of 65% to 93%…

…I think there is one clear takeaway from Munger’s Tenneco investment.

When you encounter a company with a quality, in-demand product and/or a great brand, and that company is suffering, look twice. 

3. Systems of Record Won the SaaS Era – Clearinghouses Will Win the Agents Era – Jamin Ball

In financial markets, the clearinghouse sits between different parties that aren’t able to fully trust each other. The clearinghouse verifies / authorizes / settles trades, and ultimately keeps the receipt. Nobody really loves the clearinghouse, but it’s clear it has to exist for the ecosystem to transact.

Now think about where enterprise software is heading. Agents from tons of different vendors, acting autonomously, touching your most critical data, and even in the future spending real money. Some company has to sit in the middle of all that and decide: which agent is cleared to act? On what data? With what limits? And can you prove what happened after the fact? Whoever holds that seat holds incredibly “strategic real estate.” (and every founder I’ve worked with has probably heard me discuss strategic real estate over and over). That’s the clearinghouse.

This may sound counterintuitive, but owning the clearinghouse for agents (given agent companies themselves will want to be the clearinghouse) may create a deeper moat than the one systems of record had. A system of record controlled your data. It kind of controlled your workflows (but not always, oftentimes someone else controlled the workflows, but the data in the system of record was a critical part of the path). The Clearinghouse controls four things: memory (what your agents know), context (what they see and how it’s served), execution (what they’re allowed to do), and governance (who’s allowed to do what, plus the audit trail behind all of it). If migrating off a system of record was painful, migrating off the thing that holds your policies, your permissions, and your entire audit history is probably harder (especially when the agents start to handle more and more of the work). AND – I think these agent companies that become The Clearinghouse will start to look more and more like systems of record in their own right. Data in systems of record were oftentimes transactional data. Data in agent systems of records (ie Clearinghouses) will be agent traces, agent evals, agent telemetry data, agent A/B data, etc

4. Automation’s Asymptote: Part 2 – Abdullah Al-Rezwan

Tom Reed wrote a very good piece last month arguing that we may be pursuing what he calls “Goodhart Singularity”. Reed’s counter to automation doom is disarmingly simple: you cannot get good at solving problems without access to a source of problems, and the only source of most problems is slow, expensive interaction with the real world. Without that contact, the recursive loop produces something far less impressive than advertised. From Reed’s piece:

“The output of the R&D produced by an isolated datacenter of geniuses would be a mere Goodhart Singularity.4 An isolated AI improving itself against benchmarks would only appear to be approaching superintelligence, while actually optimising for eval performance that fails to generalise beyond the lab.”

Why would self-improvement stall outside the lab? Because models get good at what they practice, and for most economically valuable work, there is nothing to practice on. Reed’s most clarifying observation is about what kind of data exists at all:

“For most tasks in the economy, the pretraining corpus contains writing about the task, but not a record of the task itself. This is of course one of many reasons coding has progressed faster than other domains – code is one of the neat cases for which the task itself is almost entirely reducible to its token trace.”

The internet contains commentary and advice in abundance, but the actual steps of closing an M&A deal or deciding which drone prototype to ship were never serialized into tokens. The natural rebuttal is that a sufficiently smart system can simulate whatever data it lacks. Reed is skeptical that simulation is a viable path:

“Consider that almost half of SWE-Bench submissions accepted by AI auto-graders would be rejected by the actual human maintainers of the relevant repositories. The fact that you can pump SWE-bench scores without increasing actual merge rates is, to me, suggestive of the situation the datacenter-genius will find itself in.

The great Zhengdong makes this point about the progress of AI research itself. Not only are “evals” the only things that models are capable of getting good at, but “the researchers [themselves], they just wanna optimise… they just want an important problem to solve, a clear evaluation that measures progress towards it, and then they just wanna optimise it.” I suggest that AI companies need real-world deployment as a source of problems, or else they will have no good targets for optimisation.”

5. Mao’s economic record wasn’t bad, actually – Arnaud Bertrand

One number for you: under Mao, China’s GDP PPP per capita (meaning per person) was multiplied by about 2.5x from just above $400 in the early 1950s to nearly $1,000 in 1978. These figures aren’t from a “communist source”, they’re taken straight from a report by the Congressional Research Service, the research arm of the U.S. Congress…

…This is confirmed in another report by the extremely serious National Bureau of Economic Research (NBER), one of the most prestigious economic research institutions in the U.S., who found in a report entitled “The Economy of People’s Republic of China from 1953” that “the Chinese economy in 1952-1978 grew rather rapidly” with an average annual growth rate of real GDP of 6%. This equates to the overall Chinese economy being multiplied by 5 over the Mao era, which is consistent with China’s GDP per capita nearly tripling since the Chinese population simultaneously increased by 75% during the period (5 divided by 1.75 equals 2.85)…

…The data is overall clear: during the Mao era, China outperformed both its most comparable peers. It grew roughly 25% faster annually than India (5-6.7% vs ~4%) and modestly faster than Indonesia (5-6.7% vs 4.8-4.9%). Which means that whatever criticisms one might make of Mao’s policies, the prevalent Western narrative that he presided over an “economic catastrophe” is demonstrably false. The reality, confirmed by American research institutions, international databases, and comparative studies alike, is that Mao presided over significant economic expansion that exceeded comparable peer nations.

Sure, it wasn’t all plain-sailing, to say the least. For instance during the Great Leap Forward, according to the Penn World Table data, China’s GDP contracted by 20.8% from its 1959 peak to the 1962 trough – a severe three-year recession that took until 1965 to fully recover from. Similarly, at the beginning of the Cultural Revolution, GDP contracted by 5.9% from 1966 to 1968, with back-to-back annual declines of 3.3% and 2.7% before rebounding strongly with 9.9% growth in 1969…

…We shouldn’t dismiss the human toll that the Great Leap Forward inflicted. It remains the most severe policy failures in modern Chinese history, causing genuine excess mortality and widespread suffering. But we shouldn’t exaggerate the catastrophe either: probably the best way to assess mortality rates during the Great Leap Forward is to look at population numbers and reconcile them with birth rate data (which dropped from 37 per thousand in 1959 to just 21 per thousand in 1960…

…But let’s be clear though: the Great Leap Forward was a largely man-made economic catastrophe stemming from disastrous policies that backfired spectacularly. Mao didn’t intend to cause a famine, but his policies – including unrealistic production quotas and the diversion of agricultural labor to backyard steel furnaces – undoubtedly did. He himself acknowledged some responsibility for the disaster, as did the Party officially, with Liu Shaoqi (then Chairman of the PRC) stating at the Seven Thousand Cadres Conference in 1962 that the famine was attributed to “thirty percent natural disasters, seventy percent man-made problems.”…

…Overall, China’s GDP nearly doubled over the entire 10-year period of the Cultural Revolution and the 1969-1975 period at 6.86% annual growth was the fastest sustained growth period during the Mao years, even exceeding the celebrated First Five-Year Plan period (6.53% average annual growth). This really goes against the widespread perception that the Cultural Revolution was an economic disaster comparable to the Great Leap Forward: not only it wasn’t, but China’s economy was actually booming during the period!…

…This is what resolves an oft-discussed paradox (discussed, for instance, by Branko Milanovic here): How could a “thoroughly inefficient system” create the basis for explosive subsequent growth? The answer is that the Mao era, despite its inefficiencies and disasters, created specific tangible foundations – human capital, physical infrastructure, industrial capacity, organizational systems, and transformed property relations – that made the reform era’s success possible.

You couldn’t have had the TVE explosion without the organizational legacy of communes. You couldn’t have absorbed foreign technology without an educated workforce. You couldn’t have rapidly expanded manufacturing without existing industrial infrastructure and millions of workers with basic industrial skills. You couldn’t have sustained 10% growth rates for three decades without the healthcare improvements that gave China a healthy, productive workforce. 

---

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 no vested interest in any company mentioned. Holdings are subject to change at any time. 

We’ve constantly been sharing a list of our recent reads in our weekly emails for The Good Investors.

Do subscribe for our weekly updates through the orange box in the blog (it’s on the side if you’re using a computer, and all the way at the bottom if you’re using mobile) – it’s free!

But since our readership-audience for The Good Investors is wider than our subscriber base, we think sharing the reading list regularly on the blog itself can benefit even more people. The articles we share touch on a wide range of topics, including investing, business, and the world in general. 

Here are the articles for the week ending 14 June 2026:

1. Gas Prices, Stock Bubbles, Grad Advice — And Teaching Personal Finance In School (Transcript here) – Morgan Housel 

I want to start with what is the biggest economic news story of this year: the war in Iran. For most of you listening or watching, the biggest impact that’s had on your life is the rise in gas prices and oil prices. I want to make a very nuanced point here about making predictions about the future, which is so common in economics, and so difficult and humbling.

When the war in Iran first started about three months ago, it was very common among the smartest, most astute, most educated economists, oil analysts, and talking heads to make predictions along these lines: if the Strait of Hormuz is closed for another week or two, you’re not going to see a rise in oil prices — you’re going to see an explosion of oil prices. Not $100 a barrel, but $150, $200, $250. Not $4 gas, but $7, $8, $9 gas, with flights being cancelled. Those predictions have been made for months, and it was always along the lines of “if it stays closed for another week or two, this is going to happen.”

I want to make this point without minimizing what’s happened to gas prices all over the world and what could happen in the future. I don’t want to say, “Look at all these people — they were wrong,” because the price of oil today is about where it was three months ago when the war first started. It surged and then plateaued at this level. That is something almost no one watching this three months ago would have predicted. Virtually everybody, if you had told them we would be three months into this war with the Strait of Hormuz closed, would have said this is going to be a Mad Max scenario in oil. And so far, as I record this, it has not been.

I want to make an important point here without making any predictions about what might happen next — almost the opposite point, about why these kinds of things happen. There is such a long history in economics, in politics, and in any kind of social world that makes predicting what’s going to happen next so hard, even when it seems like the most rational conclusion. It’s so appealing and so easy to make simple predictions: if X happens, Y will be the result. Very appealing, and I think very comforting, because when you make a prediction like that, it gives you — or the person listening to that forecast — a sense of control in a world that is uncertain, if not unpredictable.

I say this with the glory of hindsight and nothing else; I would not have known any of this three months ago. But from my understanding, a lot of why oil has not yet reached those Mad Max levels, despite being three months into the Strait of Hormuz closure, comes down to a few reasons. Number one, the United States is exporting oil and gas like never before, which has taken some of the supply-crunch pressure off. Number two, Saudi Arabia has a series of oil pipelines that have been massively extended and expanded over the last three months — one big pipeline going to the Red Sea has gone from 2 million barrels a day to 7 million barrels a day, taking a lot of pressure off oil that used to go through the Strait of Hormuz. Number three, China has massively decreased the level of its oil imports. And number four, all over the world, we’ve been draining down oil stocks and reserves. Can that last forever? Of course not. I’m not making any predictions about what’s going to happen next.

The point I want to make — in a much broader way that applies to so many more things in the world of money and economics than just the Strait of Hormuz and oil prices — is that it is extremely difficult to know how people are going to adapt and evolve to a change in the economy. It’s very easy to say “if X, then Y,” and it makes a lot of sense and it’s very comforting. It’s much more difficult to say, “If the Strait of Hormuz closes, then people are going to adapt in this way, and this way, and this other way, and therefore we don’t really know what the end result is going to be.” There are so many cases like this, whether it’s housing prices, stock prices, whatever it might be.

I’ll give you one example that was crazy at the time. 2009 was one of the worst years in economic history since the Great Depression — absolutely dreadful, during the financial crisis. Stocks finished up that year. They increased. It’s so easy to say that the second coming of the Great Depression would be bad for the stock market; that’s a very easy prediction to make. It was much more difficult to see how people, prices, and valuations would adapt and respond in that era.

I was thinking about this recently because gas prices in my town went up tremendously in the last three months, but they’ve been about the same for the last two and a half. They exploded at the beginning of the war and then plateaued. Looking into how the global oil market has adapted and evolved — and again, maybe that doesn’t last forever, I’m not making any prediction — it’s so important to have a sense of humility about how complex the global economy is, and about people’s ability to adapt in ways you never saw coming. That’s what makes predictions about what’s going to happen this year, next year, or over the next month so difficult.

The last thing I’ll say about the psychology of making predictions: it is very common that the higher the stakes, the more people are willing to believe forecasts. When the stakes are really high — gas prices could explode so much that you can’t afford your commute, or your flights are cancelled — people are willing to believe anybody who says, “I can tell you what’s going to happen next.” That becomes very appealing. The irony is that when the stakes are that high and things are moving that quickly, that’s when forecasts become the least reliable. The demand for forecasts increases exactly when the forecasts themselves become least reliable, because people are adapting and changing so quickly. That is why there is such a long history of economic forecasts for things that never happened.

2. Avoiding Death on the Yellow Brick Road – Joe Schmidt IV

The Yellow Brick Road is our shorthand for the path the labs are walking, where they’re committing extraordinary resources. The reason the labs are best-suited for problems like code generation, writing, or image-creation is because these problems improve with raw model capability: every dollar spent on pre-training and post-training improves product quality. Meanwhile, the rest of Oz is inhabited by more complex, often vertical problems, that aren’t as simple as giving a business user a horizontal tool with access to standard tools and computer use. The value comes less from the underlying model’s raw capability (though that’s still important!) than from the scaffolding around it that makes the output trustworthy, compliant, and operational inside a specific industry…

…The labs will certainly improve, but I’d argue there are a few ways the rest of Oz can defend themselves over time:

Data and learning flywheels: A lot of what you internalize isn’t in any training set — unwritten industry norms, undocumented standards, the tribal knowledge that lives in practitioners’ heads. None of it is on the public web. No amount of training compute substitutes for being inside the workflows where this knowledge actually lives. There are two flywheels stacked on top of each other here: an across-customer one — patterns that compound as you see more variants of the same problem — and a within-customer one — the why behind specific decisions, the unsaid exceptions, the firm’s own rules of thumb that only surface through real interaction with the system…

…A horizontal agent could in principle build the same learning infrastructure. The reason it doesn’t, beyond pure focus, is UX: capturing this kind of knowledge depends entirely on the workflow surfaces you give the user, and vertical players can shape those surfaces around exactly what their workflow needs to surface. Horizontal tools can’t. Eval sets, labeled outputs, and edge-case taxonomies can compound into a vertical-specific data flywheel which can fuel fine-tuning the next entrant can’t generate without comparable production exposure. Whether this is possible depends on data rights, the volume of production exposure accumulated, and the structure of customer contracts, but pattern recognition accrues regardless.

Managing model variability and complexity: The labs are already routing internally — different model classes for different requests, ensembles under the hood. What they can’t do is route across vendors, or evaluate a competitor’s model for a specific sub-task, or use an open-source fine-tune for the narrow piece where it’s actually best. The Rest of Oz company picks the right model for each sub-task across the entire model market, not just what its parent lab ships. It also does the work nobody wants to do — re-running evals on upgrades, recalibrating prompts for the customer’s edge cases, rolling out without breaking production — every time a new model lands. The labs aren’t doing this on the customer’s behalf; they sell you their next model and tell you to migrate…

…Cost optimization: Running every query through Opus 4.7 is the fastest path to negative gross margins. The best Rest of Oz companies route across tiers of models — frontier models for the hardest tasks, mid-tier for the bulk, smaller custom or fine-tuned models where they’ve earned the right to use them. Some are now post-training their own models on top of that, optimizing them for the narrow slice of work their customer cares about and serving them at a fraction of the cost of a frontier API call…

…Governance: There is considerable value in becoming the control plane for how their customers run AI in that vertical – the place where permissions, auditing, what-the-agent-is-allowed-to-do, and what-the-agent-actually-did all converge. That control plane is built out of use case specific guardrails that look completely different across industries and job types. Because they own the tools, the workflows, and the data the agent touches end-to-end, they can provide deterministic outcomes in ways horizontal tools will struggle to. They are also the entity that absorbs the regulatory complexity for the end buyer — FRCP and bar rules in legal, HIPAA in healthcare, SEC and FINRA in finance, state insurance regulations, and so on. A horizontal player can’t credibly do that without becoming a hundred different verticals at once. CIOs want to have a partner that contractually states they are handling compliance for the agents they are providing.

All of these come back to the same thing: focus. That could be a vertical (insurance, legal, accounting) or a function done deeply (sales, customer support, finance). Either way, the work needs a team that’s heads-down on one customer set — its workflows, its edge cases, its regulations. The labs aren’t built for that. They have to be everywhere, for everyone, which is how they built the Yellow Brick Road in the first place. 

3. Sergey Brin: Where Frontier AI Is Headed | Unscripted Q&A @ AGI House × Google DeepMind (Transcript here) – Rocky Yu and Sergey Brin

Sergey Brin: That’s a great question—what’s next after we hit AGI? Everybody is pretty focused on accelerating the growth in AI right now. You’re right: we started with the web and internet search, went through the mobile generation, which was another big explosion, and now AI is a huge new industry trend. What comes after that? I think if you can answer that, you’ll have a fantastic company on your hands…

…Audience Member: I have two questions. First, now that we talk about superintelligence, and AI can help us drive cars and do office work—what kind of thing do you think only humans can do after superintelligence? Second, 20 years ago Google was famous for connecting people, and now it’s a company focused on AI. So my question is about strategy: what do you think Google’s role will be over the next 20 years?

Sergey Brin: Small questions, I guess—what is humanity’s role in this world, and what is Google going to do for the next 20 years? The definition of intelligence has always shifted with what machines can do versus what people can do. For a long time, chess was the measure of intelligence, and then Deep Blue beat Kasparov in the 1990s. The interesting thing is that people kept playing chess. How many people here know who the top-ranked human chess player is? Anyone can yell the name—I’m assuming it’s Magnus Carlsen, and people bounce up and down. But how many know the top-ranked AI program?

Audience Member: Stockfish?

Sergey Brin: That’s the most popular—is it number one? You don’t think AlphaZero can beat Stockfish? Okay, well, you’re the only one who named the top chess program; let’s point that out. My point is that computers doing things well hasn’t stopped humans from getting better and better at them, getting more recognition, and enjoying them. We’ve adjusted our view over time—it used to be that chess was the intelligent thing, then Go was the intelligent thing, then poetry or painting. I think we’re going to find that AIs can do a whole lot of surprising things, but they also help advance people in doing those things. Since AlphaGo, the game of Go has advanced a lot—the players who played against Lee Sedol became vastly better afterward, and Ke Jie did too after he played AlphaGo. It pushed the state of the art. So people will be able to enjoy and do a lot of things even with AI assistance. As for the 20-year question—I don’t know. I think we should let somebody else ask. That’s a big one.

Audience Member: Do you believe transformers are sufficient for AGI?

Sergey Brin: Great question. I’ve asked myself that a bunch of times. Transformers have been weirdly flexible—we use them for image and video in addition to text, and they’ve exceeded their original capability. To be fair, they’ve also changed along the way: we have sparse transformers and a lot of little details that have shifted, so it’s not exactly the same thing as the transformer paper. If I had to guess whether something close to that could be AGI, I’d say yes—just because they’ve been able to evolve so much. But they are changing; it’s not the exact same thing as the original transformer paper…

…Audience Member (Boris): What’s your perspective on how world models can help reach AGI?

Sergey Brin: World models are basically video models. People talk about AGI pretty broadly. I think of AGI as the idea that the AI can actually improve itself. Other people—and they’re probably more correct—think AGI means the AI can do anything a person can do. Those are two different things. To do anything a person can do, you absolutely need to understand and interact with the physical world. So being able to dream or imagine what’s going to happen in the world if you do something, and to comprehend it, is obviously important. If you’re going to do everything—and that extends to robotics—world models are key. You all have probably had more time to play with our Gemini Omni model than I have, honestly, because I’m deep into the self-improvement game. But we’ve been working on that for a long time, and Omni is the latest version. Omni is also pretty cool because it’s the same Gemini—we train it with all the text and all the other things, exactly the same way. The fact that these converge is amazing. But yes, you need that capability for the ability to interact physically.

4. Blackstone Investors Ask to Pull $4.4 Billion From Private-Credit Fund – Matt Wirz

Investors in Blackstone’s flagship private-credit fund, known as Bcred, asked to redeem 10% of their shares in the second quarter, up from about 8% in the first quarter. That amounted to investors asking for $4.4 billion.

Blackstone will limit redemptions from the $79 billion fund to 5%, a reversal from its strategy in March when it opted to pay the full amount requested. The about-face highlights rising financial strain on managers of large private-credit funds marketed to individual investors who continue to ask for their money back…

…“BCRED remains well capitalized, and repayments [from loans] and inflows have outpaced shares repurchased,” the firm said Thursday. It said the fund’s structure, allowing it to limit redemptions, is a core feature that is meant to trade some liquidity for long-term performance…

…Wealthy individuals piled into private-credit funds—known as business-development companies, or BDCs—which invest in high-interest loans to midsize companies and distribute most of the income they collect to shareholders via dividends. The boom ended this year when investors turned bearish over increasing loan defaults and the potential for future losses from lending to software companies.

The Blackstone fund is the largest of the bunch, surging to a high of $82 billion at the end of 2025, but it is now shrinking, cutting into the fees the firm can collect. 

5. The AI Price War Is Here, Piling Pressure on OpenAI and Anthropic – Bradley Olson and Tina Li

Big companies and startups, chafing at rapidly escalating artificial intelligence costs, are increasingly turning to tools that tap in to cheaper AI models, including some from China. That’s raising pressure on industry leaders OpenAI and Anthropic to lower their prices, a prospect that could hurt their ability to grow into profitable enterprises…

…The ecosystem allows autonomous AI systems, or agents, to use cheap models—including those made by Chinese companies like Alibaba and DeepSeek—for many functions. The agents only tap the most capable versions of OpenAI’s ChatGPT and Anthropic’s Claude for more complex tasks. That can reduce costs for some AI-assisted work by as much as 95%, according to executives using the tools.

“Once we find something that is working well and engineers love, we find ways to make it cost effective,” said Dan Robinson, founder of Detail, a startup that identifies bugs. “There’s really an embarrassment of riches right now coming out of the open source labs.”

Robinson shifted 90% of Detail’s workload from Claude and Google’s Gemini to custom models and GLM, a family of models developed in China…

…OpenAI is considering drastic cuts to the prices it charges AI users, ahead of similar cuts the company expects at Anthropic, The Wall Street Journal reported. The company sees itself as having an advantage in such a scenario because it spent massive sums in the past year to secure access to computing resources at far lower prices than what’s available now…

…Open-source Chinese models have been rising in popularity across American businesses. DeepSeek’s share of AI usage rose from 1% in April to 17% in May on the startup Vercel’s platform, the company said.

On OpenRouter, another startup that processes AI queries, DeepSeek has been the most-used AI company since mid-May. Among their highest-spending customers, open-source token usage grew four times faster than closed-source between fall 2025 and spring 2026, OpenRouter said. The company has also seen more than 500 organizations swap from proprietary to open-source models…

…Anthropic’s recently-released Fable 5 model is more than 50 times more expensive per token than DeepSeek’s V4 Pro, for example.

But the top proprietary models from companies like OpenAI, Anthropic or Google remain four to six months ahead of open-source competitors, researchers say. In some cases that means they can complete a complex task using fewer tokens, equating to a lower total cost…

…Many companies have begun to design their own AI models using open-source alternatives and say they are managing to reduce AI costs. When companies build in-house models and train them with company data, their performance can improve or even exceed the capabilities of frontier AI models, executives say.

---

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). 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 07 June 2026:

1. Most of the Economy Won’t Run on the Best Model – Rihard Jarc

When a company hires an accountant, it does not go out and hire a PhD in pure mathematics to reconcile the ledgers. Not because the PhD couldn’t do it — they obviously could, and probably faster — but because it makes no economic sense. The PhD is overqualified, which is just another way of saying they are too expensive for the value the task produces. The economic output of bookkeeping is capped. There is only so much upside in getting the books done. So you hire the cheapest person who clears the quality bar, and you pocket the difference…

…If you are running a drug-discovery program, you absolutely want the PhD — in fact you want five of them, plus a Nobel laureate consulting on the side. Why? Because the economic output of a single discovery is enormous, almost unbounded…

…This is, I think, exactly how the AI model market is going to bifurcate…

…Today, essentially everyone uses the state-of-the-art (SOTA) model for everything. You want to summarize an email? SOTA model. Classify a support ticket? SOTA model. Extract three fields from an invoice? SOTA model. We do this for one simple reason: the frontier models have only just crossed the threshold of being broadly truly impactful for knowledge work, and when something has only just started working, you reach for the best version of it you can find. You don’t optimize cost on a capability you weren’t sure you had last quarter.

But I believe this is a transitional behavior, not a stable equilibrium…

…We have a rapidly falling price for any given level of capability and frontier that is already shrinking in size in terms of what is actually being deployed, and we have companies burning through their annual token budgets in a matter of months.

As such, I believe that for the overwhelming majority of economically valuable knowledge work, the correct model is not the SOTA model. It’s the cheapest model that clears the task’s quality bar. And as pilots move into full production (which is the stage we are in today) — where you’re suddenly paying for millions or billions of tokens a day instead of running a demo — intelligence-per-dollar becomes the only metric that survives contact with a CFO…

…The sellers of new compute (semis) are only winners in a world of continued high-cadence spending on new compute. And my thesis specifically questions whether that cadence is necessary. So let me lay out the two states the world can be in, because the asymmetry between them is the whole argument.

Scenario 1: Capex falls or stabilizes. If you can squeeze an order of magnitude more useful tokens out of the hardware you already own — because models got smaller, cheaper, more efficient and verticalized — then you no longer need to spend $100bn+ every single year just to stay relevant. In this world, the owners of the installed base win and the sellers of new compute lose. Hyperscaler free cash flow inflects sharply upward, because capex was the one thing suppressing it. Multiples re-rate higher as the cloud business converts from a capex incinerator into a cash machine running largely paid-for, partly-depreciated hardware. And the semis de-rate, because the market finally realizes the upgrade treadmill has slowed.

Scenario 2: Capex stays high — and revenue explodes. This is the Jevons-paradox-on-steroids case. Demand is so strong that hyperscalers do both: they extract enormous output from cheap, long-lived existing hardware and keep buying new gear. Here everyone wins at once — but the hyperscalers win more, because their incremental revenue now lands on a cost base that is partly depreciated and dramatically more efficient per token. Operating leverage goes vertical.

2. Calling the Top – Dirtcheapstocks

Spacex is set to go public next month.

I read the S1 and felt like I was watching “Whose Line is it Anyway?”. You know, the show where everything’s made up and the points don’t matter…

…Spacex is eyeing a ~$1.8 trillion valuation, from the latest reports I’ve seen. SPCX did $18.7B of revenue and generated a net loss of $4.9B in 2025. Free cash flow was severely negative: -$15.8B (adjusting for stock-based comp).

So the business is valued at ~100x revenue, and revenue has been growing at a ~34% CAGR over the last two years. Q1 2026 revenue grew 15% yoy.

The business has never sustained profitability, as evidenced by a $41B accumulated deficit…

…If you pay $1.8T for a business, and want a 10% return, you need it to send you $180B in year one. If it sends you $0 in year 1, you need it to produce $198B in year 2, and every year after that until the end of days. If year 2 also produces $0, you need year 3, and every year beyond that, to produce $218B.

I don’t think it’s likely that SPCX will reach profitability in the next couple years…

…According to ChatGPT, General Motors (in the 1950’s) was the largest company in American history when measured on GAAP revenue as a percent of GDP.

This isn’t a perfect metric, but I think it helps us get a rough feel for how large a company can become as compared to the ecosystem in which it exists.

GM’s revenue was equal to ~2.3% of American GDP. This shouldn’t be surprising as GM had ~50% market share in the second most expensive asset Americans owned…

…Now let’s take this metric and apply it to SPCX.

U.S. GDP is ~$32T today. Historically speaking, it would be difficult for a single business to earn more than $750B in annual revenue.

But SPCX will conquer the world (and Mars), so let’s assume it shatters the record. Maybe SPCX revenue can be 3% of GDP, beating out every business in history by 30%!

That would imply SPCX revenue of $960B. So what kind of profit margin can we expect for this business…

…But let’s say SPCX is a killer business at scale and it can achieve 20% operating margins, and 15% net margins. And let’s say it takes us 10 years to work our way there.

So, at a $1.8T valuation, we need $180B of cash in our pocket this year to generate a 10% return.

If we are unable to earn an cumulative profit above $0 for the next 10 years, then year 11 (and every year after that) needs to pay us $466B!

Alright, so we need $466B of profit in year 11. At 15% net margins, that means we need $3.1T of revenue.

If nominal GDP compounds at 7% for a decade, then GDP will have grown to ~$64T. So, SPCX in year 11, will need to have grown its revenue to ~4.8% of GDP ($3.1T / $64T) – a percentage more than double any company in history.

To get to $3.1T of revenue in 10 years, SPCX will need to grow its top line at 67% annually. The past couple years have shown revenue growth in the 30’s…

Hmm, this is getting difficult.

3. X thread on the difference between HBF (High Bandwidth Flash) and HBM (High Bandwidth Memory) – Eugene Ng

HBF is essentially HBM but with NAND flash dies instead of DRAM. It uses similar 3D stacking and TSV technology, delivering 8-16x higher capacity than HBM in a comparable footprint, while offering similar bandwidth, much lower cost per GB, lower power, and acceptable latency for read-heavy AI inference workloads (e.g., massive model weights, long context windows, and large KV caches)…

…HBF Shines in Inference: AI inference (LLM serving) is dominated by read-heavy, capacity-bound tasks, loading huge models, managing long contexts, and high-throughput batching. HBF excels better than HBM…

…Limitations: HBF has significantly higher latency (~10 µs, roughly 100x slower than HBM), slower write performance, and limited endurance (~100k write/erase cycles), making it unsuitable for frequent updates during training…

…Training vs. Inference Shift: As inference grows faster than training in overall AI compute, hybrid HBM + HBF setups are superior to HBM alone. HBM dominates training, while HBF’s capacity and cost advantages break the “memory wall” for cheaper, higher-throughput inference at scale…

…Bottom line: HBF expands the total AI memory TAM without cannibalising HBM. It creates a new high-value inference tier, making the overall market more competitive, multi-layered, and resilient, which is great for innovation and supply diversity.

4. Project Glasswing: what Mythos showed us – Grant Bourzikas

Mythos Preview is a real step forward, and it’s worth saying that plainly before getting into anything else. We’ve been running models against our code for a while now, and the jump from what was possible with previous general-purpose frontier models to what Mythos Preview does today is not just a refinement of what came before.

It’s a different kind of tool doing a different kind of work, and that makes a clean apples-to-apples comparison to earlier models difficult. So rather than trying to benchmark Mythos Preview against general-purpose frontier models, it’s more useful to describe what it can actually do, and two features that stood out across the work we did with Mythos Preview:

• Exploit chain construction – A real attack rarely uses one bug. It chains several small attack primitives together into a working exploit. For instance, it might turn a use-after-free bug into an arbitrary read and write primitive, hijack the control flow, and use return-oriented programming (ROP) chains to take full control over a system. Mythos Preview can take several of these primitives and reason about how to combine them into a working proof. The reasoning it shows along the way looks like the work of a senior researcher rather than the output of an automated scanner.
• Proof generation – Finding a bug and proving it’s exploitable are two different things, and Mythos Preview can do both. It writes code that would trigger the suspected bug, compiles that code in a scratch environment, and runs it. If the program does what the model expected, that’s the proof. If it doesn’t, the model reads the failure, adjusts its hypothesis, and tries again. The loop matters as much as the bugs it finds, because a suspected flaw without a working proof is speculation, and Mythos Preview closes that gap on its own.

Some of what we describe above is not entirely unique to Mythos Preview. When we ran other frontier models through the same harness, they found a fair number of the same underlying bugs, and in some cases they got further than we expected on the reasoning side too. Where they fell short was at the point of stitching the pieces together. A model would identify an interesting bug, write a thoughtful description of why it mattered, and then stop, leaving the actual chain unfinished and the question of exploitability open.

The Mythos Preview model provided by Anthropic, as part of Project Glasswing, did not have the additional safeguards that are present in generally available models (like Opus 4.7 or GPT-5.5).

Despite this, the model organically pushes back on certain requests – much like the cyber capabilities that made it useful for vulnerability hunting, the model has its own emergent guardrails that sometimes cause it to push back on legitimate security research requests. But as we found, these organic refusals aren’t consistent – the same task, framed differently or presented in a different context, could produce completely different outcomes…

…When we first started AI-assisted vulnerability research last year, our instinct was the obvious one: point a generic coding agent at an arbitrary repository and ask it to discover vulnerabilities. This approach works, in the sense that the model will produce findings, but it doesn’t work in producing meaningful coverage of a real codebase and identifying findings of value…

…Four lessons came out of running the work at scale, and each one pointed to the need for a harness that manages the overall execution:

• Narrow scope produces better findings – Telling the model “Find vulnerabilities in this repository” makes it wander. Telling it “Look for command injection in this specific function, with this trust boundary above it, here’s the architecture document and here’s prior coverage of this area” makes it do something much closer to what a researcher would actually do.
• Adversarial review reduces noise – Adding a second agent between the initial finding and the queue – one with a different prompt, a different model, and no ability to generate its own findings – catches a lot of the noise that the first agent would miss if it just checked its own work. It turns out that putting two agents in deliberate disagreement is way more effective than just telling one agent to be careful.
• Splitting the chain across agents produces better reasoning – Asking “Is this code buggy?” and “Can an attacker actually reach this bug from outside the system?” are two different questions, and the model is better at each one when you ask them separately, because each question is narrower than the combined version.
• Parallel narrow tasks beat one exhaustive agent – Coverage improves when many agents work on tightly scoped questions and we deduplicate the results afterward, rather than asking one agent to be exhaustive.

Each of those observations is about model behavior, and put together they describe something that isn’t a chat interface anymore. It’s a harness that helps you achieve the final outcomes.

5. Open-source agents with frontier advisors: matching frontier performance through training and harness engineering – Fireworks AI

On LAB’s continuous mean-score metric, GLM 5.1 ranks highest among the open-source models we evaluated, at 0.8921 mean score putting it directly alongside frontier: Claude Opus 4.7 at 0.911, GPT-5.5 at 0.892. Kimi K2.6 (0.863) and DeepSeek V4 Pro (0.871) come in just below, both still clearly viable for production legal workloads.

On the LAB all-pass metric, the production-readiness measure, the closed frontier holds a small lead: Opus 4.7 at 14 / 100, GPT-5.5 at 11 / 100, GLM 5.1 at 12 / 100. That gap is where the rest of this post lives; the two interventions we describe below close most of it.

Cost is the headline. GLM 5.1 reaches its 0.8921 mean for $121 across the 100-task run. GPT-5.5’s nearly identical 0.892 costs $560. Claude Opus 4.7’s 0.911 mean and 14 / 100 all-pass runs $954, roughly 8× any open-source candidate.

“The customer ask is no longer ‘how do we get the smartest model on every query.’ It is ‘how do we get frontier-quality outputs on the queries that need them, and a model we control on the queries that don’t.’”…

…A single LLM call is the wrong unit of work for a legal task: reasoning chains run long, citation discipline is unforgiving, and under all-pass grading any missed criterion costs the entire task. To solve the problem, the team built a small, opinionated multi-agent harness with the open-source worker at its core. The configuration is straightforward: open weights at the core, orchestration the team can inspect and tune, and the frontier model invoked as a callable tool rather than a load-bearing dependency.

A frontier advisor as a callable tool. Treating Opus 4.7 as an advisor the worker can call on hard sub-tasks unlocked the cost savings on the harness. The GLM 5.1 worker does the bulk of the reasoning, drafting, and tool calls. There is no external router or orchestrator. The worker pulls the advisor in itself, wherever it needs a second opinion: retrieval, drafting, validation. Across the run, the advisor is invoked just 0.83 times per task on average — sparse-but-targeted use. That captures most of the quality lift of running the frontier end-to-end, at a small fraction of per-query cost, and it gives us a tunable cost/performance knob: dial advisor calls up on complex matters, down on routine ones.

The harness traces show a recognizable pattern. The worker’s turn count rises meaningfully versus a GLM 5.1-only run: the model reaches an uncertain step (typically during validation, occasionally mid-draft), calls the advisor for guidance or review, then resumes the trajectory with additional turns informed by the response. The advisor is doing less of the writing and more of the steering; the worker is doing the rest of the work it would not have known to do on its own. Sparse advisor calls, denser worker activity downstream of them.

The harness moves GLM 5.1 from 12 / 100 all-pass to 18 / 100 — higher than Claude Opus 4.7’s 14 / 100 — at $368 across the 100 tasks, roughly 39% of Opus’s $954 standalone cost (Figure 1). Against Opus the comparison is clean on both axes: −$586, +4 tasks all-pass. Against the GLM-only baseline, the advisor adds +6 tasks all-pass for +$246 — the cost increase is real, but it is the cost of beating Opus while still running the open-source worker at the core.

---

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 no vested interest in any company mentioned. Holdings are subject to change at any time. 

We’ve constantly been sharing a list of our recent reads in our weekly emails for The Good Investors.

Do subscribe for our weekly updates through the orange box in the blog (it’s on the side if you’re using a computer, and all the way at the bottom if you’re using mobile) – it’s free!

But since our readership-audience for The Good Investors is wider than our subscriber base, we think sharing the reading list regularly on the blog itself can benefit even more people. The articles we share touch on a wide range of topics, including investing, business, and the world in general. 

Here are the articles for the week ending 24 May 2026:

1. The end of the resource exponential – Brandon Carl

Financial analysts are currently engaged in a collective exercise in ruler-drawing. By mapping the trajectory of compute spending and GPU sales, they have constructed a future that is essentially a larger version of the present. In this view, the path to artificial intelligence is a matter of pure capital expenditure. If $100 billion buys a certain level of reasoning, then $1 trillion must buy ten times as much. It is an investment thesis built on extrapolation.

History, however, is not linear. In any technological cycle, the most dangerous moment is when the market begins to treat the status quo as a permanent law. Today’s AI logic—that hardware scale is the primary lever—is less a rule of physics and more a temporary workaround for inefficient architecture. As investors calculate the return on ever-larger clusters, they are ignoring a more fundamental lesson: what is built today is rarely what defines tomorrow.

The shift from brute force to elegance is not just likely; it is a mathematical necessity. Much of modern AI is built on transformer architectures that exhibit quadratic complexity. Double the input, and the requirements for compute and memory grow fourfold. Quadratic consumption of any limited resource will eventually consume everything. Efficiency is not an optional optimization; it is a condition for survival…

…This does not mean the end of the massive GPU cluster, but it does mean that algorithmic efficiency, rather than just raw silicon, will increasingly solve resource shortages. As architectural pivots reduce the dependence on brute-force scaling, the “mix” of hardware required by a data centre five years from now will look nothing like the procurement lists of today.

The risk for investors is to over-index on “selling the shortage” based on current constraints. Subsidizing the construction of yesterday’s architecture is a recipe for stranded assets. In the history of technology, the greatest returns have rarely accrued to those who simply bought the most hardware, but to those who understood how the math was changing. Ideas travel much faster than silicon.

2.AI Chip Mania Sows Seeds of Its Own Destruction – James Mackintosh

Memory chips are a perfect example of a highly cyclical industry. Heavy investment is required to build a fabrication plant, or fab. When demand rises, it takes several years for supply to catch up, during which prices and profits jump. Those high profits encourage CEOs to expand supply. And the high fixed costs encourage producers to run fabs at full capacity—even when supply overshoots demand. The cycle turns when excess supply pushes down prices and profits plunge, as they did in 2022-23.

Already the high profitability has encouraged heavy capital spending. Micron is spending $150 billion to build or expand fabs in New York, Idaho and Virginia, and new Korean fabs are opening…

…The risk of a downturn is embedded in Micron’s valuation. Two weeks ago it was the S&P’s third-cheapest stock measured by price to forward earnings, and it’s still at under 10 times, tame for a highflying stock. That doesn’t make it cheap, though. It just means investors recognize that the boom times in memory chips never last.

History shows how this works. In the last cycle Micron stock peaked at the start of 2022, with the forward P/E at just nine times, ahead of a halving in the shares that year. The stock bottomed out and subsequently doubled after the loss was baked into predictions…

…The biggest risk is impossible to quantify: AI technology could become far more efficient in its use of memory, meaning data centers need less of it. Memory stocks took a hit in March when Alphabet researchers published a paper showing dramatic improvements in memory efficiency, but have recovered. Large language models are an immature technology, and engineering improvements for specialized data centers should be expected—but how big they are and when they come is unknowable in advance.

Other risks apply to the whole AI supply chain: Data-center plans may be scaled back, AI uptake prove slower than hoped, or a political backlash may hinder expansion. All are plausible; none are considered that serious by the AI bulls driving stock prices.

A final risk is that supercharged profits attract new rivals to enter the market. For now, that seems unlikely in the superfast memory Micron makes, but it’s already happening with other highly profitable chips used in AI.

3. The toll booths of lending – Michael Fritzell

To manage risks, banks and companies gather information on their counterparties. And one way to do so is to buy data from so-called “credit bureaus”, also known as “credit reporting agencies”.

These credit bureaus gather information on borrowers’ creditworthiness. These include consumers, corporate borrowers, and trade counterparties. The data is then used to support lending decisions, ensuring that each lender is comfortable with their exposures…

…On the corporate side, credit bureaus collect all sorts of data on private businesses: business registration numbers, legal addresses, ownership data, the executive leadership, name changes, etc. And more importantly, they collect data on revenues, profitability, and leverage from public filings, interviews, payment data, etc. They also cooperate with debt collectors to understand whether each business has had payment issues in the past.

All this data then ends up in credit reports, which you can purchase for US$150 each. Historically, these credit bureaus made money by selling credit reports a la carte. But today, the entire industry has moved towards subscriptions that generate much higher-quality, recurring, and sustainable revenue. If you’re an ongoing subscriber, you’ll get alerts if there are any changes to the creditworthiness of any particular counterparty…

…Buyers of corporate credit data tend to be small- and medium-sized enterprises that want to know whether they extend favourable credit terms to their counterparties. Or banks that want to know how to extend credit to. The local Asian credit bureaus have almost impenetrable market positions, as they’ve gathered detailed information on millions of businesses. And the reports can be purchased for very little money, while costing almost nothing to produce. No serious lender would skip a US$50 credit check before extending a half-million loan…

…And because collecting consumer data is sensitive, it is highly regulated and therefore protected. The buyers of credit data tend to be financial institutions that want to know whether to extend a mortgage or consumer loans.

There are clear network effects: in many cases, credit bureaus get data on consumer borrowers from their bank customers, who willingly provide the information in exchange for data on other banks’ borrowers. So the bureaus almost become central exchanges that become difficult to displace.

On the other hand, the heavy regulation also means that pricing power tends to be limited. So it’s a scale business, with significant operating leverage if credit growth for whatever reason starts to accelerate.

And this is the exact bull case for Asia’s credit bureaus: the credit penetration in this part of the world remains low, especially in emerging Asian nations like Indonesia and the Philippines.

4. 18% IRR for 57 Years – Joe Raymond

George Batten founded the Batten Company in New York in 1891. At the time, advertising was mostly about placing ads in newspapers.

In 1919, Barton, Durstine & Osborn emerged, focused more on messaging, copywriting, and persuasion.

The two merged in 1928 to form Batten, Barton, Durstine & Osborn.

Over the next several decades, BBDO became a core player on Madison Avenue, helping large corporations build brands as radio and television expanded their reach.

BBDO International started trading over the counter in 1968…

…As Larry recalls:

“I came to realize advertising was a royalty business. If you had a consumer product, you needed to advertise. And you needed to use an ad agency like BBD&O. I viewed it as a royalty on consumer spending.”…

…He paid less than 8x earnings for a business generating 20% return on equity, growing in the low-double-digits, and yielding 7.5%…

…BBDO grew revenues from $49 million to $155 million from 1969 to 1979 (12% CAGR).

Net income tripled from $4 million to $12 million. Shares outstanding declined from 123 million to 106 million. As a result, EPS quadrupled from 3 cents to 12 cents (15% CAGR).

The P/E multiple ended the period at about the same 7.6x it started.

The stock went from 25 cents in 1969 to 85 cents in 1979 while also paying out 46 cents per share of dividends.

Including dividends, the IRR for his first decade of ownership was 20%…

…EPS over the 11 years from 1979 to 1990 grew from $0.12 to $0.25 (7% CAGR) while paying out a cumulative $0.99 per share of dividends. Not spectacular performance, but not terrible either.

The stock started the decade at $0.85 and finished at $2.73. Thus, Larry had a 10-bagger in his first 20 years of ownership, plus dividends worth nearly 6x his purchase price.

1979 to 1990 was a mediocre stretch for earnings growth. But dividends were consistently paid and the multiple expanded 45% from 7.6x to 11.0x. The result was a 17% IRR for the 11-year period…

…Like many other stocks (and the market averages), 2000 to 2010 represented a “lost decade” for Omnicom shareholders.

The business itself grew at a decent rate–EPS compounded at 8% and $5.48 of cumulative dividends per share were paid.

Counteracting these factors was a 50% reduction in the multiple. 32x in 2000 fell to 15x in 2010. The net result was a 1% IRR for the decade.

Operationally, the 2000s didn’t look that different than the 1970s (8% EPS growth in the former vs 7% in the latter). Yet the 1970s produced a 17% annualized return while the 2000s yielded only 1%.

Such is the power of valuation. The same quality business can deliver wildly different results depending on the price paid. In this case, paying 8x earnings resulted in an annual return of 17% for a decade while paying 32x delivered almost nothing for 10 years….

…BBDO was an ideal buy and hold investment in the 1960s and 1970s.

The economics were attractive (20%+ ROE) and growth prospects solid (decades of global advertising growth ahead). Capital allocation was sensible (small bolt-on acquisitions, share repurchases, and dividends), and the valuation was cheap (sub 10x earnings).

$10,000 invested in 1969 and held through today would be worth $3.2 million, with an additional $1.7 million of dividends received as well.

5. The American Rebellion Against AI Is Gaining Steam – Amrith Ramkumar, Katherine Blunt, and Lindsay Ellis

Delivering a commencement address at the University of Arizona, Schmidt told students the “technological transformation” wrought by artificial intelligence will be “larger, faster and more consequential than what came before.” Like some other graduation speakers mentioning AI, Schmidt was met with a chorus of boos.

In one poll after another in recent weeks, respondents have overwhelmingly voiced concerns about AI, a challenge to claims by industry executives that their technology would gain popularity by improving people’s lives…

…Pollsters and historians say the souring of public opinion is all but unprecedented in its speed. “I don’t think I’ve ever seen something intensify this quickly,” Gregory Ferenstein, who conducted a recent poll with researchers at Stanford University and the University of California, Berkeley, said of the backlash…

…Voters in Festus, Mo., ousted four city council members a week after they approved a $6 billion data center. Dozens of communities in states from Maine to Arizona are trying to ban new data centers. Some 360,000 Americans are in Facebook groups opposed to the facilities, roughly quadruple the number from December, figures from organizations fighting the AI build-out show…

…AI has risen in importance most quickly among 39 political issues studied by polling firm Blue Rose Research in the past year, though it still trails priorities including the economy, immigration and foreign policy…

…But all over the country, community-level organizations have been succeeding in blocking data-center projects. Local opposition blocked or delayed at least 48 projects valued at some $156 billion last year, according to Data Center Watch, an organization tracking the trend. A record of 20 were canceled in the first quarter of the year because of local backlash, figures from climate-media outlet and data provider Heatmap show. Dozens more are currently facing similar obstacles on top of obstructions because of permitting snafus and equipment shortages.

---

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 Meta Platforms (parent of Facebook). 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 17 May 2026:

1. A Government Debt Crisis? – Ben Carlson

One of my favorites is the 1972 Time Magazine cover story:

This sounds like it could have been written today:

Debt service is now the third highest public expense, exceeded only by spending for defense and education; most of the money goes to banks, which are the major buyers of bonds that governments at all levels sell to cover their deficits. Moreover, debt functions as a wrong-way income redistribution device, channeling tax money that is paid in large part by the poor and the middle class into the pockets of wealthy holders of trust accounts or stock in banks.

When this cover was published, government debt was roughly $430 billion.

Today it’s fast approaching $40 trillion in total…

…The Wall Street Journal shows that publicly held debt to GDP is now 100% for the first time since WWII..

…Here’s the trillion dollar question — why have none of the government debt crisis predictions come to fruition?…

There are two big mistakes people make when they predict a catastrophe from U.S. government debt levles:

1. Conflating U.S. government debt with household debt. Government debt is not like a mortgage that needs to be paid back. As long as the economy keep growing, debt levels will likely keep rising.1 Plus, the U.S. government has the ability to print the global reserve currency. You can’t print more dollar bills in your basement.

2. The government’s liabilities are someone else’s assets. Treasuries are bonds owned by pensions, insurance companies, fund managers, and households. It’s the largest, most liquid bond in the world and there isn’t an alternative…

…So what would make me worry about government debt levels?

The biggest risk of large deficits and government spending is inflation…

…Continuously rising interest rates would also be cause for concern…

…Another concern is the fact that interest expenses are becoming a larger share of the government’s budget…

…Interest expenses now exceed the defense budget.

The good news is that interest expense as a percentage of GDP is at 1980s levels.

The bad news is that it has risen like a rocket and rates were a lot higher back then…

…Is there a line in the sand where a government debt crisis automatically kicks in?

No one knows.

2. China’s $3 Trillion of Hidden Bad Debt Prolongs Economic Pain – Bloomberg News

By any measure, Tom Hu should be in default on a $730,000 bank loan for his plastics business in China. He barely brings in enough revenue to pay expenses and can’t cover the debt costs.

Yet rather than calling in the loan, his bank lets him defer payments — keeping him afloat, while avoiding another past-due loan on its books…

…Stories like Hu’s are playing out across China as banks grapple with a growing pile of bad debt. It’s impossible to quantify the true extent of the problem, though most economists say the ratio of bad loans is significantly higher than the 1.5% official rate. One analyst at Absolute Strategy Research in London pegs it at about 10%, which would mean a staggering $3 trillion in loans that should be classified as past due are not. Others say it could be double that amount…

…The apparent stability of the official bad loan rate is all the more surprising given that the economy has experienced a major property collapse and posted the slowest nominal growth outside Covid since the 1970s. In March, China lowered its 2026 growth target to between 4.5% and 5% — its least ambitious goal since 1991.

Regulators have taken note. Despite seemingly strong capital buffers and stable NPL ratios, officials have moved to bolster the nation’s six biggest banks with more than $100 billion in fresh capital…

…The primary culprit for the surge in bad loans is a mountain of credit extended to companies whose earnings are insufficient to cover interest payments. About 10% of listed non-financial firms have failed to cover interest payments from their earnings before interest and tax for three consecutive years, according to Absolute Strategy Research. As a result, the non-performing loan ratio is probably closer to 10% than 1.5%, according to Adam Wolfe, an emerging markets economist at the firm…

…China’s official NPL ratio has always been a bit of a mystery. In good times and bad, it’s rarely wavered much from 1.5%, and most economists say it greatly understates the true stress in the system. The figure captures only loans officially classified as “substandard,” “doubtful,” or “loss.”

In reality, the classification is often a subjective assessment and banks have different internal criteria. A much larger pool of troubled credit remains in the “special mention” — those that may have already become overdue but yet to be categorized as nonperforming — or “normal” categories, thanks to an aggressive use of leniency known as forbearance.

Existing rules stipulate that when repayment on a loan is overdue by more than 90 days and the borrower can’t fully repay the amount, it should be marked as nonperforming.

Economists including Wolfe estimate that about 40% of loans are either eligible or already in some sort of forbearance program, where banks are strongly discouraged from seeking repayment or recognizing losses…

…In other words, rather than cracking down on deadbeat borrowers, China’s banks are encouraged to cut them some slack. Regulators have for years urged the big banks to keep their reported bad loan ratio under 2%, according to people familiar with the guidance.

With the forbearance policy — a legacy of Covid support programs that’s been extended to property developers and other firms — Beijing is signaling its desire to maintain financial stability. It wants to avoid a rash of bank failures that would follow a surge in reported bad credits and company defaults.

A leniency policy for small businesses that was introduced during the pandemic was extended in 2024 to encourage banks to roll over loans for companies enduring temporary difficulties. This policy is effective until late next year, and applies to 9.4 trillion yuan ($1.38 trillion) worth of loans, according to officials.

As a result, banks routinely roll over maturing loans, extend repayment periods, or allow interest to be capitalized to avoid triggering NPL recognition. Local governments also exert pressure on lenders to maintain stability by avoiding cuts to risk classifications on loans tied to sensitive sectors. Those include property developers, local government debt and small businesses in weaker regions, according to a dozen bankers interviewed by Bloomberg News…

…All this leniency comes at a cost. Financial resources are trapped in unprofitable and even inactive firms, hindering banks’ ability to promote growth in healthy businesses. Overall loan growth is slowing significantly after fixed-asset investment experienced an unprecedented contraction last year…

…Chinese banks are also accelerating write-offs and transfers of bad assets. Lenders have disposed of more than 3 trillion yuan of non-performing assets a year since 2020, with the total rising to roughly 3.8 trillion yuan in 2024, the highest on record.

Banks have stepped up transfers of NPL portfolios to asset management companies, which typically hoover up bad assets in China. Still, these firms entrust collection back to the originating banks in many cases, according to people familiar with the matter. The funds used to purchase bad loans largely come from the banks, meaning the risks aren’t fully removed from the financial system.

3. The Inference Shift – Ben Thompson

Specifically, coding with LLMs requires a human in the loop. It’s the human that defines what is to be coded, checks the work, commits the pull request, etc.; it’s not hard to envision a future, however, where all of this is completely handled by machines. This will apply to agentic work broadly: the true power of agents will not be that they do work for humans, but rather that they do work without human involvement at all.

This, by extension, will mean that the likely best approach to solving agentic inference will look a lot different than answer inference. The most important aspect for answer inference is token speed; the most important aspect for agentic inference, however, is memory. Agents need context, state, and history. Some of that will live as active KV cache; some will live in host memory or SSDs; much of it will live in databases, logs, embeddings, and object stores. The important point is that agentic inference will be less about GPUs answering a question and more about the memory hierarchy wrapped around a model.

Critically, this articulation of an agentic-specific memory hierarchy implies a necessary trade-off of speed for capacity. Here’s the thing, though: lower speed isn’t nearly as important a consideration if there isn’t a human in the loop. If an agent is waiting around for a job that is being run overnight, the agent doesn’t know or care about the user experience impact; what is most important is being able to accomplish a task, and if entirely new approaches to memory make that possible, then delays are fine.

Meanwhile, if delays are fine, then all of the focus on pure compute power and high-bandwidth memory seems out of place: if latency isn’t the top priority, then slower and cheaper memory — like traditional DRAM, for example — makes a lot more sense. And if the entire system is mostly waiting on memory, then chips don’t need to be as fast as the cutting edge either. This represents a profound shift in future architectures, but it also doesn’t mean that current architectures are going away:

• Training will continue to matter, and Nvidia’s current architecture, including high-speed compute, large amounts of high-bandwidth memory, and high-speed networking, will likely continue to dominate.
• Answer inference will be a meaningful market, albeit a relatively small one, and speed from chips like Cerebras or Groq (I explained how Nvidia is deploying Groq’s LPUs here) will be very useful.
• Agentic inference will gradually unbundle the GPU, which alternates between stranding high-bandwidth memory (during the prefill process) and stranding compute (during the decode process), in favor of increasingly sophisticated memory hierarchies dominated by high capacity and relatively lower cost memory types, with “good enough” compute; indeed, if anything it will be the speed of CPUs for things like tool use that will matter more than the speed of GPUs…

…To date the invocation of “scaling with compute” has implicitly meant Nvidia bullishness. However, much of Nvidia’s relative advantage to date has been a function of latency: Nvidia chips have fast compute, but keeping that compute busy has required big investments in ever-expanding HBM memory and networking. If latency isn’t the key constraint, however, then Nvidia’s approach seems less worth paying a premium for…

…China, meanwhile, for all of its lack of leading edge compute, has everything it needs for agentic inference: fast-enough (but not leading-edge) GPUs, fast-enough (but not leading-edge) CPUs, DRAM, hard drives, etc. The challenge, of course, is compute for training; it’s also possible that answer inference is more important for national security, at least when it comes to military applications.

4. 50 Learnings from the War in Iran – Tomas Pueyo

Missile and drone launching can be dramatically curtailed, because you can track where they’re launched from and destroy that.

But they’re very hard to fully eliminate. This is the beginning of aerial drone warfare. It suggests it will be super important in the future as an asymmetric weapon: Countries can produce drones in a decentralized way and launch them from many different, constantly changing places.

The other way in which drones and missiles can be intercepted is at the destination. Israel has proven that this can work quite well: Iran has been unable to cause critical damage in the country despite trying over and over again…

…Iran’s entire fleet was destroyed in a matter of days (Ukraine did something similar over the last few years, virtually wiping out Russia’s fleet in the Black Sea).

This marks the end of naval warfare as we know it. Few countries will invest in a full traditional naval force anymore…

…Israel and the US blew up a lot of the command chain, but they couldn’t have done that just with airplanes. They needed intelligence, satellites, cyber penetration, AI, amazing communications, and fast command decisions. Doing all of these steps well and integrating them seamlessly is beyond the capability of most countries today…

…For the first time in history, Israel deployed an Iron Dome system in a foreign country—the UAE—manned by Israeli soldiers. This is unprecedented: Israel defending Arabs against other Muslims!…

…Iran finally executed their biggest threat, which gave them lots of leverage in negotiations: They closed the Strait of Hormuz.

It wasn’t clear that this was a threat they could actually follow through with. But it is. They closed it.

They did so even without air supremacy or a naval force. This is very counterintuitive! It turns out you can use small boats and drones to close a big international highway…

…Although US opponents have more incentives to de-dollarize, one thing is to want it and the other to succeed. The dollar has actually risen during the war, and its position as a reserve currency hasn’t changed.

5. An Ode to Restraint: Lessons from the Tim Cook Legacy! – Aswath Damodaran

If you were to create a profile of Tim Cook, the manager, based upon the choices that he has made at Apple during his tenure as CEO, two very divergent views emerge. To his admirers, his actions on some fronts (initiating dividends, massive stock buybacks, borrowing money) and inaction on other fronts (no big acquisitions, diffidence on AI investments), represent an exercise in discipline and restraint, preserving the company’s crown jewel (the iPhone) and fending off the bankers and consultants, with their false promises. To his critics, and there are quite a few, Cook’s caution has cost Apple its disruptor status, when it could have used its ample cash reserves to buy its way or invest in into almost every new business that has bloomed in the last fifteen years. In fact, they point to chances that Apple has had to buy some of the biggest stars in the market, from Tesla and Netflix more than a decade ago to Anthropic, Mistral and Perplexity in more recent years.

It is impossible to argue that one side is right and the other side wrong, but it is undeniable that both pathways (the restrained pathway that Apple adopted and the more aggressive pathway that it could have taken) include trade offs. It is true that Apple’s restraint has led it to miss out on some of the biggest trends in technology over the last decade, but it has also avoided the overpayment that is so common with high profile acquisitions of big companies. The argument that Apple would be worth a lot more today if it had bought Netflix or Tesla a decade ago falls flat for two reasons. The first is the selection bias in picking two companies that, in hindsight, have emerged as winners, when in fact there were at least a dozen other worse-performing companies that were also on Apple’s radar. The second is the presumption that companies like Tesla or Netflix would have been just as successful, owned by Apple, as they were as stand alone enterprises. The clash of corporate cultures that would have ensued if Apple had bought either Tesla, a company that reinvents its business narrative every few hours, or Netflix, an entity that makes content in quantity with the hope that some it sticks, would have been epic, with the risk that both Apple and its acquired target would have gone down in flames.

More generally, though, the question of whether you want a visionary or a disciplined business builder at the top of a firm is not one that has an easy answer, since it depends on the firm in question. In my work on corporate life cycles, I focus on the management skills that are needed most in a company, based upon where it is the life cycle, and that may help address the choice between vision and restraint:…

…With young companies, vision dominates, as managers work to sway investors, employees and nascent customers that their product or service will find a market. As the vision takes hold, converting it into commercial products and services requires trading off some portions of vision for pragmatism, in the interest of getting the business going. As products and services find demand among customers, business building becomes a key difference-maker, with the grunt work of marketing, production facilities and supply chains coming into play. Assuming that you have made it through these three stages, the trade offs of scaling up come into focus, and as you hit market limits, success depends on being opportunistic in finding new products and markets, but only if they exist. In corporate middle age, pathways to easy growth, especially at scale, become difficult to find, and to the extent that value comes from moats and core products, playing defense against competitors takes priority. Finally, in decline, a phase that no company ever wants to enter, but is inevitable at some point, you need to be willing to shrink a firm, shutting down businesses that no longer deliver value and selling other assets to high bidders.

Given these very divergent management functions, it should come as no surprise that there is no prototype for the perfect CEO, McKinsey and Harvard Business School blueprints notwithstanding.

---

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 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 10 May 2026:

1. Corporate dark arts: when incentives tell you what might be coming $GME $EKSO $VAC $RPD – Andrew Walker

EKSO is a tiny little company; its market cap for most of last year was <$10m. But it’s a perfect case study in the dark arts and why paying attention to them can be profitable. In late November they gave all of their executives’ PSUs that vested only if the company underwent a change of control and the stock was “at least $7.50” per share within the next five years. The stock was trading in the mid-$4s at the time.

I’m not sure I’ve ever seen a single PSU grant that flashes “we are for sale” harder than that grant.

Sure enough, at the end of December EKSO announced a deal to merge with APLD’s cloud business spinoff. A few weeks later, EKSO did a private placement; it will shock you to learn the placement was priced at $8.22/share, above the mark that vested EKSO’s PSUs.

It wasn’t guaranteed that the market would respond positively to EKSO’s merger…. but I’d suggest EKSO’s board and management knew something was in the pipes when they made those grants, and that whatever was coming was likely to excite the market.

As I write this, EKSO is trading at $12/share.

2. OpenAI’s AI Chip Deal With Broadcom Hits $18 Billion Financing Snag – Anissa Gardizy

When OpenAI and chip designer Broadcom announced last fall that they would make custom artificial intelligence chips together, they positioned it as a done deal.

The companies said the deal would bring enough chips online before 2030 to consume 10 gigawatts of power, equivalent to five Hoover Dams’ worth of electricity, in a bid to lessen OpenAI’s costly dependence on Nvidia hardware.

What they didn’t say was that they hadn’t figured out how OpenAI would pay for the project.

Months later, the companies are negotiating an agreement for Broadcom to finance the first phase of chip production, which would consume 1.3 GW of data center capacity and would cost around $18 billion, according to an internal memo and two people involved in the talks. At that rate, the full 10 GW program, code-named Nexus, could cost $180 billion in chip production alone before factoring in data center construction and other costs…

…But the negotiations have run into a potential problem. Broadcom has said it would finance the first phase only if Microsoft agrees to buy roughly 40% of the chips, an OpenAI executive told colleagues in a memo last month. Microsoft would install the chips in its data centers and then rent them back to OpenAI.

A purchase commitment from Microsoft, one of the world’s most creditworthy companies with decades of data center experience, would give Broadcom confidence it would get its money back, said a person involved in the talks.

But Microsoft could choose not to buy OpenAI’s chips, which would change the financing terms for the project, the memo said…

…OpenAI has made a habit of announcing landmark partnerships without ironing out the details. A month before the Broadcom announcement, for instance, OpenAI said Nvidia would provide up to $100 billion in funding, allowing OpenAI to build its own data centers and use Nvidia’s chips to power them. The headline-making deal eventually fizzled, though Nvidia later made a $30 billion equity investment in OpenAI…

…And in January 2025, OpenAI announced Stargate, a joint venture with SoftBank and Oracle to spend $500 billion developing data centers. But the effort floundered as the three sides disagreed over details and lenders balked at backing multibillion-dollar projects tied directly to a company with an unproven business model…

…Despite the risks from Microsoft’s sway, talks between Broadcom and OpenAI have been progressing. Broadcom had long insisted that OpenAI put up one dollar of its own for every dollar Broadcom provided in financing, a typical arrangement to limit the chip vendor’s risks. That requirement had become a sticking point in the talks, according to the memo and an executive involved in the talks.

But Broadcom recently decided to relax that demand and invest more capital up-front than OpenAI, breaking from Broadcom’’s “long-held hard-line requirement,” the OpenAI memo said.

3. The Fertilizer, the Bond Market, and the End of the Country Banker – Dirt Cheap Banks

Chapter 12 farm bankruptcy filings rose 46% in 2025. That followed a 55% rise in 2024. That is the third consecutive annual increase. The Midwest jumped 70%. The Southeast jumped 69%. Montana, of all places, jumped 200%. Pennsylvania jumped 160%. Arkansas led the country in absolute filings — the most this century for the nation’s top rice-producing state. Total farm debt is projected to hit a record $624.7 billion in 2026. The American Farm Bureau Federation surveyed 5,700 farmers and 70% of them said they could not afford all the fertilizer they needed for the spring. The U.S. Department of Agriculture itself — not some doom-pusher on the internet, the actual government department whose job is to make this look fine — projects that 2026 corn will cost roughly $5.00 a bushel to grow and sell for $4.20. Soybeans, $12.27 to grow and $10.30 to sell…

…Nearly 40% more new farm operating loans were opened in Q4 2025 than in Q4 2024. The average operating loan in 2025 was 30% larger than 2024, with maturities running three months longer. Farmers are not borrowing more because they are growing. They are borrowing more because they are bleeding. And the only reason aggregate farm income looks anything like solvent is that the federal government will spend roughly $55 billion this year — $44.3 billion in direct payments, plus crop insurance subsidies, plus the $11 billion Farmer Bridge Assistance Program — propping up an industry that is, in market terms, no longer functional. Strip the subsidies out and 2026 net farm income falls off a cliff that nobody in Washington wants to look over. Agricultural lenders surveyed by the American Bankers Association expect only about 58% of farm borrowers to remain profitable in 2025, down sharply from 78% in 2023. NDSU’s Agricultural Risk Policy Center projects $44 billion in net cash income losses on the 2025-26 crops alone…

…The North Dakota State University agricultural trade modeling team ran the fertilizer scenarios and they are worth your attention because they are the most rigorous public modeling that exists.

Under their “Quick Reopening” case, urea peaks at $782/short ton in June 2026 and eases gradually. Under their central “Contested Transit” case, peak urea hits $784/st in July with prices staying above $700/st through November; fall 2026 prepay urea averages $733/st (56% above pre-crisis); winter fill at $643/st; spring 2027 top-off at $590/st. Add another fifty to eighty dollars per ton for freight and dealer margin to get the actual interior Corn Belt retail price. Under their “Extended Conflict” case, fall prepay climbs to $989/st; winter fill to $945/st; spring 2027 spot prices remain near $791/st. The World Bank’s Commodity Markets Outlook, released April 28, expects global fertilizer prices to rise more than 30% in 2026, with urea closing the year at $675 per ton — nearly 60% above 2025 levels.

For the farmer, this means 2026 is the easy year. Most spring 2026 nitrogen had already been contracted before the Strait closed in February. The real budgeting concern is 2027. American Farm Bureau Federation survey data shows that for every farmer more concerned about fertilizer for 2026, nearly two are more concerned about 2027. Damage to liquefied natural gas production and sulfur output in the Persian Gulf could take years to repair, even if shipping normalizes tomorrow. The infrastructure does not just turn back on.

If the central NDSU scenario plays out, the 2027 crop year sees farmers face fertilizer costs roughly 50% above pre-war levels at exactly the moment their working capital — the cushion that lets them absorb a bad year — has been exhausted by 2025 and 2026. This would be the fourth consecutive year of negative crop margins. Operating loans would grow even larger, even longer. Chapter 12 filings would push past 600 a year. Agricultural bank delinquency rates, currently 1.09% as of July 2025, would climb to 2.5% to 3.5%. Still well below 1985’s peak of 6.7% at agricultural banks, but moving in the wrong direction at speed.

If the extended conflict case plays out — Strait remains contested through 2027, fertilizer at near-1980-level real prices, fifth consecutive year of negative margins — the trajectory accelerates. 2028 starts to look uncomfortably similar to 1984. The structural buffers begin to fail in sequence, not in parallel…

…The American Enterprise Institute has been making the case openly: most farm households receive over half their income from non-farm sources; the agricultural sector’s debt-to-asset ratio is 13.75%; the system can absorb shocks without the level of subsidization currently in place. That argument is not winning yet. But it is being made by serious people in Washington, and it is being made at a moment when every other federal spending priority is under similar pressure. If a debt-ceiling fight or a continuing-resolution fight produces a sequester or a freeze, agricultural subsidies are not exempt. They are politically vulnerable in a way they have not been in a generation.

If subsidies are cut even modestly — say, a 30% reduction from the projected $55 billion to roughly $38 billion — the market-based losses that currently get masked by federal payments become visible all at once. Farm income drops by an amount equivalent to roughly 11% of total receipts. The farms that are barely solvent stop being solvent. The farms that depend most heavily on subsidies — the commercial row-crop operations in the Midwest and Plains, the largest borrowers, the ones holding the biggest loans at the most concentrated agricultural banks — fail in clusters.

If subsidies are cut substantially — back toward the 2024 level of roughly $10 billion — the math becomes cataclysmic. Net farm income outside government payments would fall by roughly $40 billion. The structural protection that has kept the current stress from becoming a 1980s-style crisis disappears. Farmland values, which have so far held in part because farmers can still service their debts, begin to crack. The 220 community banks that the FDIC identifies as having agricultural loan concentrations above 300% of capital become acutely vulnerable.

This scenario is the dark mirror of 1985. In 1985, there were no subsidies of this scale to remove. The crisis happened anyway. In 2027 or 2028, removing the subsidies would be the trigger that closes a system that is currently holding together by their grace alone…

…The 1980s farm crisis killed 205 agricultural banks between 1984 and 1987 — 37.4% of the 548 total bank failures during that window. There were 14,483 FDIC-insured commercial banks in 1984; by 2023 that number had fallen to 4,027 — a 72.19% decline. At the end of 2024 there were approximately 4,050 community banks left in the United States. Roughly 220 of them carry agricultural loan concentrations above 300% of capital, clustered in eight states: Illinois, Iowa, Kansas, Minnesota, Missouri, Nebraska, North Dakota, and South Dakota. Most have under $200 million in assets. Most are not publicly traded.

4. Warren Buffett Case Study – East Sullivan Mines 1962 – Dirt Cheap Stocks

At yearend 1962, the Buffett partnership was managing $9.8 million.

East Sullivan was a $106,000 position.

East Sullivan was a mining business that produced copper, gold, silver and zinc.

It was headquartered in Quebec and formed in 1944.

East Sullivan had profitable operations. In 1962, it produced millions of pounds of zinc and copper along with 4,600 ounces of gold and 168,000 ounces of silver.

In 1962 the business had 33% EBIT margins. 1961 had 20% EBIT margins.

It was a nice little business. Of course, margins would swing wildly in this kind of operation, but still, it was doing well when Buffett owned it.

The business had cash and investments in excess of its market cap. It was profitable and paying a sizable dividend.

East Sullivan’s investments were largely made up of ownership in affiliated companies.

Members of the Beauchamin family made up the majority of the management team and the board.

Then there were a bunch of related businesses that were also interconnected and controlled by the Beauchamin family…

…East Sullivan was doing $1.2mm of EBIT from its own operations.

Let’s assume that the $9.6mm of marketable securities and affiliated businesses could produce a 7% return. That’s probably conservative.

7% on $9.6mm is an additional $672k of look-through ebit.

The market cap was $8.9mm. EV would’ve been $7.8mm if only giving credit for East Sullivan’s cash account.

The look through EBIT is $1.9mm (1.2mm + 672k).

That’s ~4x EV/EBIT…

…We don’t know how long Buffett held. But the investment was likely a good one for him.

Shares touched $3.00 in 1963. By 1964 they were $5.70. And they peaked at $9.40 in 1965.

If Buffett had held to the top in 1965 he would’ve earned a 73% IRR.

If he held through the end of his partnership in 1969, he would’ve earned a 34% IRR.

5. Iran war is crushing Asia’s farmers, threatening global food supply – Rebecca Tan and Wilawan Watcharasakwej

Saithong Jamjai has just finished harvesting the rice on the 19 hectares of farmland she owns in central Thailand and now is the time to sow again. But she won’t, she said, because of the U.S.-Israeli war against Iran.

She has gone over the math for weeks. Because of surging prices, driven by the war, of fuel, fertilizer, plastics and other necessities, planting and harvesting will cost her at least $33,000, she said. The grain that she’ll produce, she estimates, will sell in August for only $22,000.

“A confirmed loss,” Saithong, 53, concluded. She’d rather let her land bake under the yellowing husks from last season…

…Addressing world leaders in Rome on Thursday, Dongyu Qu, the director general of the U.N. Food and Agriculture Organization, said the war had created not only a geopolitical crisis but “a disruption at the core of the global agrifood system.”

Iran’s destruction of gas infrastructure in the Gulf and the dueling U.S.-Iran efforts to choke the Strait of Hormuz have prevented crucial supplies of fuel and its derivatives like urea — a potent source of nitrogen that enhances harvests — from leaving the Middle East. Because fuel infrastructure takes years to build, there is no ready replacement for these supplies.

In effect, 30 percent of the world’s urea has been “wiped out,” said Pranshi Goyal, senior analyst at the market intelligence firm CRU Group. China, a major fertilizer producer, has restricted exports to ensure its farmers have enough. Russia, another big manufacturer, is seeing demand soar, potentially boosting its economy and aiding its war in Ukraine. On what is known as the spot market, urea prices are up 40 percent since February…

…The longer the production plants in the Middle East stay closed, the longer they will take to restart. “This problem builds in a nonlinear fashion,” Goyal said.

So do its repercussions.

In Thailand, the Philippines, Bangladesh and Australia, which are the first since the war to enter key sowing periods, farmers are choosing to skip or reduce planting, or cut fertilizer use, which will lower yield.

As the war stretches deeper into the crop calendar, farmers from more countries will be forced to make similar choices, said Maximo Torero, chief economist for the FAO. “Right now, the impacts are more severe in Asia,” Torero said. “But clearly, this is moving east to west and south to north.”

In June, India and Brazil, two of the world’s biggest agricultural producers, will ramp up orders for urea. If, by then, vessels carrying urea are not sailing, there will be “significant yield loss” across many countries, Torero said…

…Thailand’s Commerce Ministry, for example, said in April the country still has 343,000 tons of urea fertilizer, sufficient to support the upcoming planting season. Driving through the vast flatlands surrounding Thailand’s Chao Phraya River basin, however, reveals a different picture.

Across Ayutthaya and Suphan Buri provinces, fertilizer shops large and small were completely out of urea — and said they had been for weeks. Distributors are offering only Russian compounds that farmers are wary to use, shop owners said. Seansdee Teerasattayaporn, 62, who runs a fertilizer wholesale business, sent a truck to a marketplace frequented by large dealers to try to procure urea but after waiting four days, he said, the truck returned empty.

Heading into planting season, many farmers said they are facing the worst conditions in their lifetimes. Not during the outbreak of the Russia-Ukraine war were shortages or costs this dire, they said. Nor during the pandemic…

…In an interview, Foreign Minister Sihasak Phuangketkeow asserted that Thailand still has sufficient farming supplies and Thai leaders are jetting across the world to procure more. But he acknowledged the country is competing against bigger nations with deeper pockets, amid extraordinary logistical challenges. “We have not faced such a crisis before,” he said.

On Tuesday, two weeks after a trip to Moscow, Thailand’s agricultural minister said an attempt to secure urea from Russia is likely to fall through. Because of shipping disruptions, it would take at least two months for Russian urea to arrive in Thailand — far too late for the current planting season.

Agricultural experts say the Iran war has underlined the need for farmers to become more self-reliant, for example, weaning themselves of diesel by switching to solar power or swapping out chemical fertilizer for organic alternatives that can be produced locally. But to make these switches, farmers need government subsidies and time, both of which are in short supply, said Esther Penunia, secretary general of the Asian Farmers Association…

…Thai farmers have been doubly hurt because the Middle East is also one of their biggest export markets. The region accounted for 17 percent of Thailand’s rice exports in 2025, according to customs data. Iraq was the single largest destination for Thai rice.

The day U.S. and Israeli forces bombed Iran, ship operators at a Bangkok port told sellers to lift containers of rice bound for Gulf countries off ships and back into warehouses, said Chookiat Ophaswongse, president of the Thai Rice Exporters Association. Since then, there have been no shipments of rice to the Gulf. Malaysia and the Philippines have absorbed some of Thailand’s excess supply but not all of it, leaving a glut that has kept rice prices low, Chookiat said.

Even before the war, many Thai farmers were in financially precarious situations, relying on loans to survive from one season to the next. Now, the squeeze of higher planting costs and lower projected rice sales could drive millions of farmers into spiraling debt that will take years to clear, said Pramote Charoensilp, 64, president of the Thai Farmers and Agriculturists Association. 

---

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 Microsoft. Holdings are subject to change at any time. 

We’ve constantly been sharing a list of our recent reads in our weekly emails for The Good Investors.

Do subscribe for our weekly updates through the orange box in the blog (it’s on the side if you’re using a computer, and all the way at the bottom if you’re using mobile) – it’s free!

But since our readership-audience for The Good Investors is wider than our subscriber base, we think sharing the reading list regularly on the blog itself can benefit even more people. The articles we share touch on a wide range of topics, including investing, business, and the world in general. 

Here are the articles for the week ending 03 May 2026:

1. Oracle’s Deluge of AI Debt Pushes Wall Street to the Limit – Peter Rudegeair and Berber Jin

Banks including JPMorgan Chase struggled for months to spread the risk of billions of dollars in loans they made to build data centers leased to Oracle in Texas and Wisconsin, people familiar with the matter said. Many financial institutions that would ordinarily buy those loans face restrictions on how much exposure they can have to a single counterparty, and the sheer size of these debt packages pushed them to the limit with Oracle. As a result, bank balance sheets got clogged, constraining the financing prospects of future projects tied to Oracle and OpenAI.

For example, lenders balked at financing the expansion of a data-center complex in Abilene, Texas, if Oracle were the tenant, according to people familiar with the matter. That led the developer, Crusoe, to lease it to Microsoft instead…

…Lenders grew more comfortable with Oracle-related projects after the company said it would raise all the money it needed for 2026 by issuing roughly $50 billion in stock and bonds. Oracle said in a post on X last week that each data center it is developing for OpenAI is moving forward on time.

But even after it raises that amount, Oracle still has additional cash funding needs of $100 billion or more for 2027 and the first half of 2028, according to Morgan Stanley credit analysts. “We’ve pondered how [Oracle’s] considerable funding needs over the next three years may test the depths of different fixed-income markets,” the analysts wrote in February…

…Oracle, though, is in a comparatively weaker financial position than big tech rivals. It has a lower investment-grade credit rating, more debt and is burning cash. Much of its future revenue is tied to a money-losing startup that is facing growing competitive pressure. The cost of protecting Oracle’s bonds against a potential default via credit-default swaps roughly quadrupled between late September and late March, though it has fallen slightly since then…

…Much of the borrowing tied to the OpenAI megacontract was done by projects involving data center developers working with Oracle. The debt was structured as short-term construction loans meant to be syndicated among a group of banks and other institutions. Oracle is the tenant and OpenAI is the subtenant on the deals, but the debt doesn’t sit on Oracle’s balance sheet.

2. OpenAI Misses Key Revenue, User Targets in High-Stakes Sprint Toward IPO – Berber Jin

Chief Financial Officer Sarah Friar has told other company leaders that she is worried the company might not be able to pay for future computing contracts if revenue doesn’t grow fast enough, according to people familiar with the matter. 

Board directors have also more closely examined the company’s data-center deals in recent months and questioned Chief Executive Sam Altman’s efforts to secure even more computing power despite the business slowdown, the people said…

…OpenAI missed an internal goal of reaching one billion weekly active users for ChatGPT by the end of last year, according to people familiar with the goals. The company still hasn’t announced that milestone, unnerving some investors. It also missed its yearly revenue target for ChatGPT as well after Google’s Gemini saw massive growth late last year and ate into OpenAI’s market share, the people said. The company has also struggled with defection rates among subscribers, according to people familiar with those figures.

OpenAI missed multiple monthly revenue targets earlier this year after losing ground to Anthropic in the coding and enterprise markets, people familiar with its finances said.

3. If AI is so great, why isn’t it working? – Vas M.

AI is working for one group of people right now, at scale, because it’s the group of people that rely the least on business logic. It’s software engineers. The biggest winner from 18 months of AI improvement, by miles, has been engineers writing code in Cursor, Claude Code, Codex, etc. Some stats for you if for some reason you still don’t believe in agentic engineering:

• GitHub’s 2024 study clocked Copilot users at 55% faster on real tasks. 1 hour 11 minutes vs 2 hours 41 minutes on the same work.
• Anthropic ran an internal study in August 2025 across 132 engineers and 100,000 real Claude conversations. AI cuts developer task completion time by roughly 80%.
• Sundar Pichai said at the start of 2026 that 75% of new code at Google is AI-generated and engineer-approved. That number was 30% in April 2025.

Yes, the tools still overpromise on the hard stuff: security review, complex distributed systems, novel debugging. Caveat very real and noted. But the bread-and-butter productivity gain on shipping code is the biggest jump engineering has had since the IDE…

…So why does AI work for engineers and not for any of these? What’s different about engineers? As a former software engineer, engineering work has four properties that basically no other enterprise function has. Yes there are nuances but these are directionally correct, please relax in the comments.

• It’s bounded. A function takes inputs and returns outputs. The scope of “fix this bug” lives inside a file or a module. The dependencies are explicit and importable.
• It’s checkable. Compilers tell you in milliseconds whether the code parses. Tests tell you whether it works. Type systems catch entire classes of error before runtime. Feedback loop: seconds.
• The substrate is structured. Code lives in files, in version control, with a deterministic build pipeline underneath. Same input, same output. You can replay any state.
• The output is verifiable. A pull request is a discrete artifact. A reviewer can look at the diff in 10 minutes and say yes or no.

When you point a capable AI at work that’s bounded, checkable, structured, and verifiable, the leverage is enormous. Cursor and Claude Code are the proof. And if we’re being honest, the biggest reason is that the AI labs (OpenAI, Anthropic, Cursor) poured every single ounce of resources they had into figuring out software engineering. If they can make their own engineers better, they can make the models better, faster, and achieve the ever-elusive “AGI”, which will then make every other task on the planet (Finance, Sales, Operations, Marketing, etc) much easier downstream.

But contrast software engineering with a finance close.

Finance involves AP, AR, intercompany reconciliations, FX, accruals, journal entries, and exception handling that spans NetSuite, Concur, three banks, two ERPs from acquisitions, a custom intake form, and a Slack channel where the controller flags “weird stuff she sees.” The “process” is documented in an SOP that doesn’t match what actually happens. The output is “the close was clean,” which takes two senior accountants two days to verify.

Sales ops involves a CRM, an outbound tool, a calendar, a notes platform, an enrichment vendor, an attribution tool, and a Slack channel where the AE is asking the CRO whether to discount this deal. None of those systems share state cleanly. The process for qualifying a lead is different across reps, even on the same team.

This is what every ops function looks like in every company Varick has ever audited. None of it is bounded, checkable, structured, or verifiable the way code is. And trying to wrangle generic AI to these functions that are incredibly specific to your company and its processes is a fools errand.

Pointing an LLM at this work gives you negative ROI. The operator was doing the work in 30 minutes. Now they’re doing the work in 30 minutes plus another 30 minutes correcting the AI’s mistakes. Most if not every vendors’ “AI for [department]” has the same arc. A nice flashy demo showing how great it works for startups, then a big series A, then quietly killed after it fails to work for enterprise…

…Ok so what does the 5% that ships and stays in production do consistently that makes them so good:

1. They audit before they build. Four weeks (often longer) of mapping the actual workflow before anyone touches a model. The audit produces a digital twin: a live map of how work moves through the org, where the conformance gaps are, what’s pattern-matchable, and what genuinely needs human judgment. The document itself matters less than the alignment it forces between the AI team and the operators. Make sure everyone is aligned on what the bottle-necks are, what the optimal state should be, and what is going to be done to fix it.

2. They decompose the work until most of it is deterministic. LLM goes ONLY where judgment is absolutely required, while plain code goes everywhere else. Most production systems we ship at Varick end up as 5-10 deterministic steps with maybe one or two model calls in specific places. Boring in production is genuinely the goal, and is how we’ve seen the most success.

3. They build a single orchestration layer that sits on top of the existing software stack. At Varick, we call this the single pane of glass. Finance, sales, ops, and engineering agents all live on the same platform, share the same context, and can talk to each other when they need to. Every new use case lands as configuration on top of the platform. In turn, sprawl is dead on arrival.

4. They stay model-agnostic. Abstractions get built at the task level, not at the model level. Each step routes to the best-fit model at any given moment. When OpenAI deprecates a model or Anthropic ships something dramatically better, the routing layer absorbs the change and your workflow keeps running without anyone noticing.

5. They treat the deployment as continuously evolving infrastructure. There is a real team responsible for ongoing tuning, retiring agents that aren’t earning their keep anymore, and shipping improvements every quarter. The deployments that pay off over five years are the ones that get tuned every quarter if not every month, not the ones declared “done” at go-live. You have to get over this fact if you want to succeed with AI. 

4. Software Is Eating the World (But Actually This Time) – Siddharth Ramakrishnan

In 2011, software ate the world. At least that’s what Marc Andreessen told us. But if that’s true, then why does the Bay Area still exist? If software really ate everything, wouldn’t we all have moved to New York or Miami by now?

Well, let’s look at what software actually ate: banks got apps, retail got websites, hospitals got EHR systems, and taxis got dispatched with a few taps instead of a phone call at 2am when you maybe don’t remember exactly where you are.

Software ate the interfaces, but the actual work? That mostly stayed human.

A customer calls about a billing dispute and software routes the call, pulls up the account screen, and then logs the resolution afterward. But here a person is still the one listening, figuring out whether the refund policy applies here, deciding what to do, and actually talking to the customer. A loan officer reviewing an application gets the credit score surfaced by software and the documents pulled up on screen, but they’re the one reading those documents and making the judgment call. For 15 years, software has been really good at the plumbing while humans kept doing the actual work.

Now, AI can actually do the work! A customer service call is becoming an agent loop where the system handles speech recognition, looks up the account via API, pulls the relevant policy, reasons about whether the customer qualifies, triggers the refund, and responds with text-to-speech. An insurance claim is becoming document intake followed by coverage checks, fraud flags, reserve calculations, and settlement workflows, all running as code. A coding task is already 30 rounds of reading files, editing code, running tests, and revising with no human involved at all…

…I think most people dramatically underestimate how much inference these converted workflows actually consume, because they’re picturing one model, one call, one response, and some hallucinations along the way, but the reality is very different.

Take a voice support agent handling something simple but real, like rescheduling a medical appointment. To the customer, it feels like one conversation. Under the hood, it is a small autonomous system running continuously. As the caller speaks, a speech recognition model transcribes audio in real time. An orchestration model then reasons over the transcript, pulls the patient record, checks scheduling constraints, looks up provider availability, decides what to ask next, and calls the relevant tools. Once it has enough information, it synthesizes the result into a response, and a text-to-speech model turns that back into natural audio. In parallel, other models may be monitoring sentiment, checking compliance, or deciding whether the call should be escalated.

The system is doing all the work itself: listening, retrieving, deciding, tool-calling, verifying, and responding in a loop. An 8 minute call might contain only ~3k tokens of raw transcript, but the orchestration layer can easily consume ~40k tokens once you account for repeated reasoning over the growing conversation, retrieved context, and tool outputs, on top of continuous ASR and TTS inference running for the duration of the call. “One AI phone call” is really a multi-model inference stack operating continuously…

…In customer support, a basic FAQ bot in 2023 might have consumed around 3,500 tokens for a ticket, better retrieval pushed that higher, then tool use and reasoning pushed it higher again, and now full voice support stacks are higher still. Coding follows the same pattern, just more violently: what used to be tens of thousands of tokens for a bounded coding task has become hundreds of thousands or even well over a million as agents became capable enough to handle real debugging, refactoring, and multi-file work. Each useful task now justifies much more inference than it did a year or two ago, because the model can actually finish the job.

This is a subtle version of Jevons paradox. The sticker price per token has actually been rising for frontier models, not falling. But the value per million tokens has gone up much faster: a frontier model today can complete a workflow in one coherent session that would have required dozens of brittle attempts a year ago, or simply could not have been done. Effective cost per useful outcome is dropping even as nominal cost per token climbs. And that dynamic is what opens up entirely new categories: complex insurance claims, broad code refactors, long-running research tasks, multi-step back-office processes. These were not meaningfully part of the inference market two years ago because the models could not stay coherent long enough to do them.

The aggregate numbers suggest this is already happening. OpenAI’s API is processing more than 15B tokens per minute as of April 2026, up from 6B half a year earlier. Google went from 9.7T tokens per month to 480T in a year, about 50x growth. OpenAI says reasoning token consumption per enterprise organization grew 320x year over year. Anthropic’s latest reported annualized revenue of $30B (up from $10B to start the year…) speaks for itself, especially given the main driver is Claude Code and their API…

…As models commoditize, the durable application companies will be the ones that see the real work: the tool calls, retries, escalations, corrections, and edge cases that never show up in a benchmark. That is where the system learns how a specific workflow actually runs, and where proprietary context starts to accumulate. Over time, the advantage is not just access to a model. It is knowing how this insurer handles claims, how this hospital works denials, how this codebase breaks, how this finance team closes. The apps that capture that messy operational data will be the ones that improve fastest and defend their position longest.

5. Nike and the Arithmetic of Durability – Andrew Chou

As of April 2026, Nike stock sat below US$45 – a market capitalisation of US$68 billion, its lowest level in over a decade, and a fall of more than 75% from the US$280 billion the company commanded at its 2021 peak.

How does what was once considered one of the widest consumer brand moats in the world, built over half a century, erode over the course of a few short years?

A good starting point is January 2020, when John Donahoe took over as Nike’s new CEO. The board wanted a digital-first operator, and Donahoe had the résumé – ServiceNow, eBay, and Bain – even if he was one of the few leaders in Nike’s history not to have risen through its operating ranks…

…Under Donahoe, Nike began systematically pulling back from these wholesale relationships. The logic was straightforward: move more volume through direct channels, control the brand experience, and capture more margin.

By September 2021, Nike had exited roughly half its retail partners. Big names like Foot Locker, Zappos, Dillard’s, and Big 5 Sporting Goods saw their allocation of the most sought-after models shrink in favour of Nike’s directly owned stores. Gross profit margins expanded immediately.

The vacated shelf space that followed was quickly and eagerly filled by competitors. Adidas, New Balance, Puma, Hoka, On, Brooks, and Salomon—brands that had suddenly found themselves with prime real estate in the stores Nike had walked away from…

…That same model of deep, sport-specific immersion was eventually replicated across basketball, football, tennis, and dozens of other categories. Teams embedded in each discipline accumulated years of insight about athletes, usage patterns, and the fine distinctions that matter in performance products. This kind of expertise accumulates slowly—through proximity to athletes, coaches, biomechanics, and the subtle demands of each sport.

Under Donahoe, Nike restructured around a simpler model: Men’s, Women’s, and Kids. The rationale was familiar—less duplication, cleaner accountability, more consistency across segments—and the resulting redundancies left the org chart looking tidier on paper. Overhead expenses came down immediately.

What it also did was dissolve the sport-by-sport expertise and institutional knowledge accumulated over decades. Product lines that had once been shaped by deep category knowledge were now filtered through broader consumer-demographic lenses…

…Nike has long been famous for marketing that built meaning before it chased sales. The ability to turn a product into a cultural moment was arguably Nike’s most valuable and least replicable asset.

The Banned Air Jordan story is perhaps the purest illustration. In 1984, Michael Jordan wore black-and-red sneakers that violated the NBA’s uniform rules. The league threatened fines. Nike’s response was not to comply—it was to lean in. The company shot a television commercial showing the shoes blacked out by censorship bars, declaring that the league had thrown them out of the game but could not stop you from wearing them. That single ad helped sell 50,000 pairs almost immediately…

…Under the new model, marketing spend shifted from broad, culture-shaping storytelling into programmatic digital advertising designed to drive traffic to Nike’s own e-commerce channels. Performance marketing has direct, measurable KPIs – but by its nature, it harvests existing demand rather than creating it.

Anyone can pay for web traffic, but doing so does not build a competitive advantage. Just ask the direct-to-consumer startups built on performance marketing in the 2010s that failed to sell to a large incumbent with real distribution before the music stopped…

…Nike shares climbed from around $100 when Donahoe took over to an all-time high of $179 in November 2021 – a company valued at roughly $280 billion. The “transformation” was working.

But these gains came from somewhere. They were, in effect, the monetisation of business value painstakingly built over decades: the distribution footprint Knight and his team had cultivated since the 1960s; the product expertise and institutional knowledge that Bowerman’s culture had embedded across dozens of categories; the brand equity that campaigns like the Banned Air Jordan and Just Do It had compounded over generations.

Most business decisions sit on a spectrum between maximising long-term net present value and maximising short-term accounting profit. When the asset being spent is the moat itself, the spending does not show up as a cost. Each of Nike’s three shifts boosted reported profitability immediately and reduced the long-run NPV of the franchise meaningfully. The trajectory of the income statement and the moat moved in opposite directions – but only the income statement was visible quarter to quarter.

---

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). 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 26 April 2026:

1. Pancreatic cancer mRNA vaccine shows lasting results in an early trial – Kaitlin Sullivan, Marina Kopf and Anne Thompson

Nine days later, Gustafson had surgery to remove the Stage 2 cancer from her pancreas. The day before she was supposed to start chemotherapy, her doctors told her about a clinical trial exploring the use of personalized messenger RNA vaccines for cancer. It was February 2020 — months before mRNA vaccines for Covid would become one of the world’s hottest commodities. Very soon after, Gustafson was the first person to get one for pancreatic cancer.

“It was a no-brainer,” Gustafson said of joining the trial. “I knew that statistically, the odds were against me.”

Less than 13% of people diagnosed with pancreatic cancer live for more than five years, making it one of the deadliest cancers. There is no routine screening for pancreatic cancer, such as colonoscopy or mammogram, and symptoms typically don’t show up until the disease is advanced. Once detected, there are few options for treatment. Only about 20% of cases are operable, which is currently required for someone to be eligible to join a pancreatic cancer vaccine trial…

…The vaccines work as a type of so-called immunotherapy, harnessing a person’s immune system to fight cancer cells. The goal is not to eliminate existing tumors, but instead to stamp out lingering, undetected cancer cells, and later any new cells that form before they can cause a recurrence.

…Pancreatic cancer is the poster child for these difficult-to-treat cancers, Balachandran said, and experts have long believed that people with pancreatic cancer could not generate an immune response against tumors. But after nine doses of the personalized vaccine, Gustafson is one of eight people in the 16-person Phase 1 trial who did just that, producing an army of immune cells called T cells that seek out and destroy tumor cells.

“This is one of the hardest cancers to generate any immune response, let alone such a potent one,” Balachandran said.

Balachandran and his team published the results of the Phase 1 clinical trial last year. At the time, the patients, all of whom had early-stage disease before they joined the trial, had only been tracked for just over three years, and it was unclear whether the immune response would last and lead to the patients living longer, he said. New data collected during the trial’s six-year follow-up period shows that it may.

Six years after treatment, Gustafson and six others who responded to the treatment are still alive, along with two of the eight people who did not respond. Two of the responders, including the one who died, had a cancer recurrence; Gustafson’s cancer has not come back.

“The most important finding here is that the people who mount a response to the vaccine live longer than those who do not,” said Dr. William Freed-Pastor, a physician-scientist at Dana-Farber Cancer Institute, who was not involved with the trial. He cautioned, however, that the results come from a very small group of patients. More research is still needed…

…Earlier research tested mRNA vaccines to treat people with advanced cancer, with disappointing results, “so we thought we didn’t have a vaccine that would work,” said Dr. Robert Vonderheide, the president-elect of the American Association for Cancer Research and director of the Abramson Cancer Center at the University of Pennsylvania.

In reality, newer research like this Phase 1 trial suggests the immunotherapy may work in less advanced cancer.

2. Brad Setser on the War in Iran and the Future of the US Dollar (Transcript Here) – Tracy Alloway, Joe Weisenthal, Brad Setser

Tracy Alloway: Why don’t we start with that historic analogy—the 1970s oil shock. Lots of ink is currently being spilled on whether or not that’s the correct parallel for our current crisis. In your view, how much does this particular oil shock resemble that of 50 years ago?

Brad Setser: There’s the obvious parallel in the sense that the 1970s oil shocks—’73 was a function of the Yom Kippur War and the Arab nations’ reactions to it. The second oil shock in 1979 was a function of the Iranian revolution. Same geographic region, but different in the sense that the US and Israel are the instigators, and different in that, so far at least, the magnitude of the shock is not at all comparable. It’s not at all comparable in price terms. In ’73 and then in ’79, oil doubled or tripled, and by the end of the decade oil had gone up six or seven times in dollar terms, less in real terms. We’ve only gone up maybe 50% max from spot oil for Brent and WTI and next month’s future. It’s a little higher for delivery in Asia, but we are not yet at the magnitude of the shock we saw in the 1970s.

The obvious point is that our economy as a whole—for the US and for the world—is a little less oil-dependent, but I wouldn’t push that too far. The main distinction is that we sort of started it—the US and Israel—and we in theory can end it, although we would only end it if Iran finds its own equilibrium that allows other countries’ oil to pass through the strait. At least so far, the market has not anticipated that this will need the same kind of jump in price to balance supply and demand. That could change. If you look at it in terms of physical interruption of the flow of oil, some of your guests have noted we’re similar, maybe even worse. So we’re in this weird world where the physical interruption is bigger but the price reaction is smaller.

Joe Weisenthal: I’m glad you brought this up. You talk to the commodity guys like we do, and they’re saying, “This is crazy, this is the biggest shock ever.” Guys like me—I’m an efficient-markets guy, I just see what’s on the screen, and it looks like it’s not that big of a deal. You be the third-party arbiter here. How do you make sense of the gap between what we see on our screen versus the shortfall in physical barrels—20 million every single day that aren’t coming to the market?

Brad Setser: It’s not quite 20. You’ve got the East—there’s been some rerouting. It’s somewhere between 10 and 15, which happens to be between 10 and 15% of global supply and between 20 and 30% of global traded oil. It is still a massive, massive shock, and my elasticities would imply a much bigger increase in price if that was a sustained, expected interruption.

You end up dealing with the reality that oil is close to being a perfectly fungible commodity, but it is not a perfectly fungible commodity. A North Atlantic barrel can only get to China or Japan with a long trek around the world, so there’s an extra shipping cost. A lot of the barrels in the North Atlantic are sweet and light—”light” is a measure of the weight of the oil, “sweet” means less sulfur. A lot of the refiners in Asia were set up to refine medium sour. For some things you want heavier grades of oil because you get more diesel out of the heavier grades. Refiners are configured for different grades of oil. When you interrupt the flow—fundamentally the flow from the Gulf countries to Asia—there’s no immediate, instantaneous substitution using barrels from the North Atlantic. That’s the first point.

The second point is that what people think of as traded oil is not actually oil for delivery tomorrow. It is the futures contract for the next month, and the month after that, the world could look completely different. The US has within its ability the capacity to pull back. If the US pulls back—and maybe the Iranians insist on a toll—there is no shortage of oil that could come out. It’ll take a little longer now because of the physical destruction of some of the export facilities in the Gulf, but if you don’t have this particular choke point strangled, the old global oil market was very well supplied. So the futures market has to balance between one possibility—that there is plenty of oil two or three months out and oil is on a trajectory, not immediately because of the damage, back to $60—and another possibility where this persists and oil is at $150 or above. The market’s had trouble figuring that one out…

…Joe Weisenthal: There are obviously differences, but how did the ’70s reshape the world? You had these oil shocks, and people then started talking about “petrodollars”—a word that came into existence. What kind of legacy did those shocks leave on the global financial system?…

…Brad Setser:  Americans are very unhappy—if you remember in the 1970s it was not good for President Carter. When the Iranian revolution came, there were the hostages, but the oil shock did not help his popularity. Americans in general are very unhappy when oil prices are high—it’s one of our national quirks.

In the short run at that time, there was a huge windfall into the Gulf states. The Gulf states piled up dollars, and they were dollars. Most oil—oil was priced in dollars before 1973. It didn’t take a deal to price oil in dollars. The US had been the biggest producer of oil in the 1930s. We were the supplier of oil to the Brits and others during World War II. It was only over the course of the 1950s and ’60s that other parts of the world caught up with US oil production, but the oil industry, in a deep sense, was born in the United States and was always priced in dollars. Saudi Aramco was originally a joint venture with an American company—or maybe even fully owned by an American company, I forget—so it was natural that it was priced in dollars. It wasn’t like in the 1970s you had to do a new deal to price oil in dollars rather than something else. Oil was in dollars.

Those dollars piled up, and it was a period of difficulty in the international monetary system. The US was going off the gold standard; Bretton Woods was breaking down; high inflation was not well contained after the first oil shock. There was an effort to convince the Saudis to keep their large stock of new petrodollars in dollars—not buy euros—and to use them at least in part to buy Treasuries. Even then, the Saudis were a little reluctant to visibly buy Treasuries. Some Bloomberg reporters several years ago went through this history, and the US started masking who was buying Treasuries at the request of the Saudis. The Saudis essentially said, “You guys are supporting Israel, we don’t really want to be seen buying your bonds directly, can you hide it?” And we agreed. Because there was still residual tension between the US and many parts of the Arab world, a lot of the dollars did not flow into the Treasury market. They flowed into bank accounts in London—offshored effectively eurodollars originating from petro-states. Those got recycled and lent in no small part to oil-importing emerging economies, and that is viewed as the start of the buildup of vulnerabilities that led to the Latin American debt crisis in the 1980s.

There’s another part of this whole story that I think people forget, which is sort of irritating me lately. After 1979–80, the Saudis had built up huge stocks of dollars—a great decade for the Saudis in the 1970s. In the ’80s, in order to keep prices high they had to cut production, and eventually that wasn’t enough and the oil price collapsed. By the end of the 1980s, and certainly by the middle of the 1990s, all the dollars that had been built up in the 1970s had been spent. The Saudi cumulative current account balance went back to basically being neutral or in deficit by ’95 and certainly by 2000. So in some sense the petrodollar boom came and it went. By the time of the Asian financial crisis, oil prices were very low—in the $20s—and there were no flows of petrodollars nor a very large stock of petrodollars. There’s sometimes a tendency to think the ’70s just continued and continued, but the reality is that, setting aside the really rich Emirates and Kuwait, the rest of the oil exporters were not in a position to continuously build up and save over most of the period after 1980 until the big run-up in oil from 2003 to 2014…

…Brad Setser: The last point, and this is just to be provocative because I’m tired of people blabbering about the dollar as the global reserve currency and how that’s the foundation of everything: an international large-cap equity portfolio will have a US share of roughly two-thirds—65 to 70%. The Saudi Public Investment Fund—my friend Alex Etra has done some work on it—its international portfolio has a dollar share of 80%, and that’s probably typical, because most private equity funds are going to be pretty dollar-heavy. A typical global reserve portfolio is now at 57% dollars. So the notion that reserves are the source of inflows into dollars is a bit dated. A reserve portfolio will typically have a lower dollar share than a standard return-seeking equities fund, which just because of the outperformance of US large caps will be more overweight dollars…

…Brad Setser:  a quarter of global reserves, to the first approximation, are in China. China still manages its currency against the dollar, but China as a matter of policy brought its formal disclosed dollar reserve share down to 55%, from 79% in 2005. They did not like the optics of financing their strategic rival and holding a lot of Treasuries in visible ways. That’s a bit misleading, because the dollar share of the portfolios of the state banks—which now have a very large share of the total state portfolio—is much higher, around 70%. If you actually net out the offshore liabilities of the state banks and just look at the net, the euro offshore portfolio is matched by euro offshore liabilities. The dollar offshore portfolio is matched by dollars onshore. In a sense, the BOP flow through the state banks was, setting aside some of the CNY lending which has gone up, almost 100% dollars…

…Brad Setser: Now, we are in a world where an enormous share of the world’s financial wealth—both people looking for safety in reserve assets, people looking for a bit more yield than you can get out of a safe G10 government bond, the private credit/CLO world, and people wanting the equity home runs—all those investors globally are now quite overweight US assets. As a result, the dollar is quite strong. To me, the core question is not really whether geopolitics will change things, assuming we don’t get into a full-on blow-up with Europe, which would accelerate some shifts. The real question is: is this intense overweight in the dollar sustainable when we have fairly reckless policies? The answer so far has been yes.

3. Token Cost Conundrums – Abdullah Al-Rezwan

Each model has its own tokenizer that decides how many tokens your prompt becomes. Feed the exact same prompt to GPT-5.4 and Claude Opus 4.7, and Claude might slice it into 2–3x as many pieces. So even if the headline price were exactly the same, you’d pay 2–3x more for identical content…

…”We sent identical inputs through each provider’s official token counting API and normalized against OpenAI’s…

…”The differences are dramatic. On tool-heavy workloads, claude-opus-4-7 costs 5.3x more than gpt-5.4 even though their list prices are only 2x apart. The rankings also flip depending on what you’re sending: Gemini is the cheapest option for text and structured data, but becomes 46% more expensive than OpenAI on tool definitions.

The only way to know what you’re actually paying is to measure it.”…

…Similarly, after understanding these nuances, I think any enterprise would be really imprudent to standardize on just one model developer. This is because the customer loses bargaining power, a benchmark, and the ability to distinguish real quality differences from billing artifacts. If the seller controls both the meter and the service, and the buyer has no parallel benchmark, the buyer is highly likely to end up paying more over the long term. Even if the model developer isn’t sneakily charging you higher price, without any benchmark, how will the customer press the model developer to lower their price or even understand that they’re paying too high a price?…

…Nonetheless, the smart move does seem to be multi-model capability (even if 95% of volume goes to one vendor) plus internal benchmarks run on your actual prompts. That gives you the optionality to switch and more importantly, the negotiating leverage to push back at contract renewal. Given this context, I believe it will be exceptionally unlikely that enterprise AI will ever be dominated by one model developer. Anthropic may be dominating enterprise AI today, but OpenAI and Google will also likely have plenty of opportunities to gain further ground.

4. Elite law firm Sullivan & Cromwell admits to AI ‘hallucinations’ – Sujeet Indap and Kaye Wiggins

Sullivan & Cromwell told a US federal bankruptcy court that a major filing it made in a high-profile case contained multiple “hallucinations” made by AI software…

…The case in question revolves around S&C’s representation of liquidators appointed by legal authorities in the British Virgin Islands who are pursuing actions against Prince Group and its owner Chen Zhi.

US federal prosecutors last year charged Zhi with wire fraud and money laundering, accusing him of “directing Prince Group’s operation of forced-labour scam compounds across Cambodia . . . that stole billions of dollars from victims in the United States and around the world”.

In a separate action, US prosecutors also filed a civil forfeiture complaint seeking to seize nearly $9bn worth of bitcoin that the US authorities said represented the proceeds of the Prince Group crimes. Zhi was arrested earlier this year in Cambodia and extradited to China after a request from Beijing.

Prince Group is incorporated in the British Virgin Islands and the Chapter 15 proceeding in the US court system is designed to get the US government to formally recognise the powers of the BVI liquidators to represent creditors and victims in the US legal proceedings, liquidators told the court.

In multiple instances, S&C in the April 9 filing erroneously summarised the conclusions made in other cases, according to a list of strike-through corrections the firm submitted to the judge.

S&C has an enterprise licence for ChatGPT according to multiple people familiar with the firm’s operations. According to S&C’s website, at least five high-level partners have been assigned to the Prince Group bankruptcy case.

5. Anthropic’s Mythos Model Is Being Accessed by Unauthorized Users – Rachel Metz

A handful of users in a private online forum gained access to Mythos on the same day that Anthropic first announced a plan to release the model to a limited number of companies for testing purposes, said the person, who asked not to be named for fear of reprisal. The group has been using Mythos regularly since then, though not for cybersecurity purposes, said the person, who corroborated the account with screenshots and a live demonstration of the model.

Anthropic has said Mythos is capable of identifying and exploiting vulnerabilities “in every major operating system and every major web browser when directed by a user to do so.” As a result, the company has taken pains to ensure that the technology is only available to a select batch of software providers through an initiative called Project Glasswing, with the goal of allowing those firms to test and safeguard their own systems from potential cyberattacks…

…The users relied on a mix of tactics to get into Mythos. These included using access the person had as a worker at a third-party contractor for Anthropic and trying commonly used internet sleuthing tools often employed by cybersecurity researchers, the person said. The users are part of a private Discord channel that focuses on hunting for information about unreleased models, including by using bots to scour for details that Anthropic and others have posted on unsecured websites such as GitHub…

…The group is interested in playing around with new models, not wreaking havoc with them, the person said. The group has not run cybersecurity-related prompts on the Mythos model, the person said, preferring instead to try tasks like building simple websites in an attempt to avoid detection by Anthropic.

---

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). Holdings are subject to change at any time.

We’ve constantly been sharing a list of our recent reads in our weekly emails for The Good Investors.

Do subscribe for our weekly updates through the orange box in the blog (it’s on the side if you’re using a computer, and all the way at the bottom if you’re using mobile) – it’s free!

But since our readership-audience for The Good Investors is wider than our subscriber base, we think sharing the reading list regularly on the blog itself can benefit even more people. The articles we share touch on a wide range of topics, including investing, business, and the world in general. 

Here are the articles for the week ending 19 April 2026:

1. A Bakery, a Fortress, and Three Fired Central Bankers – Thomas Chua

Between 1991 and 1995, Croatia fought for independence as Yugoslavia dissolved. At its core, it was a war between a Croatian state seeking independence and Serbia wanting all territories where Serbs lived to be under Serbian control. Serbs were roughly 12% of Croatia’s population, but backed by the Yugoslav army, they pushed for roughly one third of the land.

An estimated 250,000 to 300,000 Croats were expelled from their homes, their houses looted or destroyed…

…But of all the stories I heard across Croatia, the most impactful came from our guide in Trogir.

Her grandmother believed one of her sons (the tour guide’s uncle) had been killed in the war. Heartbroken, this woman, living in a rural village, took her entire life savings and set out to find her son’s body so she could bring him home for a proper burial.

She couldn’t find him.

Eventually, she walked into a bakery and asked if anyone had seen her son’s body. They said no. She placed all her life savings on the table and told them: this is yours if you can find my son’s body. Please let me know.

The people at the bakery refused the money and said they would help, but not for the money. The grandmother left it on the table regardless.

Months later, her son came home. Alive. With her life savings in his hand. The bakery had found him and passed the money back.

In the middle of a war where Croats and Serbs were killing each other, where homes were being bombed and families torn apart, the people at that bakery who helped this grieving mother find her son were Serbs.

Not everyone supports the war. There can still be kindness across enemy lines…

…The tour guides all shared something similar. The pain never fully goes away, even if their rational minds tell them to let bygones be bygones. But they all said the same thing about the next generation: the children don’t carry the same weight. And that gives them hope that pain from the war will heal…

…I sat down at a casual spot and ordered a kebab. Nothing fancy. The bill came to 300 lira. I checked the Google reviews for the same place, and photos from a few years back showed kebab prices around 25 to 35 lira. That’s not a typo. Prices here change so fast that some of the menus had white stickers plastered over the old prices, one layer on top of another. Some restaurants had just given up on the lira entirely and started quoting in euros instead.

Our tour guide shared how prices had spiralled out of control over the past few years, and how the government is almost certainly underreporting the real inflation rate. The official numbers are bad enough.

Turkey’s official annual inflation rate was around 20% in 2021. By October 2022, it had hit 85%. It’s come down since, to around 31% as of March 2026, but independent analysts believe the real numbers are significantly higher.

Meanwhile, the Turkish lira went from about 8 per US dollar in early 2021 to around 44 per dollar today. That’s over 80% of its value gone in five years…

…How did this happen? President Erdogan holds an unconventional economic belief: that high interest rates cause inflation, not the other way around. This is the opposite of mainstream economics, where central banks raise rates to cool an overheating economy. Erdogan has called himself an “enemy of interest rates” and has also cited Islamic beliefs against usury as part of his reasoning…

…Between 2019 and 2021, Erdogan fired three central bank governors in roughly two years. The most dramatic was in March 2021, when he sacked Governor Naci Agbal just two days after the bank hiked interest rates to 19% to curb inflation. Agbal had been on the job less than five months and had been winning investor confidence. His replacement did exactly what Erdogan wanted: slashed rates from 19% down to 14%. The lira lost 44% of its value in 2021 alone.

And they kept cutting. By late 2022, the central bank had pushed rates down to 9%, even as inflation was running above 80%. The lira went into freefall. Ordinary Turks watched their purchasing power evaporate.

After winning re-election in 2023, Erdogan quietly reversed course. A new economic team was brought in and interest rates were hiked aggressively, eventually reaching 50% by March 2024. It was an implicit admission that the previous policy had failed, though Erdogan has never said so publicly.

The lesson is straightforward: when the central bank loses its independence, the consequences are severe and they fall hardest on ordinary people. A president who fires central bankers for doing their job, who replaces them with loyalists willing to cut rates into the teeth of 80% inflation, isn’t just making a policy error. He’s destroying the institutional credibility that takes decades to build and years to repair.

2. The coming El Niño of 2026 – Michael Fritzell

But first, let me explain what El Niño is. It’s essentially a climate pattern that drives global temperatures to rise, leading to droughts across Asia and Africa.

In normal years, winds blow from the eastern Pacific Ocean near South America to the western Pacific Ocean near Asia. These winds push warm water towards Asia. In normal years, this warm water causes clouds to form and rain to fall in Asia.

And since the warm water moves away from South America, the remaining water close to South America tends to be cool.

The so-called El Niño weather cycle disrupts this pattern. Instead of winds moving west, the warm water stays in the middle of the Pacific, or even moves east.

This causes:

• Less rainfall in Asia, leading to droughts in Australia, Southeast Asia and even parts of Africa
• More rainfall in the Southern United States and South America, leading to flooding in those regions…
• …The US National Oceanic and Atmospheric Administration gives a 61% chance of El Niño emerging by July 2026.
• Roughly half of the team at the European Centre for Medium-Range Weather Forecasts expect temperatures in the main El Niño region in the Pacific Ocean to exceed 2.5 degrees Celsius above the seasonal average by October 2026. Making it one of the most intense El Niños of the past century..

…First, droughts will negatively impact palm oil yields for Malaysian and Indonesian plantation companies, perhaps by as much as 10-20%. That’s how much output was impacted by the unusually strong El Niño of 1997…

…Droughts in Asia tend to reduce hydroelectric output, boosting the demand for coal in India and Indonesia. So coal prices could be heading higher, all else equal. And Indonesian coal miners stand to benefit…

…There have been a few instances, such as 2017, when key weather agencies forecasted an El Niño, yet none materialised.

However, I think there’s an asymmetry here, given that investors are not yet prepared for the potential of a super-El Niño, which could rival the one we saw in 1997.

3. China shock 2.0: the flood of high-tech goods that will change the world – Ryan McMorrow, Sam Fleming, Peter Foster, and Joe Leahy

Twenty years ago the global economy was shaken by a first “China shock” as a wave of low-cost goods destroyed the business models of manufacturers in advanced economies, displacing millions of workers and feeding discontent that fuelled populist politicians including US President Donald Trump.

Now a second shock is under way — one that is even more threatening to China’s trading partners: an assault on high-end manufacturing.

Vicious domestic competition, coupled with vast industrial scale, ample pools of engineering talent and some of the highest subsidies in the world, has generated world-beating Chinese champions in EVs, solar panels, batteries, wind turbines and a lengthening list of advanced manufacturing sectors…

…After racking up a record trade surplus in goods that surpassed $1tn in 2025, China boosted exports by nearly 15 per cent year on year in the first three months of 2026…

…BYD, the world’s largest EV maker, saw its average selling price per car fall from Rmb143,100 in 2021 to Rmb119,223 last year. Nio, one of China’s premium EV brands, has lowered the price of its flagship ES8 SUV by about 20 per cent since its 2018 debut, despite packing much more technology into the car.

Chief executive William Li says cutting costs has been a focus as they have redesigned the car. “For the first-generation ES8, the vehicle structure used 97.4 per cent aluminium, which was very expensive,” he says. “Today, we can achieve the same strength with less aluminium.”

Li adds that the group has brought the manufacture of components such as semiconductors in-house and localised the sourcing of parts such as the air suspension, which was once imported from Germany…

…“There is an ideological hardwiring at the top of the Chinese hierarchy to favour production over consumption,” says Daleep Singh, a former White House adviser under Joe Biden who is now chief global economist at PGIM, the asset management group.

“China will continue to rely on the rest of the world to absorb their excess production because the domestic political cost of empowering their own consumers is too high.”…

…The surge in Chinese exports in the first three months of 2026 was driven by shipments to the EU, up 21.1 per cent, and to south-east Asia, up 20.5 per cent year on year — even as exports to the US fell…

…A further, critical factor is the Chinese currency. Lower inflation relative to Chinese trading partners has led to a real exchange rate devaluation in the past three years, helping boost net exports and the current account surplus, which stood at 3.7 per cent of GDP last year.

The IMF estimates the country’s real effective exchange rate — which measures the real value of the currency against a basket of competitors — is undervalued by around 16 per cent, fuelling the competitive advantage enjoyed by Chinese exporters.

China has kept exports competitive by buying dollars and depreciating the currency, accumulating “shadow reserves” through a complex web of state-owned banks.

Then, crucially, there is Beijing’s industrial policy.

China has a ream of policies to help companies get off the ground, with local governments in particular battling with each other to offer the best subsidies, cheap land, financing and tax breaks to lure in manufacturers and seed new industries on their turf.

The competition between localities can be so great that some businesses move from one place to the next as they chase subsidies and investment. They have become known as “migratory bird enterprises”…

…The way the Chinese system works, local officials have every incentive to protect their companies.

Value added tax generates nearly 40 per cent of China’s tax revenue, and the central government splits the receipts with the localities where products are made, giving them a direct stake in keeping factories running.

Adding local production capacity also creates the growth that officials are largely judged on, and any large-scale lay-off could threaten social stability, Beijing’s overriding priority.

“Officials are scared of missing their GDP targets. Nobody is scared of overcapacity,” says another founder, who asks to remain unnamed. “As long as you’re manufacturing, there’s VAT revenue. Whether you sell [a product] or make a profit, that doesn’t really affect them.”…

…Recent OECD analysis underscores the role of subsidies. Company-level analysis of Chinese industry by the 38-member organisation estimates that Chinese businesses are subsidised at between three and nine times the rate of their rich-world counterparts.

As well as grants and tax breaks, the OECD data finds that the biggest subsidies come in the form of loans from Chinese state banks offering below-market rates to Chinese companies that undercut international competition.

While such dynamics have helped Chinese groups dominate globally, profits are vanishing. In the solar industry, overcapacity has led to vast losses, which China’s top six publicly traded solar groups indicated would cumulatively total Rmb43bn for 2025.

Yet the subsidies continue. One of those six companies, Jinko Solar, received Rmb1.3bn in subsidies in the first half of 2025 but still lost Rmb3bn in the period…

…As Chinese factories rushed into solar, production capacity skyrocketed. The country has the ability to manufacture 1,200GW of solar panels annually, roughly double the 647GW installed worldwide last year, according to the China Photovoltaic Industry Association and energy think-tank Ember.

“Why was it possible to build capacity exceeding global demand by double in such a short time?” asked Li Dongsheng, the chair of television and solar conglomerate TCL. “The key reason is the distortion of resource allocation and inappropriate local government participation,” he said in an interview with local media last month.

4. Corporate dark arts gone awry: how executive incentives can destroy shareholder value $NNBR $GME $HAIN – Andrew Walker

A comp scheme that could encourage management to destroy value to maximize their own payout.

Gamestop (GME) serves as a perfect example here. In January, they gave their CEO a huge option package: the CEO got >171m options struck at $20.66/share (the stock’s closing price). The options don’t expire for 10 years, and they only vest if the company hits certain market cap and EBITDA targets…

…You can certainly see the logic behind the award: GME’s market cap is <$10B, and their 2026 EBITDA was ~$345m. This comp package is encouraging massive market cap and EBITDA growth in order to even begin vesting.

Corporate governance ninjas can probably already see the issue with this package: it encourages any growth in market cap and EBITDA, not per share numbers. That incentive carries a host of issues. To take it to the most extreme: the CEO could easily hit all of his targets by issuing stock like a wild man in order to boost the company’s market cap. He could then take all of that cash and go on an acquisition spree in order to drive the company’s EBITDA up. It doesn’t matter whether the acquisitions create value for shareholders; if they boost EBITDA, they help from a vesting perspective…

…A comp package could actually disincentivize management from maximizing shareholder value.

Why does this one scare me? Because I’m so focused on incentives, and I’m always worried I’ll be lured into a situation where the incentives look positive but are actually insidious.

A live example will show this best: consider NNBR. In 2023, the stock was trading for just over $1/share, and they recruited a new CEO with a contract that would give him up to 2.5m shares if the stock price could hold $11/share…

…Fast forward to today, and things haven’t gone that well. The stock is back down to $1.50/share (though some early strength in the stock resulted in the $2 and $3 tranches vesting), and the company is reviewing strategic alternatives. Imagine you’re the CEO and had two choices right now: sell the whole company for $3/share, or max out the company’s credit line, head to Vegas, plop down at a roulette table, and bet it all on lucky #13.

If we ignored the fact that option #2 would result in some jail time, the CEO is actually incentivized to pursue that “lever up and risk it all” option. Why? Selling the company doesn’t help him vest more units, so he’s not super incentivized to pursue a sale (particularly because it puts him out of a job). In contrast, if he got lucky with the “lever up and risk it all” strategy all those PSUs would go in the money and he’d grab a multi-million dollar windfall…

…Someone highlighted COOK’s pay to me recently, and I’d be remiss if I didn’t mention it. COOK’s financial performance for 2025 missed all of their executive team’s performance goals, resulting in their stock declining >50% during the year and “no payments under the program to the Company’s named executive officers”…. but “the Board decided to award Jeremy Andrus, the Company’s Chief Executive Officer, and Michael Joseph (Joey) Hord, the Company’s Chief Financial Officer, discretionary cash bonuses equal to $956,250 and $270,938, respectively, due to their significant contributions to the Company in 2025 and to promote retention.” Well done guys; if I was a shareholder I know I’d be thrilled with that decision!

5. Letter to the 20-year-old investor – Chin Hui Leong

If you are closer to 20, you have an edge that no amount of money can buy. More on that later…

…I actually started investing much earlier, in 2002. Back then, there weren’t many choices. I bought the only unit trust available that tracked the US-based S&P 500…

…But when I bought my first individual stock in 2005, things changed. I actually felt more comfortable holding individual stocks than I did when holding index funds…

…Since 1928, the S&P 500 has fallen 10 per cent (or more) roughly every 1.8 years and 20 per cent every five years or so. When that happens, if you’re watching the index too closely, you’ll be upset.

You’ll start looking for reasons why it declined; my advice is don’t.

The S&P 500 is made up of 500 stocks…

…Trying to figure out why all 500 – or even 30 – stocks fell at once is too much work…

…When I held individual stocks, whenever a stock price fell, I could look at how much cash the company had. I could check whether its products were still selling. I could see whether it was generating profits and free cash flow…

…Between 2005 and 2010, the S&P 500 peaked in 2007, only to fall spectacularly during the global financial crisis. While the US market recovered starting from 2009, the index ended 2010 roughly where it started five years earlier…

…During what was rated as one of the deepest recessions in 70 years, I started noticing that certain companies were thriving.

The companies included Apple, Amazon, Booking Holdings, and Netlifx. They were among the 25 stocks I bought and held for a decade or more…

…Through it all, there were benefits I did not expect. I had a window into the future. I knew that online streaming was coming before it happened. I knew that same-day delivery was possible back in 2009…

…Amazon is up 39 times from when I bought it in 2010. Netflix has grown over 313 times. Booking Holdings is up 21 times. And Apple, which people thought had saturated its market a decade ago, is up 26 times…

…If you started investing at 20, or even earlier, time is on your side.

---

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 Amazon, Apple, and Netflix. Holdings are subject to change at any time.