Category: What We’re Reading

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 September 2025:

1. The ROI Question – Abdullah Al-Rezwan

A friend recently DM-ed me to highlight one of the quotes from Nvidia’s CFO in their recent earnings: “New NVFP4 4-bit precision and NVLink 72 on the GB300 platform delivers a 50x increase in energy efficiency per token compared to Hopper, enabling companies to monetize their compute at unprecedented scale. For instance, a $3 million investment in GB200 infrastructure can generate $30 million in token revenue, a 10x return.”

10x return? That’s a bit eye-popping number. My friend was understandably a bit skeptical of this claim, so he asked ChatGPT to show the math and some reasonable assumptions behind this claim…

…Clearly hyperscalers aren’t realizing such revenue from their investments in Nvidia chips yet…

…Batch size, which is the number of concurrent user requests processed simultaneously, is the single most important operational factor for maximizing throughput. The highest throughput numbers are always achieved with the largest possible batch sizes.

However, large batch sizes increase latency,..

…To maintain low latency, providers must deliberately use smaller batch sizes. This inherently sacrifices aggregate throughput to ensure a good user experience. The 1M tokens/sec benchmark mentioned in the ChatGPT screenshot above is likely achieved at latencies that would be unacceptable for real-time use…

…While 20% utilization may seem conservative, achieving this average utilization consistently (24/7/365) with monetized workloads may not be super easy in inference. AI inference demand is often “peaky.” Infrastructure built for peak load sits idle during off-hours…

…Hyperscalers do not only run the most expensive models. A likely material portion of their workload involves smaller, cheaper models (often <$1 per 1M tokens), reducing the actual blended revenue shown in my screenshot above. If the average realized price drops to $2/M tokens, the idealized revenue drops from $31.5M to $12.6M in my example (ceteris paribus).

2. AI Agents and the Future of Grocery Delivery – Thomas Reiner

Whether it’s OpenAI, Gemini, Siri, or some other tool, every consumer will have a personal agent in their pocket. It will book travel for you, make dinner reservations, manage your schedule, and for purposes of this discussion it will order your groceries for you.

For the average American family that gets groceries 1x per week it’ll know what you often order, it’ll recommend recipes, it’ll monitor past usage and wastage of food products, and it’ll know your consumer preferences around store loyalty. If you change your plans and tell your agent that you’re hosting a dinner party for 8 people serving it can assist by recommending what to serve and automatically ordering it from the grocery store…

…Looking across these models there’s two key areas where there are middlemen to be disrupted: 1) Grocery Delivery Marketplaces and 2) White Label Solutions. The Age of AI is going to be the age of efficiency and wringing out middlemen from the equation. Grocery Delivery Marketplaces are definitely middlemen and I’d argue that white label solutions from providers like Instacart Storefront sort of are…

…AI agents mean that the importance of brand goes down, while the importance of service goes up, and that’s where all the incremental dollars from both players are going. They can best position themselves to win in a world where AI agents make decisions based on best outcome (cost, quality, speed).

DoorDash with their DashMart concept is trying to take themselves out of the middleman equation and focus on being the 1P provider which is a lot more defensive in an AI agentic world.

The biggest challenge will be on crossing the trust chasm. While consumers have high trust in Amazon for shelf-stable goods, it’s non-existent for fresh goods. Early returns from Amazon same-day perishable trial showed 75% of consumers were first-time perishables shoppers at Amazon but only 20% reordered multiple times within the first month. “U.S. shoppers have shown they prefer to buy fresh goods from retailers that run brick-and-mortar stores, as evidenced by the struggles of online-only grocers like Peapod and FreshDirect.”…

…Generally the rise of AI Agents is a lot more mixed picture for the delivery marketplaces. On one hand, an AI that can spontaneously order anything might increase demand for delivery, on the other hand these services might be commoditized as the consumer UX slowly fades away and the importance is put on the underlying speed, convenience, and price.

3. An Interview with Cloudflare Founder and CEO Matthew Prince About Internet History and Pay-per-crawl – Ben Thompson and Matthew Prince

The reason to talk now, and we’ve talked offline about this a few times, both this year and last year, is your push for this pay-per-crawl concept. Why don’t you give me the high level overview, the pitch from your perspective, which I think has evolved? I would like to think partially based on some of my feedback, but what’s the pitch in September 2025?

MP: Let’s take Cloudflare out for a second and just talk about—

Talk about Matthew, the English student? The student newspaper editor.

MP: This is me channeling inner law professor. Let me give you the history of the Internet and why the Internet exists the way that it does and what’s changing.

This is usually my job, but go ahead.

MP: And you can tell me where I’m wrong, but this is my quick history of the Internet, and apologies to Michelle who hates history lessons.

For the last 25 years, the interface of the Internet has been search, and Google has dominated that space, and Google, their incentives as a company were to have the Internet grow as much as possible because if you have chaos, then the search becomes the organizer of the chaos. But you need incentives for people to actually create content and so Google not only had to create the thing that organized the Internet, but they then had to take the thing that took the traffic of where people went and then helped people monetize that, largely through advertising, although they also helped with subscriptions, and Google was the great patron of the Internet for the last 25 years. The web would not exist the way it does if there were not something like Google out there to create the incentives around.

There were a lot of problems with incentivizing around traffic, we created systems where people would just literally try and create rage-baity headlines to get people to click on things so that they could put ads against them and so not perfect, but we don’t have the Internet that we have today unless we have Google and search funding that.

That is changing. The world is shifting where the interface of the web is shifting from search engines and search engines give you a treasure map and say, “Hey, go figure out what your answer is by clicking on these 10 blue links”, to what are effectively answer engines. So if you look at OpenAI, if you look at Anthropic, if you look at Perplexity, even if you look at modern Google, they are not a search engine, they don’t give you a treasure map. Instead, they give you an answer right at the top of that page. That answer, for most users, 95% of the users, 95% of the time, it’s a better user interface. I’m not anti-answer engines, I’m not anti-AI, I think it’s better in every possible way for that to be what the interface is that we all interact with.

But the problem is that if you get the answer and you don’t get a treasure map, then you don’t generate traffic and if you don’t generate traffic, then the entire business model of the web, which has been based on traffic starts to break down and you can see that, not so much in e-commerce sites, not so much in things that actually sell you the physical thing because if you asked what’s the best camera to buy, even if you get an answer, you’ve still got to go buy it from somewhere. It’s going to take the e-commerce and the people who are selling things that’s going to work but the person who wrote the review—

The great thing about physical products is by definition they are scarce and the problem with text on the Internet is it is not scarce.

MP: It’s not scarce, that’s exactly right, and Google set this expectation that everybody can scrape the Internet for free, but it was never free. The Internet has never been free. Google paid for it for a really long time and the quid pro quo with the content creators was, “We get a copy of your content and in exchange we’ll send you traffic and help you monetize that traffic”.

That quid pro quo breaks down as we shift from search engines to answer engines and so something is going to change. I see three possible outcomes for that. And again, none of this involves — if Cloudflare disappeared tomorrow, this is still happening, one of these three things will happen. One, all of the journalists, academics, and researchers in the world will starve to death and die. And it’s crazy, like when you post this stuff on Twitter, how many people were like, “Well, we don’t really need journalists anymore, we have drones”, and I’m like, “I think we still need journalists”…

…If it’s inevitable though, then why does Cloudflare need to be so aggressive? You’re instituting these policies of doing your best to block bots, putting together protocols for recognizing what it’s worth, payments, etc., all very nascent to be sure, a lot to be figured out. But you are not taking the posture of a company that this is inevitable and it’s going to be great, you are being pretty forceful in trying to make something happen.

MP: Well, I think if we weren’t doing it, someone else would. But what I think we have a unique ability to do is we’re really good at stopping things like bots because we do it every day.

So again, it wasn’t like we were sitting around being like, “Hey, what should we do next? Let’s go change the business model of the web”, it was our customers who were publishers were coming to us being like, “We’re dying and we don’t have the technical wherewithal to step in front of it, but we need to stop this, please help”. And honestly, when Neil [Vogel] at Dotdash Meredith was telling me this, I rolled my eyes and I was like, “Publishers, they’re such Luddites, they’re always complaining about the new technology, they’re always complaining about the next thing, this isn’t a big deal”. And Neil and a bunch of others finally said, “Just go pull the data”, and it was only when we actually saw the data, when we saw that over the course of the last 10 years, it’s become 10 times harder to get a click from Google for the same amount of content on that same kind of basis, it’s now 750 times harder with OpenAI, it’s 30,000 times harder with Anthropic.

The business of traffic on the Internet as being the currency is going away and so something either again, either content creation is going to die, it’s going to become futile, or we’ve got to create a new business model. Again, if our mission is to help build a better Internet, this seems squarely in the line with what we should be working on.

So why does Garry Tan say that you are an axis of evil with Browserbase and you should legalize AI agents?

MP: I really don’t understand. I mean, I’m confused by Garry, I think part of it might be that he’s an investor in Perplexity.

Every story needs four characters, you need to have a victim, you need to have a villain, you need to have a hero, and you need to have the village idiot or the stooge. And if you think about it, any news story has those four characters. Right now, the people who have most been the villains have been Perplexity, where they’re doing just actively nefarious things in order to try and get around content company.

I’ll give you an example of something that we’ve seen them do, which is that if they’re blocked from getting the content of an article, they’ll actually, they’ll query against services like Trade Desk, which is an ad serving service and Trade Desk will provide them the headline of the article and they’ll provide them a rough description of what the article is about. They will take those two things and they will then make up the content of the article and publish it as if it was fact for, “This was published by this author at this time”.

So you can imagine if Perplexity couldn’t get to Stratechery content, they would say, “Oh, Ben Thompson wrote about this”, and then they would just make something up about it and they put your name along it. Forget copyright, that’s fraud, just straight up and that’s the sort of bad behavior of some tech companies that again, I think needs to be called out and punished.

4. Bitcoin TreasuryCos: Lessons From The 1929 Crash – Be Water

The explosive proliferation of Bitcoin treasury companies mirrors that of the 1920s investment trusts, and both gold rushes stem from a perfect storm of greed: intense investor demand for exposure to a scarce asset creates mNAV premiums that promoters rush to monetize. If Goldman Sachs could extract enormous profits from its trust in the 1920s, why couldn’t everyone else? If MicroStrategy can monetize its mNAV premium, why shouldn’t every other company follow suit?

Galbraith documented the explosive growth of trusts in the 1920s:

During 1928, an estimated 186 investment trusts were organized. By the early months of 1929, they were being promoted at the rate of approximately one each business day, and a total of 265 made their appearance during the course of the year…

…The renowned Yale economist Irving Fisher famously declared that stock prices had reached a “permanently high plateau” just prior the 1929 Crash. Fisher’s declaration exemplified the kind of euphoric confidence that typically marks a market top…

… Fisher’s plateau quote is now infamous, but the lesser-known context that gave rise to it tells a more revealing story. He was actually defending investment trusts as a key support for stock valuations, much as Bitcoiners cite built-in demand from Bitcoin treasuries today. The New York Times reported at the time:

Professor Fisher spoke on the subject of investment trusts and presented a defense for them against recent attacks in which they have been charged with responsibility for many present evils.

Fisher defended trusts on the grounds that these vehicles were awakening people to the superiority of stocks over bonds and providing investors with a superior structure for gaining equity exposure—much as Bitcoin treasury advocates today claim MicroStrategy offers turbocharged “torque” over direct Bitcoin ownership, and Bitcoin itself offers superiority over TradFi assets like fiat currency, stocks, bonds, and real estate:

I believe the principle of the investment trusts is sound, and the public is justified in participating in them, with due regard to the character and reputation of those conducting them. Largely through the influence of the investment trust movement, the public has been waking up to the superior attraction of stocks over bonds. And I believe the operation of the investment trusts, as a whole, has acted to stabilize the stock market rather than to make its fluctuations more violent…

…Saylor’s confidence in monetizing NAV discounts—which is perhaps reasonable for MicroStrategy in isolation—mirrors the same logic 1920s trust managers used to justify buybacks—only to find that such support strategies are ineffective when liquidity across the ecosystem vanishes and selling pressure dominates.

The trusts discovered that buying back shares when investors are selling and credit is tightening is vastly different from issuing shares when investors are buying. Desperate to prop up their stock prices, the trusts began buying back shares at a discount to NAV—a strategy Bitcoin treasury companies will likely adopt with equally disappointing results for most:

The stabilizing effects of the huge cash resources of the investment trusts had also proved a mirage. In the early autumn the cash and liquid resources of the investment trusts were large…But now, as reverse leverage did its work, investment trust managements were much more concerned over the collapse in the value of their own stock than in the adverse movements in the stock list as a whole…

Under these circumstances, many of the trusts used their available cash in a desperate effort to support their own stock. However, there was a vast difference between buying one’s stock now when the public wanted to sell and buying during the previous spring—as Goldman Sachs Trading Corporation had done—when the public wanted to buy and the resulting competition had sent prices higher and higher. Now the cash went out and the stock came in, and prices were either not perceptibly affected or not for long. What six months before had been a brilliant financial maneuver was now a form of fiscal self-immolation. In the last analysis, the purchase by a firm of its own stock is the exact opposite of the sale of stocks. It is by the sale of stock that firms ordinarily grow.

As the crisis deepened and the mNAV continued to trade at a discount, trusts depleted their remaining cash reserves in a desperate—and ultimately self-defeating—effort to support collapsing share prices:

However, none of this was immediately apparent. If one has been a financial genius, faith in one’s genius does not dissolve at once. To the battered but unbowed genius, support of the stock of one’s own company still seemed a bold, imaginative, and effective course. Indeed, it seemed the only alternative to slow but certain death. So to the extent that their cash resources allowed, the managements of the trusts chose faster, though equally certain death. They bought their own worthless stock. Men have been swindled by other men on many occasions. The autumn of 1929 was, perhaps, the first occasion when men succeeded on a large scale in swindling themselves.

5. Technology vs Platform Shift, Portfolio Change – Abdullah Al-Rezwan

Casey Winters made this point almost a couple of years ago which I think still holds up pretty well:

What I realized having gone through the internet and mobile platform shifts is that the technological and distribution shifts did not happen at the same time. Platform shifts that create both technological and distribution opportunities happen in a sequence, not all at once…AI has come out and definitely created a technological shift that enables new ways to solve problems that couldn’t be done before. But AI lacks a new distribution channel. ChatGPT is “not it”, as the kids would say. At least not yet…

… Sameer also points out that in a technology shift, users may not even be aware about the tech (it just works) whereas in a platform shift, the change is front and center for the user:

In a technology shift, form factor does not and should not matter. For example, scaling Snapchat’s picture messaging functionality would not have been possible without the shift to cloud computing. While Snapchat’s cloud hosting costs were significant, it would not have been possible to scale it as quickly if it relied on large, operationally complex investments into server infrastructure. The most important part — Snapchat’s end users did not know or care about this in any way. The user interface did not change to call out Snapchat’s “Cloud powered” technology. The biggest changes happened in the backend, not the frontend. 

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Disclaimer: The Good Investors is the personal investing blog of two simple guys who are passionate about educating Singaporeans about stock market investing. By using this Site, you specifically agree that none of the information provided constitutes financial, investment, or other professional advice. It is only intended to provide education. Speak with a professional before making important decisions about your money, your professional life, or even your personal life. We currently have a vested interest in Alphabet (parent of Google) and Amazon. 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 31 August 2025:

1. Monetary policy is not about interest rates, it’s about the money supply – Steve H. Hanke and John Greenwood

The ongoing feud between President Trump and Fed Chairman Jerome Powell centers on interest rates. This tells us more about the near-universal view of what constitutes monetary policy than it does about Trump or Powell. While Trump and Powell might quibble over the proper level for the Fed funds rate, they both think monetary policy is all about interest rates…

…Why the obsession over interest rates? One reason hinges on the fact that for over the past 30 years or so, macroeconomic models are neo-Keynesian extensions of dynamic stochastic general equilibrium (DSGE) models. These put interest rates front and center…

…But that’s not what monetarists, who embrace the quantity theory of money, tell us. Unlike the neo-Keynesian macroeconomic models that exclude money, the quantity theory of money states that national income or nominal GDP is primarily determined by the movements of broad money, not by changes in interest rates…

…First, let’s consider the case of Japan between 1996 and 2019. Throughout this period, the Bank of Japan’s (BOJ) overnight policy rate lingered at negligible levels, averaging 0.125%. As a result, most economists concluded that monetary policy in Japan was very “easy”. But monetarists, who focused on Japan’s anemic broad money (M2) growth of only 2.8% per year, concluded that monetary policy was “tight”…

…Japan’s inflation averaged a de minimis 0.2% per year in the 1996-2019 period. It is clear that the monetarists were correct…

…Let’s consider the U.S. between 2010 and 2019. During most of this decade, the Fed funds rate was held down at 0.25%. In addition, the Fed engaged in three episodes of quantitative easing (QE). Many concluded that this amounted to very “easy” monetary conditions. They warned that inflation would result. In fact, broad money growth (M2) remained low and stable at 5.8% per year. In consequence, inflation also remained low, averaging just 1.8% per year between 2010 and 2019. As was the case with Japan, interest rates turned out to be a highly misleading indicator of the stance of monetary policy. The growth in the money supply was a much better guide to economic activity and inflation than the course of the Fed funds rate…

…The reason why central bank policy rates are a misguided mechanism for steering and forecasting the course of the economy is because interest rates are, in large part, symptoms of past money growth, not necessarily drivers of future money growth. Changes in the quantity of money, on the other hand, directly fuel spending, and therefore correctly signal the direction of spending and inflation…

 …By ignoring the quantity theory of money and employing neo-Keynesian macroeconomic models, central bankers are often wrong-footed. They think that by managing policy rates, they are controlling monetary policy when in reality, they are just reacting to changes in the quantity of money that occurred in a prior period.

2. Global Crossing Is Reborn… – Praetorian Capital

Let’s start with total datacenter spend for 2025. Insiders think it’s going to clock in at around $400 billion…

…What’s a datacenter made of?? There are three main components; the building and land at roughly a quarter of the cost, all the power systems, wiring, cooling, racking, etc. at about 40% of the cost, and then the GPUs themselves at about 35% of the cost. I am sure I’m off by a few percent in these categories, but I’m relying on AI and we all know it’s still imperfect. I’m assuming that the building depreciates over 30 years, the chips are obsolete in 3 to 5 years, and then the other stuff lasts about 10 years on average. Call it a 10-year depreciation curve on average for an AI datacenter. Which leads you to the first shocking revelation; the AI datacenters to be built in 2025 will suffer $40 billion of annual depreciation, while generating somewhere between $15 and $20 billion of revenue. The depreciation is literally twice what the revenue is…

…With nothing to go on, I’m going to take an optimistic guess here, and say that ultimately, the margins get to positive, and then gradually creep up towards 25%. Why 25%?? I have no idea. It just sounds right because electricity is really expensive and you need a lot of expensive tech nerds to manage the equipment. Honestly, no one really knows where gross margins eventually land, so let’s just run with it, so that we can do some simple math…

…By my math, you need $160 billion of revenue at that 25% gross margin, which gives you $40 billion of gross margin against $40 billion of depreciation. Now, remember, revenue today is running at $15 to $20 billion. You need revenue to grow roughly ten-fold, just to cover the depreciation. Except, no one does anything to break even in business. For a new technology like this, with huge obsolescence risk, what unlevered ROIC would you demand?? Would you want a 20% ROIC?? That’s still dilutive to the ROIC for most of the largest capex spenders. Even at that dilutive ROIC, you’d need $480 billion of AI revenue to hit your target return…

…$480 billion is a LOT of revenue for guys like me who don’t even pay a monthly fee today for the product. To put this into perspective, Netflix had $39 billion in revenue in 2024 on roughly 300 million subscribers, or less than 10% of the required revenue, yet having rather fully tapped out the TAM of users who will pay a subscription for a product like this. Microsoft Office 365 got to $ 95 billion in commercial and consumer spending in 2024, and then even Microsoft ran out of people to sell the product to. $480 billion is just an astronomical number…

…While we all remember Pets.Com and the hundreds of other Dot Com startups that flamed away, it was companies like Global Crossing, spending tens of billions on fiber, that facilitated all of this. That fiber, amazingly, is still in use. Global Crossing went bankrupt along the way, as did many of its peers. They overestimated what people would pay for this fiber, not that it would eventually be used or valuable.

Today, I watch in awe (stupefaction really), as companies continue to throw endless resources at AI, I remember back to the Dot Com bubble and Global Crossing—fiber was the datacenter of that cycle, and Corning was the NVIDIA of its day (it lost 97% of its share price in the two years after it peaked).

3. Bitcoin TreasuryCos & The Roaring 20s – Be Water

The Bitcoin Treasury craze is either genius or madness—and very possibly some combination of both…

…This is not the first time leveraged financial vehicles promised to democratize access to scarce assets using leverage and the accretive magic of mNAV premiums: the 1920s investment trust and holding bubble followed a similar script in the run-up to the 1929 Crash…

…During the Roaring Twenties common stocks occupied a cultural position remarkably similar to Bitcoin (and arguably the S&P) today—they were viewed as the revolutionary investment of their era, and there was widespread belief that supply of stocks was too scarce to meet surging demand.

In the 1920s, mutual funds were introduced under the name “investment trusts,” and—like Bitcoin treasury companies—formed to capitalize on this scarcity. A major difference between modern mutual funds and these trusts was that the trusts were leveraged: like Bitcoin treasuries, they invested using borrowed money that was considered “safe” because—like MicroStrategy—they issued preferreds and long-term debt securities to the public to buy portfolios of stocks. Galbraith:

The most notable piece of speculative architecture of the late twenties, and the one by which, more than any other device, the public demand for common stocks was satisfied, was the investment trust. The investment trust did not promote new enterprises or enlarge old ones. It merely arranged that people could own stock in old companies through the medium of new ones…

…Like Bitcoin Treasuries, the 1920s trusts had the added appeal of mNAV premiums that seemed to offer something for nothing.

Just as Bitcoin treasury companies today boast of their mNAV and ‘bitcoin yield,’ a key feature of the 1920s bubble was the tendency for investment trusts to trade at significant premiums to mNAV during their heyday. Galbraith:

The measure of this respect for financial genius was the relation of the market value of the outstanding securities of the investment trusts to the value of the securities they owned.

Normally, the securities of the trust were worth considerably more than the property it owned—sometimes even twice as much. There should be no ambiguity on this point: the only property of the investment trust was the common and preferred stocks, debentures, mortgages, bonds, and cash that it held. (Often, it had neither an office nor office furniture; the sponsoring firm ran the investment trust out of its own quarters.)

Yet, had these securities all been sold on the market, the proceeds would invariably have been less—and often much less—than the current value of the outstanding securities of the investment company. The latter, obviously, had some claim to value that went well beyond the assets behind them…

…As with today’s Bitcoin TreasuryCos, this persistent mNAV premium created a powerful financial engine for both the trusts and the underlying stocks they were buying: the ability to conduct immediately accretive share issuances. When a trust trades at a premium to its underlying stock values, it can issue new units at the inflated market price and instantly increase the NAV for its existing shareholders.

This reflexive accretion mechanism created a self-reinforcing feedback loop similar to today’s “Bitcoin Leverage Loop”. The cycle worked as follows:

• Investor optimism drove a trust’s price to an mNAV premium.
• The trust would issue new units at this premium price, which was immediately accretive to the NAV per share.
• The new capital raised was used to purchase more stocks, adding buying pressure to the overall market and increasing the value of the trust’s own portfolio.
• The rising NAV and apparent success of the strategy further fueled investor optimism, widening the premium and allowing the cycle to repeat.
• Meanwhile, investors in the trusts and individual stocks amplified their exposure to a sure thing by using margin loans to leverage their positions, adding extra “juice” to the trade and further driving up NAVs and mNAVs for the trusts…

…Goldman Sachs Trading Corporation (GSTC) was perhaps the proto-MicroStrategy of the day. Launched by the influential Goldman Sachs partner Waddill Catchings in December 1928, it was, at its inception, the largest investment trust yet established—boasting an initial capitalization of $100 million. Its units, offered to the public at $104, was immediately oversubscribed and quickly soared in value, doubling to $226 within a short period and trading at a massive premium to the underlying value of its stock holdings…

…In  Brad DeLong and Andrei Shleifer’s The Stock Market Bubble of 1929: Evidence from Closed-end Mutual Funds, they noted:

If [investment trust mNAV premia] indeed reflect excessive investor optimism rather than skill at management, there will be a tendency for funds to pyramid on top of one another. If each fund can be sold for 50 percent more than its own net asset value, promoters can more than double their profits by establishing a fund that owns funds that hold stocks, rather than just establishing funds that hold stocks…

This prediction is confirmed by one of the largest funds: the Goldman Sachs Trading Corporation. This was a closed-end fund organized in December 1928 with a net asset value of around $100 million. In 1929, one of its largest holdings was the Shenandoah Corporation, another closed-end fund organized by Goldman Sachs. Another large holding was in its own stock.

Nor is this all. In the same year, Shenandoah organized a new closed-end fund called the Blue Ridge Corporation and became a large investor in its stock. All these funds traded at premia; at the top of the pyramid, the Goldman Sachs Trading Corporation traded at a premium to a premium to a premium to net asset value…

…If history serves as any guide, we can expect Bitcoin treasury companies to begin investing in other Bitcoin treasury companies before this cycle concludes.

4. Whatever Happened to the Self Driving Semi? – Chris Paxton

There are almost three million semi trucks in the United States alone, to the point that trucker is the most common job in 29 states. Most of these are driving 400-600 miles per day along long, straight, predictable highways — a use case that, at a glance, seem perfect for autonomy.

And yet, on-road autonomy looks guaranteed to start not with semis but with taxis, operating over much shorter distances in much less of the United States…

…Fully-loaded trucks are massive, with a legally-mandated maximum of 80,000 lbs. This makes everything a truck does notably less responsive. Planning becomes more difficult; learning methods are less effective, too, when there’s not a clear, immediate mapping between input and output.

If we want to discuss how serious a problem this is, we should look at stopping distance; i.e. how long it takes a semi truck to come to a complete stop because, say, there was an accident on the road ahead of it.

Stopping distance for a fully-loaded semi truck traveling at 65 mph is approximately 525 feet to about 600 feet. Even though most US highways have higher speed limits, trucking companies usually limit speed to 65 mph for safety and fuel efficiency reasons; it seems reasonable to expect that autonomous truckers would do the same. But note that this is under ideal conditions; stopping distances can as much as double on icy roads.

Now, a good long-ranged lidar could have 1000 feet of range. Aurora has a particularly good in-house lidar, with about 450 meters (~1500 feet) of range – much farther than many other options. But maximum range isn’t effective range, which is far more important. This is hard to estimate — it varies depending on conditions, on objects, and of course on the quality of the particular classifiers being used to interpret objects. This quantity is notably shorter than the maximum range on practically any sensor, by as much as about half; and we’ll also need to classify if this was a spurious detection (a plastic bag blowing onto the road, a cardboard box) or a serious issue.

And that’s setting aside other concerns: what if there’s a patch of black ice ahead on the road? The lidar can’t detect this at all, and it’s a huge issue for highway driving. There was a famously horrific 133-car pileup in Fort Worth, Texas in 2021, caused by black ice, which led to 65 injuries and six fatalities.

5. SITALWeek #459 – Brad Slingerlend

Investing is a form of storytelling. CEOs spin tales about their companies and try to rally the workforce to manifest them over a long time horizon. Investors decide if they too believe the stories or not. Most of the time, the stories are fiction, fantasy, or even fairy tales. Occasionally, visionary entrepreneurs pen a nonfiction, or even a compelling fiction that turns out to be so predictive of the future that it serves as prior art for reshaping reality (think of the Steve Jobs Reality Distortion Field!). There are also stories about economics, politics, and the world at large that influence the stories about companies and investments. Investors create their own stories about businesses as well, and the resulting investment ideas can end up in either a canonized history book or a throwaway dime novel. Even trying to unravel the truth of past stories can be fraught, as hindsight is only as good as the incomplete and unreliable human narratives on which history is based…

…Today, it’s not clear how much, if any, impact investors’ stories have on the daily prices of stocks. And, in some cases, it appears to me companies are losing complete control of their own narratives as well…

…And, now, we have something very different happening: all of that volume in the market, previously programmed in some form or another by humans guiding machine learning algorithms (or retail investor brains programmed by social media news cycles, etc.), is slowly being taken over by LLMs and agentic AI. I suspect autonomous AI trader bots are writing their own signal algorithms and creating their own stories. They are telling those stories to each other and executing trades. We can see clues that this shift is happening in a recent study that found meaningful drops in trading activity during ChatGPT outages. I think that tidbit of information gives us, well, the rest of the story as to what will soon define the stock market on a day-to-day basis (if it’s not already the dominant force, which I suspect it is). This agentic investing evolution will create even more noise and less signal in the daily price of any given stock. Again, this turn of events spells good news for us active investors who still think we can find stories that, with any luck, will turn out to be superior nonfictional investments.

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Disclaimer: The Good Investors is the personal investing blog of two simple guys who are passionate about educating Singaporeans about stock market investing. By using this Site, you specifically agree that none of the information provided constitutes financial, investment, or other professional advice. It is only intended to provide education. Speak with a professional before making important decisions about your money, your professional life, or even your personal life. We currently have a vested interest in Microsoft 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 17 August 2025:

1. Beyond the “Search” Box – Abdullah Al-Rezwan

Semrush tracked 260 billion rows of clickstream data on U.S. desktop users who began using ChatGPT in Q1 2025, comparing their Google Search sessions in the 90 days before and after adoption to a control group that never used ChatGPT. This setup allowed them to isolate whether ChatGPT adoption caused changes in traditional search behavior compared to natural trends over time.

The overall result of the study shows after adopting ChatGPT, users increased their Google Search sessions from 10.5 to 12.6 per week while also adding about 5 ChatGPT sessions weekly, suggesting ChatGPT use complemented rather than replaced Google searches.

Semrush shared some cohort level data by month which all show that despite sustained ChatGPT usage after adoption, Google Search usage remained resilient.

One may wonder if you keep using ChatGPT for longer than a year, perhaps it eventually changes your Google usage. That also doesn’t quite seem to be the case yet since a 500-day study by Semrush of users who began using ChatGPT in January 2024 found that Google search activity remained steady while ChatGPT usage stayed consistent after adoption.

2. Podcast: Amazon’s advertising strategy (with Adam Epstein) (Transcript here) – Eric Benjamin Seufert and Adam Epstein

Adam Epstein: I’ve been working in ad tech for seven plus years, and people have been decrying the end of the agency for as long as I can remember through the use of automated and simple software. But AI adds a new layer of complexity to everything, and complexity is good for agencies particularly. I’m not sure who coined the phrase, but they basically said agencies are cockroaches. And I believe that to probably be the case.

At least for the next three to five years, I actually don’t even think agentic AI will be a headwind for agencies—I think it will be a tailwind on two dimensions. First, the most scaled agencies in the world have been able to scale themselves not through data and technology, but through scaled processes, standard operating procedures, training collateral and docs to create expertise and uniform level of service across all their clients and team members. Well, guess what’s really good for training an LLM? Literally all of those documents.

Every agency I’ve talked to for seven years comes to me and says, “How is your off-the-shelf ad tech different than the off-the-shelf ad tech that you’re going to sell to the next agency tomorrow?” And the answer has always been it hasn’t been any different—it’s been exactly the same. But with agentic AI, you now no longer buy software—you hire software. You hire software, and you train software, and you develop a new teammate that you train and mold exactly as you would a new team member.

Agencies want this level of customization. They’re actually in a perfect position to do so because they’ve invested in collateral that allows them to train an LLM in a very efficient manner. We’re just catalyzing them and giving them the tools to do exactly that.

The other interesting thing with services businesses is that you typically need to linearly scale headcount as you scale customer and revenue growth. But I believe agentic AI will bring in a world for media agencies in particular where they’ll be able to exponentially increase customers and revenue while maintaining a flat headcount. Agentic AI will take all the operational work that teams are currently running and allow these agencies to scale in ways they’ve never been able to scale before. It’ll be a massive tailwind from an operating margin perspective, and I think people will actually start to value agencies on a different multiple than what they have in the past, given the fundamentally different margin profile.

3. Robotaxis & AI | Uncharted Territories Magazine | Tech Update Summer 2025 – Tomas Pueyo

Waymo is destroying the competition. It has surpassed Lyft in rides in SF, and is on track to surpass Uber within 8 months or so.

And this is with Waymo taking 2x longer and costing 70% more than Lyft!!!1 That’s how much better the Waymo experience is: People really care about not having a driver!…

…Uber said ride-hailing could grow by 25x if its price dropped under $1/mile…

…Uber couldn’t make it happen. But in Austin, now Tesla costs $1 per mile.

As a comparison, ride hail customers are currently paying nearly $3/mile.

If Tesla maintains this type of pricing, it won’t make sense for drivers to continue their job, and Uber and Lyft will crash.

8% of US workers are professional drivers…

…I didn’t realize how important this is until I read this article:

Something like 40,000 people die in traffic accidents in the US every year. The number is over one million per year globally.

There are over 5 million non-fatal injuries from car crashes each year that require medical attention in the US.

In 2010, the total costs from these events was $836 billion, or ~$2700 per American per year.

But these costs are just the tip of the iceberg because most of the cost of transportation, at >$2 trillion per year, comes from adjusting to human inadequacies.

Wait, what? Car accidents are costing trillions to the world economy? How?

• A big share of the materials in cars are due to safety. Without accidents, you can strip them out, saving all their money. Austin Vernon calculates we could make car weights 10x lower.
• Automobile shapes today trade off safety and aerodynamicity. Without safety, they can become more aerodynamic, and move faster at a cheaper cost.
• Cheaper transportation costs massively improve the economy.
• Lower weights on roads means less road wear, and hence less maintenance cost.

4. What If Money Expired? – Jacob Baynham

More than a century ago, a wild-eyed, vegetarian, free love-promoting German entrepreneur and self-taught economist named Silvio Gesell proposed a radical reformation of the monetary system as we know it. He wanted to make money that decays over time. Our present money, he explained, is an insufficient means of exchange. A man with a pocketful of money does not possess equivalent wealth as a man with a sack of produce, even if the market agrees the produce is worth the money.

“Only money that goes out of date like a newspaper, rots like potatoes, rusts like iron, evaporates like ether,” Gesell wrote in his seminal work, “The Natural Economic Order,” published in 1915, “is capable of standing the test as an instrument for the exchange of potatoes, newspapers, iron and ether.”…

…Gesell believed that the most-rewarded impulse in our present economy is to give as little as possible and to receive as much as possible, in every transaction. In doing so, he thought, we grow materially, morally and socially poorer. “The exploitation of our neighbor’s need, mutual plundering conducted with all the wiles of salesmanship, is the foundation of our economic life,” he lamented.

To correct these economic and social ills, Gesell recommended we change the nature of money so it better reflects the goods for which it is exchanged. “We must make money worse as a commodity if we wish to make it better as a medium of exchange,” he wrote.

To achieve this, he invented a form of expiring money called Freigeld, or Free Money. (Free because it would be freed from hoarding and interest.) The theory worked like this: A $100 bill of Freigeld would have 52 dated boxes on the back, where the holder must affix a 10-cent stamp every week for the bill to still be worth $100. If you kept the bill for an entire year, you would have to affix 52 stamps to the back of it — at a cost of $5.20 — for the bill to still be worth $100. Thus, the bill would depreciate 5.2% annually at the expense of its holder(s). (The value of and rate at which to apply the stamps could be fine-tuned if necessary.)

This system would work the opposite way ours does today, where money held over time increases in value as it gathers interest. In Gesell’s system, the stamps would be an individual cost and the revenue they created would be a public gain, reducing the amount of additional taxes a government would need to collect and enabling it to support those unable to work.

Money could be deposited in a bank, whereby it would retain its value because the bank would be responsible for the stamps. To avoid paying for the stamps, the bank would be incentivized to loan the money, passing on the holding expense to others. In Gesell’s vision, banks would loan so freely that their interest rates would eventually fall to zero, and they would collect only a small risk premium and an administration fee.

With the use of this stamp scrip currency, the full productive power of the economy would be unleashed. Capital would be accessible to everyone. A Currency Office, meanwhile, would maintain price stability by monitoring the amount of money in circulation. If prices go up, the office would destroy money. When prices fall, it would print more.

In this economy, money would circulate with all the velocity of a game of hot potato. There would be no more “unearned income” of money lenders getting rich on interest. Instead, an individual’s economic success would be tied directly to the quality of their work and the strength of their ideas. Gesell imagined this would create a Darwinian natural selection in the economy: “Free competition would favor the efficient and lead to their increased propagation.”…

…Although many dismissed Gesell as an anarchistic heretic, his ideas were embraced by major economists of the day. In his book “The General Theory of Employment, Interest and Money,” John Maynard Keynes devoted five pages to Gesell, calling him a “strange and unduly neglected prophet.” He argued the idea behind a stamp scrip was sound. “I believe that the future will learn more from the spirit of Gesell than from that of Marx,” Keynes wrote…

…That very year, the owner of a dormant coal mine near the Bavarian town of Schwanenkirchen tried in vain to get a loan from a bank to begin mining again. Stymied by the representatives of traditional finance, he went to the Wära Exchange Association, a group that was created to put Gesell’s ideas into practice. The group agreed to give the mine owner 50,000 Wära, a depreciating currency equivalent to 50,000 Reichsmarks.

The mine owner then gathered the unemployed miners and asked if they would go back to work, not for legal tender, but for this new currency. They agreed that any money was better than no money. The mine owner purchased food, clothing and household goods from warehouses that were already using the Wära currency. The miners, now back digging coal, used their wages to buy these goods from the mine owner. Soon, other businesses in town wanted to use the currency to benefit from the sudden influx of cash. Because the currency depreciated at 1% per month, everyone was eager to part with it and it circulated rapidly throughout the economy. Soon, in whole districts, the Wära currency replaced the Reichsmark, which alarmed the bigger banks and the government. Finally, the Reichsbank ended the experiment by banning the currency.

Two years later, in the Austrian town of Wörgl, Gesell’s ideas came to life again. In 1932, Wörgl’s mayor, a socialist locomotive engineer, desperately wanted to get his constituents back to work. A supporter of Gesell’s ideas, he devised a plan where Austrian schillings would be replaced with Work Certificates that depreciated at 1% per month.

The mayor hired townspeople, paid in Work Certificates, to improve roads, install streetlights and build a concrete bridge. Work Certificates circulated rapidly from merchants to tenants, to landlords, to saving accounts. People paid their taxes early to avoid paying for stamps. In one year, the Work Certificates traded hands 463 times, creating goods and services worth almost 15 million schillings. By contrast, the ordinary schilling was exchanged only 21 times.

The experiment was called the Miracle of Wörgl. Vienna newspapers took notice. The government of France expressed interest. Two hundred mayors in Austria devised similar programs in their communities. Again, however, the financial authorities grew uneasy, arguing that these local stamp scrips undermined the currency-issuing power of the national bank. By the fall of 1933, the Austrian Supreme Court had prohibited their circulation.

Gesellian experiments happened in the U.S. and Canada too, inspired by the Great Depression. In 1932, in Hawarden, Iowa, a limited amount of stamp scrip was put into circulation to pay for public works. The same year, a similar program was deployed in Anaheim, California. In 1933, Oregon attempted to print $80 million in stamp scrip, but the U.S. Treasury stopped it. The government of Premier William “Bible Bill” Aberhart in Alberta, Canada, introduced depreciating “prosperity certificates” (which people quickly renamed “velocity dollars”) in 1936.

That decade in the U.S., 37 cities, eight counties and some business groups attempted to issue almost 100 different types of stamp scrip. All these experiments were local, small in scope and short-lived. In 1933, the economist Irving Fisher, who called himself “a humble student of Silvio Gesell,” tried to persuade President Franklin Delano Roosevelt to adopt a national stamp scrip, and even convinced an Alabama senator to introduce a bill that would have issued up to $1 billion in depreciating currency. It never came to a vote. Roosevelt, who was preparing to take the country off the gold standard, worried that any further economic innovations would be too destabilizing…

…Gesell’s idea for depreciating money “runs counter to anything we’ve ever learned about the desirable properties of money,” David Andolfatto, a former senior vice president of the Federal Reserve Bank of St. Louis and the chair of the economics department at the University of Miami, told me recently. “Why on Earth would you ever want money to have that property?”

But during the economic downturn that followed the Covid pandemic, Andolfatto recognized the potential value of an expiring money in times of crisis. The relief checks that the government sent out to U.S. households didn’t immediately have their desired effect of stimulating the economy because many people saved the money rather than spend it. This is the paradox of thrift, Andolfatto explained. What’s good for the individual is bad for the whole.

“Well, what if we gave them the money with a time fuse?” Andolfatto remembers wondering. “You’re giving them the money and saying look, if you don’t spend it in a period of time, it’s going to evaporate.”

In a paper he wrote for the Fed in 2020, Andolfatto called this concept “hot money credits.” He pointed out that when the economy goes into a funk, there is a “coordination failure” where people stop spending and others stop earning. Withholding money in times of fear creates a self-fulfilling prophecy by further stifling the economy. So, could Gesell’s idea of expiring money be the cure?

“The desirability depends on the diagnosis,” Andolfatto told me. “It’s like a doctor administering a drug to a healthy person and a sick person. You administer the drug, and it has some side effects. If the person is healthy, you’re not going to make them any better. You might make them even worse. If they’re sick, it might make them better.”

The problem, Andolfatto said, is that issuing pandemic checks with an expiration date would hurt those with little savings. People with money in the bank would use their expiring money just like normal money. People with no savings, on the other hand, might find that expiring money forced them to spend and did little to stabilize their financial situations…

…Keynes believed Gesell’s expiring money amounted to “half a theory” — it failed, Keynes argued, to account for people’s preference for liquid assets, of which money is just one example. “Money as a medium of exchange has to also be a store of value,” Willem Buiter, a former global chief economist at Citigroup, told me. In a Gesellian economy, he continued, the affluent would simply store their wealth in another form — gold bars, perhaps, or boats — which could be converted into money when they wanted to transact.

Buiter doesn’t believe Gesellian money can really address serious social inequality, but he did note times when it was advantageous for a central bank to drop interest rates below zero, like when inflation and market interest rates are low and should go lower to maintain full employment and utilization of resources. Positive or negative interest rates could easily be applied to digital money in a cashless economy, for which Buiter and others have advocated. But it’s hard to imagine how a government today could practically implement a Gesellian tax on hard currency. “You’d have to be able to go out and confiscate money if it’s not stamped,” Buiter said. “It would be rather brutal.”

5. Intel’s One True Stakeholder is Here – Doug O’Laughlin

There is a rumor that the Trump administration could be taking a stake in Intel…

…And it’s no surprise that the future of American semiconductors has Intel written all over it. But there’s no other way than forward, and I think it’s time to consider what needs to happen realistically, and that’s the death of the Intel we once knew to make room for what’s next. The key is that while CPUs don’t matter, the only American leading-edge foundry left making them is critical.

The problem is that the company that funds it might run out of money, and that’s why they need to publicly threaten to stop financing the future of the foundry, because it’s a problem they can’t do alone. That is why I believe they so publicly announced the ending of future nodes past 14A…

…The calculus for America is pretty simple. In my view, there is very little strategic importance to the Intel CPU business. The x86 ecosystem was once the most incredible compute ecosystem, but AMD designs better chips than Intel could; Intel has the one thing that AMD does not, a Fab. The fabless business at Intel has a real issue in that making a CPU is becoming a relatively commoditized business. ARM has made it possible for almost any hyperscaler to have its ARM-based CPU, while AMD continues to outdesign Intel at its core job, and that’s not even discussing the longer-term RISC-V ecosystem.

Adding up the CPU side, I see a business with massive competition and Intel not at the top of the stack. Intel has to deal with increasing competition in’s core profit center while at the same time covering the increasingly heavy burden of a leading-edge fab. There is only one leading-edge foundry (TSMC), and a second American option is the single highest value-added project of all time…

…We cannot rely on Taiwan for the future of semiconductors. The more capacity we get from TSMC, the more we remain reliant on R&D in Taiwan rather than the US. Intel must be standalone and must have the capabilities to do the two things the US critically needs. High-end logic and military capabilities. I’d argue the second is met chiefly, but the first Intel is hopelessly behind.

What’s worse is that Intel has a bad customer, itself. Intel needs a good customer to be the anchor, and sadly, the core customer is a CPU company that is struggling to find its way in an accelerated compute world…

…Trump can bully Broadcom, Nvidia, Qualcomm, Apple, and AMD to put orders towards Intel, while possibly forcing Amazon, Microsoft, Google, and others to make a large investment in the fab itself (or push orders). Additionally, forcing semicap companies like KLAC, Applied Materials, and Lam Research to invest and give resources in exchange for approved licenses is another example of a carrot and a stick. I think Trump could forge the giant partnership to happen, but then execution is all up to Intel. And LBT is still once again qualified for the job.

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

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

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

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

Here are the articles for the week ending 10 August 2025:

1. How we’re making data centers more flexible to benefit power grids – Michael Terrell

That’s why we’ve been working to bring flexible demand capabilities into our data center fleet, which enables us to shift or reduce power demand during certain hours or times of the year. These capabilities, often referred to as demand response, have several advantages, especially as we continue to see electricity growth in the US and elsewhere. It allows large electricity loads like data centers to be interconnected more quickly, helps reduce the need to build new transmission and power plants, and helps grid operators more effectively and efficiently manage power grids.

We’re pleased to report on our progress in the implementation of these capabilities, including two new utility agreements with Indiana Michigan Power (I&M) and Tennessee Valley Authority (TVA). These agreements represent the first time we’re delivering data center demand response by targeting machine learning (ML) workloads. This builds on our successful demonstration with Omaha Public Power District (OPPD) where we reduced the power demand associated with ML workloads during three grid events last year — paving the way for us to pursue opportunities at other locations…

…Advancing Google’s 24/7 carbon-free energy ambition requires a holistic approach, to both procure clean energy and support the grid through demand-side solutions. Flexible demand is an important piece of this portfolio — it can be deployed quickly, helping bridge the gap between short-term load growth and long-term clean energy solutions, and delivers immediate benefits.

The first data center demand response capabilities we developed involve shifting non-urgent compute tasks — like processing a YouTube video — during specific periods when the grid is strained. Through our ongoing partnerships with Centrica Energy and transmission system operator Elia in Belgium, and Taiwan Power Company in Taiwan, we’ve leveraged this capability to help grid operators maintain reliability during those periods of the year when demand is the highest.

As AI adoption accelerates, we see a significant opportunity to expand our demand response toolkit, develop capabilities specifically for ML workloads, and leverage them to manage large new energy loads. By including load flexibility in our overall energy plan, we can manage AI-driven growth even where power generation and transmission are constrained.

2. Why China is building the world’s largest hydropower station in Tibet – Amber Zhang

On July 19, 2025, Chinese Premier Li Qiang stood in the remote southeastern Tibetan city of Nyingchi and announced the official commencement of the Medog hydropower station—what he termed a “project of the century”…

…The mega project is a hydropower station on the lower reaches of the Yarlung Tsangpo River, a plan of such breathtaking scale that it redefines the very concept of a mega-project. With a projected investment of 1.2 trillion yuan (approximately $167-170 billion) and a planned annual electricity output of 300 billion kilowatt-hours (kWh), the facility is designed to generate nearly three times the power of the iconic Three Gorges Dam, China’s previous “project of the century”.

Its output alone would be enough to power the entire United Kingdom [*(2023 statistics)] and is equivalent to 20% of China’s total residential electricity consumption in 2024 [*], enough for 300 to 400 million people…

…The Yarlung Tsangpo project marks the boldest chapter yet. It is located in Medog County, a remote corner of southeastern Tibet, at a dramatic geographical feature known as the “Great Bend” of the Yarlung Tsangpo River. Here, after flowing eastward across the Tibetan Plateau, the river makes a hairpin turn around the sacred Mount Namcha Barwa and plunges south toward India. In just 50 kilometers (31 miles), the river drops between 2,000 and 2,350 meters (over 6,500 feet) [*], through the world’s deepest canyon—three times deeper than the Grand Canyon in the United States.

It is this staggering vertical drop that has long been viewed by engineers as the single most promising site for hydropower generation on Earth, with a water energy density estimated to be seven times that of the Three Gorges [*].

To exploit the potential of the “Great Bend,” China is employing the “run-of-the-river” design, or, figuratively speaking, the “cut-the-bend” approach. Instead of constructing a single massive dam with a vast reservoir—which would be impractical and even more hazardous in this terrain—the project will consist of a series of five smaller “cascade” hydropower stations. These dams will divert a portion of the river’s powerful flow into a network of four enormous tunnels, each stretching approximately 20 kilometers (12.5 miles) and bored directly through the Himalayan mountains [*].

This “run-of-the-river” approach, which utilizes advanced dam-less diversion technology, means water is not consumed but rather borrowed with a resource utilization rate of up to 85% (*). It plunges down these tunnels, gaining immense velocity, to spin turbines located at a much lower elevation at the bottom of the canyon. After generating power, the water is discharged back into the river just before it crosses the Line of Actual Control into India. This design allows China to harness the massive potential energy from the 2,000-meter elevation drop while minimizing the size of the required reservoirs…

…For instance, the engineering challenges require technical capabilities that China has only developed in recent decades.

Beyond basic infrastructure like building roads and bridges, two key technologies have made this project feasible:

The first is tunnel boring machines (TBM)—used to dig long tunnels through mountains, similar to how a pangolin burrows through soil. These machines were once monopolized by German manufacturers and were prohibitively expensive. But after China localized their production, they became widely available and cost-effective.

In the early planning stages of the Medog hydropower station, several construction proposals were considered. Now, only one remains viable: a “run-of-the-river” approach, which involves digging a tunnel over 30 kilometers long to connect both ends of the river’s U-shaped bend, using the more than 2,000-meter drop in elevation to generate electricity. Such an idea would have been unthinkable in the past—but with TBMs, it has become a realistic option.

In fact, there’s already a prototype for this kind of construction: the Jinping II Hydropower Station on the Yalong River. Its surrounding terrain is nearly identical to that of the Medog section. There, engineers cut through both ends of a similar U-shaped bend, building four water diversion tunnels—each 17 kilometers long. The station has been operational for six years and has proven stable.

With this prior experience as a foundation, taking on the challenge of the Himalayas no longer seems so daunting.

The second breakthrough is ultra-high-voltage (UHV) power transmission. Tibet is vast and sparsely populated, and local demand is far below the project’s potential output. Most of the electricity will need to be transmitted to major power-consuming provinces in the east—or exported to Southeast Asia. The only viable solution for such long-distance transmission is UHV, one of the few technologies in which China is globally recognized as a clear leader. Years of experience from the “West-to-East Power Transmission” program have proven that UHV is both mature and reliable…

…Also, the Yarlung Tsangpo Grand Canyon is one of the most inaccessible places on Earth. Until the completion of the Paizhen-Medog Highway in 2022, the area lacked reliable road access and a power supply, making the logistics of transporting millions of tonnes of materials like steel and an estimated 40 million tons of cement a monumental undertaking.

The resulting power output is staggering. The project is designed with an installed capacity of 60 to 70 gigawatts, producing 300 billion kWh of electricity annually. This is enough energy to meet the needs of nearly 300 million people, making it by far the most powerful hydroelectric facility on Earth.

3. The Imitation Game: Defending against AI’s Dark Side! – Aswath Damodaran

A few weeks ago, I started receiving a stream of message about an Instagram post that I was allegedly starring in, where after offering my views on Palantir’s valuation, I was soliciting investors to invest with me (or with an investment entity that had ties to me). I was not surprised, since I have lived with imitations for years, but I was bemused, since I don’t have an Instagram account and have not posted on Facebook more than once or twice in a decade. In the last few days, those warnings have been joined by others, who have noted that there is now a video that looks and sounds like me, adding to the sales pitch with promises of super-normal returns if they reach out, and presumably send their money in. (Please don’t go looking for these scams online, since the very act of clicking on them can expose you to their reach.)…

…To get a measure of what the current AI scams that are making the rounds get right and wrong, I did take the time to take a closer look at both the Instagram post and the fake video that are making the rounds….

…The good news is that this AI scam gets my language and look right, but it is sloppily done in terms of content and capturing who I am as a person. The bad news is that it if this scammer was less lazy and more willing to put in some work, even with the current state of AI, it would have been easy to bring up the grades on content and message. I will wager that the Damodaran Bot that I mentioned earlier on in this post that is being developed at NYU Stern would have created a post that would have been much more difficult for you to detect as fake, making it a Frankenstein monster perhaps in the making. The worse news is that AI technology is evolving, and it will get better on every one of these fronts at imitating others, and you should prepare yourself for a deluge of investment scams…

…It remains an uncomfortable truth that the people most exposed to these scams are the ones who have read little or none of what I have written, and I wish there were a way that I could pass on the following suggestions on how they can protect themselves against the other fakes and scams that will undoubtedly be directed at them.

1. “Looks & sounds like” not good enough: Having seen the flood of fake AI videos in the news and on social media, I hope that you have concluded that “looks and sounds Iike” is no longer good enough to meet the authenticity test. This remains AI’s strongest suit, especially in the hands of the garden variety scammer, and you should prepare yourself for more fake videos, with political figures, investing luminaries and experts targeted.

2. Steer away from arrogance & hype: I have always been skeptical of the notion that there is “smart” money, composed of investors who know more than the rest of us and are able to beat the market consistently, and for long periods. For the most part, when you see a group of investors (hedge funds, private equity) beating the market, luck is more of a contributor as skill, and success is fleeting. In a talk on the topic, I argued that investors should steer away from arrogance and bombast, and towards humility, when it comes to who they trust with their money, and that applies in spades in the world of AI scams. Since most scammers don’t understand the subtlety of this idea, screening investment sales pitches for outlandish claims alone will eliminate most scams.

3. Do your homework: If you decide to invest with someone, based upon a virtual meet or sales pitch, you should do your homework and that goes well beyond asking for their track records in terms of performance. In my class on investment philosophies, I talk about how great investors through the ages have had very different views of markets and ways of making money, but each one has had an investment philosophy that is unique, consistent and well thought through. It is malpractice to invest with anyone, no matter what their reputation for earning high returns, without understanding that person’s investment philosophy, and this understanding will also give you a template for spotting fakes using that person’s name.

4. Avoid ROMO & FOMO: In my investing classes, I talk about the damage that ROMO (regret over missing out) and FOMO (fear of missing out) can do to investor psyches and portfolio.

• With ROMO (regret over missing out), where you look back in time and regret not buying Facebook at its IPO price in 2012 or selling your bitcoin in November 2013, when it hit $1000, you expose yourself to two emotions. The first is jealousy, especially at those who did buy Facebook at its IPO or have held on to their bitcoin to see its price hit six digits. The second is that you start buying into conspiracy theories, where you convince yourself that these winners (at least in the rear view mirror) were able to win, because the game was fixed in their favor. Both make you susceptible to chasing after past winners, and easy prey for vendors of conspiracies.
• With FOMO (fear of missing out), your overwhelming concern is that you will miss the next big multi-bagger, an investment that will increase five or ten fold over the next year or two. The emotion that is triggered is greed, leading you to overreach in your investing, cycling through your investments, as most of them fall short of your unrealistic expectations, and searching for the next “big thing”, making you susceptible to anyone offering a pathway to get there.

Much as we think of scammers as the criminals and the scammed as the victims, the truth is that scams are more akin to tangos, where each side needs the other. The scammer’s techniques work because they trigger the emotions (fear, greed) of the scammed, to respond, and AI will only make this easier to do. Looking to regulators or the government to protection will do little more than offer false comfort, and the best defense is “caveat emptor” or “buyer beware”.

4. How has macroeconomic research misjudged China? – Robert Wu and Dongfan Ma

From 2000 to now, China’s economic structure has undergone at least three major transitions:

• 2000–2010: Export and processing-led growth was the dominant force.
• 2010–2020: Real estate and household leverage became the drivers, with Total Social Financing (TSF) as the key indicator.
• Post-2020: Traditional models started to fail, and new growth drivers began to emerge.

Yet, most macroeconomic analyses remain stuck in phase two—still using TSF and real estate sales as core references. What we observe now is that these indicators have lost predictive value. Their correlation with PMI and corporate earnings is quickly fading…

…Processing trade began tapering off after 2010. But from 2016 onward, general trade has grown steadily and rapidly.

This distinction matters: processing trade mostly reflects contract manufacturing for others, while general trade signals the rise of China’s own manufacturing capabilities, brands, and integrated industrial chains. In 2024, China’s total export volume was already three times the size of real estate investment. Back in 2019, the two were roughly equal.

In other words, China’s new economic engine is no longer real estate, nor low-end contract exports—but rather the international expansion of Chinese brands…

…We’ve studied many outbound brands—MINISO, Pop Mart, Xiaomi, innovative pharmaceuticals, EV makers, short-form video platforms, games—and what we see is not a fleeting opportunity but a fundamental shift. It reflects the rise of talent, capabilities, and global competitiveness.

The era of debt-fueled growth is over. Today, growth comes from improvements in corporate strength, from truly competitive products, and from globalized operations…

…Still worried about China’s government debt? Concerned that the debt expansion of 2010–2020 is no longer sustainable? TSF growth slowing down? PPI still falling? Property prices not yet bottomed? Premium liquor sales still sluggish?

We have data and research to show that many former economic pillars and core assets are undergoing a transition. These variables are no longer fatal risks to the Chinese economy or its markets.

5. Wall Street’s Big, Bad Idea for Your 401(k) – Jason Zweig

Money managers are in a desperate race to stuff illiquid, so-called private-market assets into funds anyone can buy, including your 401(k). They say we all can earn high return and low risk with nontraded “alternatives” like private equity, venture capital and private real estate…

…Bluerock Total Income+ Real Estate is an “interval fund.” This is a structure that generally allows investors to buy as many shares as they wish at any time—but only to sell limited amounts at predetermined intervals, typically 5% of shares per quarter…

…Because private assets don’t trade, it’s the fund managers—not the market—that determine what they’re worth. That enables the managers to report much fewer and lower fluctuations than public funds do. Then they get to declare that private funds are low risk.

That’s ridiculous. In the real world, risk is the chance of losing money, which has nothing to do with how often prices are reported…

…Owning an alternative fund is a lot simpler than selling it. When you own it, you might take the manager’s valuations for granted, even if that’s a bad idea. When you sell it, the valuation matters—a lot. That’s a risk.

Until now, investors have been able to sell their shares back to each of these two funds at “net asset value,” or what the manager claims they’re worth. Even if other investors might disagree with some of those valuations, the manager has stood behind them.

That works until the number of people looking to sell swells and the managers can’t raise money because they are holding illiquid or distressed assets…

…The answer for these two funds, and for the alternative-asset industry writ large, is to move assets to public markets. There, the price will be set by what other investors—not the managers—believe the assets are worth. If that’s less than NAV, that’s mainly a problem for investors in the fund, not the managers…

…Most of the Bluerock fund’s holdings are stakes in other private real-estate portfolios. If it lists and ends up trading at a discount to net asset value, that might signal that the public market doesn’t believe the private valuations on dozens of these funds.

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Disclaimer: The Good Investors is the personal investing blog of two simple guys who are passionate about educating Singaporeans about stock market investing. By using this Site, you specifically agree that none of the information provided constitutes financial, investment, or other professional advice. It is only intended to provide education. Speak with a professional before making important decisions about your money, your professional life, or even your personal life. We currently have a vested interest in Alphabet (the parent company 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 03 August 2025:

1. The Jamie Dimon Interview – Ben Gilbert, David Rosenthal, and Jamie Dimon

Ben: It seems like your philosophy is that the worst thing will happen. So just plan for it. Don’t say, oh, we’re good as long as this crazy, insane four Sigma event doesn’t happen. You’re like, no. That will happen, and it happens often.

Jamie: Yeah. When I look at it, when I do stress tests and a risk for high yield, I remember getting to J.P. Morgan and going through the risk books. Their stress test was that high yield would move 40%, the credit spread. That time was at 400 or whatever it was. That means 560.

I said, no. Our stress test is going to be worst ever. Worst ever was 17%. They said, that’ll never happen again. The market’s more sophisticated. Well, in 2008, it hit 20% and you couldn’t have sold a bond. There was no market. So those things do happen.

The point isn’t that you’re trying to guess them. The point is you can handle them, so you continue to build your business. I always look what I call the fat tails and manage that we can handle all the fat tails. Not the stress test the Fed gives us, but all the fat tails.

Markets down 50%, interest rates up to 8%, credit spreads back to worst ever. Of course, your results will be worse, but you’re there. The thing about financial services, leverage kills you. Aggressive accounting can kill you, which a lot of companies do. Also, confidence. If you lose money as a financial company—I always knew this too—the headlines are people read that. If they’re a line on putting their money with you, they look at that difference.

Ben: They lose trust.

Jamie: They lose trust, and that’s what’s caused you’ve seen runs on banks. You saw some recently because people take their money out.

Ben: One, there’s a thing that you just said, which is that you might do worse, but you’re there. There’s this trade-off that you make where you’re less profitable in the short-term, but at least you stick around.

If you look back at the companies that you’ve run—Bank One, J.P. Morgan Chase—is that true in the good years that you’ve actually been less profitable than those who are risk on?

Jamie: A little bit. You’re saying that if you look at the history of banks from up until 2007, a lot of banks were earning 30% equity. Most of them went bankrupt. We never did that much. But in 2008 and 2009, we were fine and they weren’t.

But you want to build a real strong company with real margins, real clients, conservative accounting, where you’re not relying on leverage. It’s very easy to use leverage to jack up returns in any business, but in banking it could be particularly dangerous…

…David: And 2006 on Wall Street is like, go, go, go baby. It’s like the 1980s all over again.

Ben: I think you had the same incentives as everyone else, but you behaved very differently. Am I missing something? Did you have the same incentives or did you—

David: You pulled J.P. Morgan back hard on the risk side in 2006.

Jamie: I did. There were cracks out there in 2006. You may remember the quants. There started to be a quant problem late in 2006. We definitely saw subprime getting bad. I pulled back on subprime. I wish I had done more, because if you look at what I did, you say, okay, well you saved half the money, but you would’ve saved more.

David: You still had some losses.

Jamie: Yeah, but we also had, I’m going to say less, maybe a third of the leverage of the big investment banks and a lot more liquidity. So in 2006, I started to stockpile liquidity, and looking at the situation, I was quite worried. You may not remember this, but the leverage, because of accounting rules and Basel III, Basel I, investment banks, particularly the big investment banks, went from 12 times leverage to 35 times leverage. And it was go, go. The CMOs, the bridge loans, the whole thing.

In 2007, the bridge book of Wall Street was $450 billion. Today it’s $40 billion. J.P. Morgan can handle the whole $40 billion today though we’re not the $40 billion today, and they were much more leveraged deals. A lot of them fell apart, collapsed. Of course, and that was before you had the collapse in the mortgage mortgage, which really took down a lot of these banks.

Ben: But you did have the same incentives and you had the same access to information that a lot of these other folks did, but you didn’t blow up. What explains this? Because usually, behavior follows incentives.

Jamie: Well, first of all, if you work for me, I would tell you I don’t care what the incentive is. Don’t do the wrong thing. Don’t do the wrong thing to the client. If you’re the client, how would you want to be treated? I had gotten rid of, I mentioned that one risk thing. There were multiple risk things like that. They were being paid to take the risk.

David: You were telling us about the auto loan business.

Jamie: Yeah, but they’d be being paid. But the second I put in all these new risk controls, all of a sudden you weren’t making money by taking that leverage, because I was looking at how much capital it can actually be deployed if things get bad. So I was looking at earnings through the cycle, but very importantly, all of these investment banks were doing side deals, private deals, three year deals, five year deals, I got rid of almost all of them.

David: This is for comp with senior bankers.

Jamie: Almost all of them. Today at J.P. Morgan Chase, we do do things—and I know some of my partners in the room here—but we all know about it. There are no winks. There are no nods. There are no side deals. There’s almost no one paid on a particular thing, because if you’re paid on a particular thing, you can do the wrong thing, meanwhile not helping the company manage its risk or something like that. So we change the incentive programs.

I’m quite conscious about incentive programs that they don’t create mis misbehavior. But it’s also very important if you’re in a company and you say the incentive programs do that, you should tell the company. This incentive plan is not incentivizing the right behavior versus the customer. And a lot of it was leverage.

If you look at the leverage in some of these securitization and mortgage books, if you have 30 times leverage and you’re getting 20% of the profits, you’ll go to 40 times leverage. It literally will add 25% to your bonus. So I got rid of the profit pool 20% and the leverage. I lost some people too in the meantime…

…[Jamie:] If you look at the financial services, very often it’s the new products that blow up. It takes a while. They haven’t been through a cycle. You had that with equities way back in 1929, you had it with options, you had it with equity derivatives, you had it with mortgages. Even Ginnie Maes at one point blew up, even though they’re government guaranteed.

David: Arguably, you had it with quant and with LT and CM.

Jamie: It happened with quant. It happened with leveraged lending. People then become more rational how they run these balance sheets now they think through the risk.

Ben: I have to ask you, is this private credit today?

Jamie: I don’t really think so. It’s $2 trillion. It’s grown rapidly. That’s an issue. The other thing about Mark is there are some very good actors in it who know what they’re doing. Customers like the product. I always say, well, the customers like it.

But there are also people who don’t know what they’re doing, and it’s grown rapidly. There may be something in there would become a problem one day. I don’t think it’s systemic. That $2 trillion, the mortgage market, when the time it blew up was (I’m going to say) $9 trillion, and a trillion dollars was lost.

David: A trillion dollars was more than a trillion dollars back then.

Jamie: Yeah, a lot of these private credit are not leveraged like that. But that doesn’t mean there won’t be problems. It’s slightly different. You look at the whole system. There are other things out there that are leveraged that can cause problems. Of course, people take secret leverage in the ways you don’t necessarily see it.

Ben: What are some of these in your mind that are potentially problematic today?

Jamie: When you look at asset price, they’re rather high. Now, I’m not saying that’s bad, but if today PEs were 15 as opposed to 23, I say that’s a lot less risk. A lot less to fall, and you have some upside. I would say at 23, there’s not a lot of upside, and there’s a long way to fall. That’s true with credit spread…

…Ben: Silicon Valley Bank and First Republic both fail. You’re there again. Did you see it coming? What lessons did you learn from how 2008 went that you could apply in 2023? Obviously you bought First Republic.

Jamie: Silicon Valley Bank did some very good stuff. They both had something unique that we didn’t know at the time. I’m going to call them concentrated deposits. Not uninsured because people missay that concentrated, so a lot of venture capital.

What happened with Silicon Valley Bank and First Republic is some of these large venture capital companies—hundreds of them, maybe a thousand—told their constituent clients that they invested in, who all banked in the Silicon Valley and First Republic, the banks aren’t safe, get out, and they all removed their deposits.

Silicon Valley Bank (I think) had $200 billion deposits, $100 billion in one day. That caused the problem. But they also had other problems. They didn’t have proper liquidity, they didn’t have their collateral posted at the Fed, and they had taken too much interest rate exposure.

The interest rate exposure was hidden by accounting. It was called held to maturity, where you don’t have to mark even treasuries to market. I always hated held to maturity, but it gives you better regulatory returns and stuff like that. But when that held to maturity, if you said what’s the tangible book value of one of these banks, and you said it was 100, well all of a sudden it was 50 if you just marked that one thing to market.

Now you’re into judgment land. At what point, if you saw a bank where just that one mark had the tangible book value drop to 40 or 30 cents to a dollar, would you panic? I would’ve said, that’s too much risk.

The regulators helped us because they said rates are going to stay low forever. So these banks bought a lot of 3% mortgages. When rates went up to 5% worth 50 cents on the dollar, that was it. They took too much instrument exposure known to management, known to the regulators, and fixable.

2. How Bread vs Rice Molded History – Tomas Pueyo

This means that rice nourishes families on half the land that wheat requires. Which means population density in rice areas can be twice as high as in wheat areas, or four times with double cropping.2 A hectare of land can feed 1.5 families with wheat and 6 with rice.

Yet rice paddies also require a lot of work—twice as much as wheat. And that work is almost year-round: preparing paddies, raising seedlings in nurseries, transplanting every single seedling by hand into flooded fields, managing water, pumping it,3 weeding,4 harvesting, and threshing—often followed by a second rice crop or a winter crop. These tasks peak during transplanting and harvest, creating critical seasons where a huge amount of work must be done in a short window of time…

…Wheat farming historically had a more seasonal rhythm with periods of relative quiet. Wheat is typically sown in the fall or spring and then mainly just left to grow with the rain. Aside from episodic weeding or guarding the fields, there was less continuous labor until harvest time. Harvest itself was a crunch period requiring many hands with sickles—European villages would collaborate during harvest, and farmers might hire extra reapers.

These differences made these regions diverge across politics, culture, and economy…

…Wheat grows in drier, colder areas than rice and requires much less labor, but also produces less calories per unit of land than rice. As a result, rice areas had:

• More population density
• Stronger centralized states
• A psychology and cultures that foster social harmony and collaboration

Meanwhile, wheat encouraged the colonization of the New World, allowed it to grow its wealth through farming fast, and accelerated the development of the Industrial Revolution, which increased the economic divergence between wheat and rice areas.

In other words, climate determined crops, which then heavily influenced our societies. Even decades after most of us have stopped farming, these effects carry into our subconscious cultures.

3. Are Diamonds Even a Luxury Anymore? De Beers Reckons With Price Plunge – Jenny Strasburg and Suzanne Kapner

Now diamonds can be made in labs that mimic the earth’s extreme pressure and temperatures, but for a fraction of the price. A decade ago, such man-made gems were novel. Today they are mainstream, and increasingly challenging the perception of diamonds as a luxury accessory.

Walmart sold its first lab-grown diamonds in 2022, but now the stones make up half of its diamond jewelry assortment.

Signet Jewelers, which says it is the world’s largest retailer of diamond jewelry, with brands that include Kay Jewelers, Zales and Jared, is partnering with De Beers to extol the virtues of natural diamonds in a new marketing campaign. But last month, Signet said it, too, has been adding more lab-grown diamonds to its fashion jewelry, which was among the factors helping to pull the company out of a prolonged sales slump…

…More than half the engagement rings purchased last year in the U.S. had a lab-created diamond, a 40% increase compared with 2019, according to a survey of nearly 17,000 U.S. couples by wedding planning website The Knot…

…Manufactured diamonds are 100% carbon, with the same hardness and sparkle of the original. Nevertheless, De Beers’s future depends on consumers who believe that authenticity can’t be made in a lab…

…De Beers gets its name from two Dutch-Afrikaner brothers, Diederik Arnoldus de Beer and Johannes Nicolaas de Beer, who settled in South Africa and discovered diamonds on their farm in the late 1800s.

De Beers grew to control some 90% of the world’s diamond trade. When diamond demand collapsed during the Great Depression, De Beers hired the advertising agency N.W. Ayer, which convinced Hollywood actresses to wear diamond rings. One of its copywriters in 1947 came up with the now famous tagline “A Diamond is Forever.”

Over coming decades, De Beers broadly succeeded in dictating how much should be spent on a diamond engagement ring: “Isn’t two months’ salary a small price to pay for something that lasts forever?” asked a 1980s De Beers ad…

…Even gem experts need specialized machinery to tell the difference between quality lab-grown and mined diamonds. De Beers is now trying to draw more attention to the hard-to-see differences, by asking jewelers to shell out $9,500 for a new diamond-testing device called DiamondProof.

The device is about the size of an air fryer and designed to be displayed on jewelry-store counters. It takes just a few seconds to show color-coded results: If the stone’s image glows blue, it’s natural—a result De Beers says it can guarantee. If it glows yellow, it’s lab-grown or needs further testing…

…Sales of lab-grown diamonds at Walmart, the country’s second-largest fine jewelry seller behind Signet—according to National Jeweler magazine—soared 175% in 2024 compared with the prior year…

…Signet had been more reluctant to jump on the lab-grown bandwagon than other middle-market jewelers, which some analysts say contributed to a prolonged sales decline, plunging stock price and a large shareholder who had pushed for a sale of the company.

Signet Chief Executive J.K. Symancyk, who took the helm in November, laid out a new strategy in March that includes pushing more heavily into lab-grown diamonds for fashion jewelry like tennis bracelets, earrings and necklaces, while aiming to protect the allure of natural stones for milestone purchases like engagement rings.

Sales of fashion jewelry with lab-grown diamonds increased 60% in the most recent quarter, compared with a year ago, one factor that helped the company’s overall sales return to growth for the first time since April 2022.

He added that nearly two-thirds of Signet’s customers still prefer mined diamonds for special occasions like anniversaries and engagements. “We see natural diamonds as lasting and enduring,” Symancyk says. “Fashion trends change.”…

…The influx of lab-grown diamonds has pushed prices down for both types of stones.

The retail price of a 1-carat lab-grown diamond has plunged 86% since the beginning of 2016, to about $745, Zimnisky estimates. The price of the same size natural diamond is down 40% over that period to $3,925. Back in 2016, there was only about a $1,000 difference between a 1-carat lab-grown and natural diamond. A natural diamond now costs about five times as much as man-made stone.

4. Trump’s Commerce Secretary Loves Tariffs. His Former Investment Bank Is Taking Bets Against Them – Louise Matsakis and Zoë Schiffer

Cantor Fitzgerald, a financial services company led by the sons of US commerce secretary Howard Lutnick, is creating a way for investors to bet that President Donald Trump’s signature tariffs will be struck down in court…

…Lutnick ran Cantor Fitzgerald for nearly 30 years until he was confirmed by the Senate in February, when he turned over control of the firm to his sons, Kyle and Brandon, who are both in their twenties…

…But the investment bank that made Lutnick a billionaire is now letting certain clients wager that Trump’s tariffs will eventually be ruled unlawful, at which point companies that have paid the import duties can apply to get their money back.

In a letter seen by WIRED, a representative from Cantor said the firm was willing to trade tariff refund rights for 20 to 30 percent of what companies have paid in duties. “So for a company that paid $10 million, they could expect to receive $2-$3 million in a trade,” the representative wrote. “We have the capacity to trade up to several hundred million of these presently and can likely upsize that in the future to meet potential demand.”…

…“Secretary Lutnick knows nothing about this decision because he has no insight or strategic control over Cantor Fitzgerald,” wrote Kristen Eichamer, press secretary for the Department of Commerce, in an email to WIRED. “He has fully complied with the terms of his ethics agreement with respect to divesture and recusals and will continue to do so.”

Trump announced in February that the US would put steep tariffs on goods from Mexico and Canada under the International Emergency Economic Powers Act (IEEPA). He widened the trade war in April to include nearly every nation that sells goods to the US, which Trump said would now be subject to “reciprocal” tariffs ranging from 10 to 50 percent.

In response, there was a flurry of lawsuits, including one from a group of small businesses that sued the Trump administration in the US Court of International Trade, arguing that the president exceeded his authority and the tariffs should be ruled illegal. The trade court sided with the plaintiffs, but the Trump administration appealed the decision, and the appeals court allowed the duties to remain in place while the case is pending.

5. Yet Another Munger Masterclass: The 2003 Wesco Financial AGM – Kingswell and Charlie Munger

(7) “The central idea of a margin of safety when you’re making investments will never be obsolete. And the idea of making the market your servant and not your instructor will never be obsolete, either. Those two basic ideas of Ben Graham are basically reality cubed. The idea of being objective and dispassionate, which was also in Graham, that will never be obsolete. So Graham had a lot of ideas that were wonderful.”…

…(8) “I’ve picked up Ben Graham’s main ideas and discarded the practices he used that don’t suit me. I don’t want to go around now buying stocks at a big discount from liquidating value, of businesses that are mediocre or worse, run by people I don’t like, and sit there saying no matter how horrible it is to watch, it will bounce by 25%. I don’t think that approach would work very well given our size of capital. So it’s natural to follow my temperamental attraction toward the better businesses.”…

…(12) “A lot of people rise to power in big corporate bureaucracies who are very nice people and good at doing things in a fairly limited way, but whose general powers of capital allocation are inadequate. And, of course, those who are advising them — the investment bankers, the consultants, and so forth — will mislead you 95% of the time.”…

…(14) “If you could actually sit down and talk to a key manager one-on-one for an hour or so — and if you’re a very smart person — that could be a significant plus. On the other hand, I’m enough of a cynic to believe an intelligent person might be helped 60% of the time and the other 40% of the time he might be misled. So, on balance, whether it’s worth the time, I can’t tell you.”…

…”Years ago”, he said, “we were interested in a particular stock and Warren went and talked to the CEO for two or three hours at lunch — and he thought he was the biggest horse’s ass he’d ever seen. So we sold every share. Well, the thing compounded at 15% per annum for about 20 years thereafter. It finally got a big denouement [and dropped in price], but the idea that meeting the management will always help you… Well, that always amused me — to watch that stock galloping upward.”

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Disclaimer: The Good Investors is the personal investing blog of two simple guys who are passionate about educating Singaporeans about stock market investing. By using this Site, you specifically agree that none of the information provided constitutes financial, investment, or other professional advice. It is only intended to provide education. Speak with a professional before making important decisions about your money, your professional life, or even your personal life. We currently do not have a vested interest in any companies 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 27 July 2025:

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

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

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

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

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

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

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

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

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

2. How It’s Done – Doomberg

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Need an example?

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

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

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

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

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

It’s possible, of course.

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

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

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

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

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

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

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

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

Here are the articles for the week ending 20 July 2025:

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

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

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

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

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

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

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

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

…Romans loved luxury goods:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Then, the British introduced opium smoking in China…

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Here are the articles for the week ending 13 July 2025:

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

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

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

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

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

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

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

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

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

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

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

Joe: It’s done insanely well.

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

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

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

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

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

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

Tracy: No using your phones.

Joe: And ChatGPT.

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

2. Creating therapeutic abundance – Jacob Kimmel

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

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

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

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

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

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

…Efficacy failures can broadly occur for two reasons:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

That’s exactly what’s happened:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

There are no bad risks in insurance — only bad rates

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Here are the articles for the week ending 06 July 2025:

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

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

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

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

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

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

Suddenly, that’s changed:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

That leaves payments:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Here are the articles for the week ending 29 June 2025:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Everyone I spoke with on both sides agreed that:

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

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

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

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

5. The Speed of Patience – Paul Higgins

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

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

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

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

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

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

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

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

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

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

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

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