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

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

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

Here are the articles for the week ending 12 April 2026:

1. America’s AI Build-Out Hinges on Chinese Electrical Parts – Emily Forgash and Akshat Rathi

Almost half of the US data centers planned for this year are expected to be delayed or canceled. One big reason is the shortage of electrical equipment, such as transformers, switchgear and batteries. They are needed not just for powering AI, but also for building out the grid that is seeing increased consumption from electric cars and heat pumps. US manufacturing capacity for these devices cannot keep up with demand, and the scarcity has caused data center builders to rely on imports…

…Data centers consuming as much as 12 gigawatts of power are supposed to come online in 2026 in the US, according to analysts at market intelligence firm Sightline Climate, who will be releasing a new report in the coming weeks. However, only a third of that is currently under construction, Sightline estimates…

…Electrical infrastructure adds up to less than 10% of the total cost of the data center, but it’s impossible to build the operation without it. “If one piece of your supply chain is delayed, then your whole project can’t deliver,” says Andrew Likens, Crusoe’s energy and infrastructure lead. “It is a pretty wild puzzle at the moment.”…

…Though few companies are eager to talk about it, the US has been outsourcing its manufacturing to other countries, primarily China, for decades. That has contributed to a significant shortage of electrical components in the US, says WoodMac’s Boucher…

…While most of the US’s transformers come from Canada, Mexico and South Korea, US utilities imported more than 8,000 high-power transformers in 2025 through October from China, up from fewer than 1,500 imported in all of 2022, estimates WoodMac’s Boucher. This build-out “is going to be highly dependent on the import market,” he says.

Once transformers lower the voltage of electricity so it can be used in data centers, it then needs to be distributed across the data center safely. That’s done through switchgear, which includes circuit breakers and fuses. There too, data center developers are seeing delivery delays – though not as extreme as the timelines for transformers.

Equinix Inc.’s solution is to commit at least $350 million to support Hanley Energy’s new manufacturing facility in Ireland, which will make switchgear and other data center components. Equinix expects to achieve 10% to 15% faster lead times as a result.

Crusoe’s answer to that shortfall has been to pre-order lots of the equipment. That means spending many millions of dollars on supplies before the company even knows it has an order to fill, but it’s proved a winning strategy. Recently, Crusoe also began manufacturing their own switchgear…

…The share of US imports of transformers and switchgear from China has declined steadily in recent years – although for specific types of equipment that share is still hovering around 30%. The Chinese share of battery import volumes remains stubbornly above 40%.

China dominates the supply of electrical equipment because it controls so many parts of the supply chain, from materials to processing to manufacturing, and the gulf between China and the US is set to widen. In its new five-year plan, the Asian giant revealed last month that it will double down on building out its grid with renewables, while the Trump administration has dismantled policies to deploy solar and wind power.

2. “Founder Mode” Complacency – Abdullah Al-Rezwan

When DeepMind was plotting to extricate themselves from Alphabet almost a decade ago, Pichai was prescient enough to foresee AI’s paramount importance in their core business…

…As these negotiations became more tense over time, all the big guns of Alphabet planned to meet to resolve the issue at hand. Alas, some big guns didn’t seem to appreciate what was at stake. From the book:

When the two sides met again, the conversation underscored the gulf between them. Hassabis and Suleyman argued that DeepMind did not fit under Google’s umbrella: Its mission was AGI, not consumer‑internet products. Pichai objected that AI was central to his vision for Google, and that he would not allow his scientific bench to be depleted. Hassabis had hoped that Larry Page would weigh in on his side and push the Alphabet plan to a conclusion. But Page showed up for the meeting two hours late, and Sergey Brin was even later. Their version of what later came to be known as “founder mode” was that they were nowhere to be found, disproving the Silicon Valley mantra that founders deserve the right to control their companies indefinitely. With Page and Brin effectively checked out, Pichai was the man DeepMind had to deal with.

I have been thinking about the aforementioned excerpt for the last couple of days. If you glanced at my portfolio, it’s not difficult to see that I drank my fair share of kool-aid of “founder mode”. Perhaps fittingly the “founder mode” propaganda originated from a founder himself: Brian Chesky. The more I ruminated over “founder mode”, the more I came to the conclusion that there is a glaring missing aspect in “founder mode” mantra: Complacency.

It is telling that Chesky proudly recalls every chance he gets about how he figured out during Covid that Airbnb doesn’t need to do search advertising; as an investor I was actually a bit alarmed that he was running Airbnb pre-pandemic without paying close attention whether his advertising dollars was being deployed with appropriate ROAS guardrail. I can guarantee you that despite operating in “Manager Mode”, Glenn Fogel at Booking was looking at advertising ROI with a microscope and he certainly didn’t need a global pandemic to remind him how to deploy his precious advertising dollars at Booking.

3. A token is not a fixed unit of cost – Anjali Shrivastava

We only consider token count as the static linear meter because we inherited the logic from inference APIs. But, a token does not represent a fixed unit of work.

This is obvious to anyone who works in inference, but if you’re used to calculating compute budgets based on linear API rates, it takes a second to sink in.

The intuition is grounded in the autoregressive nature of the transformer: Attention is quadratic with respect to current context size…

…In layman’s terms, the language model is looking at every previous token in the context window before generating a new token, which means inference APIs are linearly pricing fresh tokens whose compute cost scales quadratically.

The scaling law for compute is likely not purely quadratic, given optimizations like caching and compacting context. But no matter what, the underlying compute cost per token grows with context length. The Nth token in a conversation is an order of magnitude more expensive than the first.

There’s signs that per-token pricing breaks down at scale: both Anthropic and Google charge different rates based on prompt length…

…Traditional SaaS has variable costs too (like hosting, customer support and third-party service costs). But these costs follow the law of large numbers, and are normally distributed at scale. You can set a single subscription price that covers this average cost, plus a comfortable margin to absorb tail risk.

In the case of AI software, it is likely that these variable costs are fat tailed. The law of large numbers assumes finite mean and i.i.d. samples, but AI software has at least one dimension with infinite first moment and non-stationary tails. The sample mean keeps wandering instead of converging…

…Margin collapse is the first and most obvious symptom of the problem. Cursor’s repricing exposed poor margins, and we also learned that Replit’s margins are volatile. And there is ample evidence that Anthropic is losing money on its subscriptions.

Each layer of the aggregate cost curve is highly variable, and the more you scale, the higher the probability that these tail risks can compound…

…Subscriptions misprice intelligence, and much of the industry recognizes this, but now we can rigorously explain why.

Traditional SaaS pricing mirrors the physics of stable software, but AI introduces high variance that breaks each of these laws…

…High variance in costs necessarily constrains demand; today, the constraints are reactive.

To safely cushion from unbounded costs, a business model must price in the variance or be well above the true cost on average. Ideally by anchoring price to value delivered instead of token cost; but value delivered also happens to be highly variable and subjective. At the same time, there’s structure to value: reliability, relevance, actionability.

The key insight is that margin squeeze and resource misallocation are two sides of the same problem. Solving one side of the equation should solve the other. If you can measure the value delivered, you can price that instead of raw compute. And if you can price outcomes in terms of value delivered, you can budget the exact amount of compute and data that maximizes profit on each task.

So the layer that owns the meter also decides how much compute and data to deploy and keeps the spread between cost and price. Today that meter sits inside the model; tomorrow it could sit inside an orchestrator that plans the whole workflow.

4. Why You Should Wait Out AI’s Super-Spending False Start – Merryn Somerset Webb

The second part, the data on which all LLMs are trained, is not. Its supply is limited. Up to ChatGPT4, the internet provided enough data for each new iteration to be better. But that version was completed a few years ago, trained on the lot. There is little more for new models to train on.

The data on the internet might have expanded over the last few years, but not in a particularly helpful way. Much of it has been produced by other LLMs: train your new model on that and you might end up degrading it. Why? Because LLMs are horribly prone to errors (confabulations or hallucinations), which means they can’t give us what we most need from them: accuracy.

An LLM is not a continuous learning machine. Its knowledge stops with its training. It also isn’t deterministic (like, say, a calculator), says AI expert Janusz Marecki (who I interviewed for a podcast this week). It knows nothing with certainty. It simply “rolls the dice” on what the next word in a series should be, giving you its best guess. The answer you get is an approximation, not a series of facts. Worse, the more complicated the task in hand, the more the errors compound. Possibly even worse, the LLM can’t tell you how likely it is that there are errors. How would it know?

These problems aren’t going to go away. They are irredeemable systemic flaws in the product.

5. Switzerland – Europe’s overlooked activist opportunity – Swen Lorenz

Switzerland is famously conservative and generally averse to outsiders telling it what to do.

This is also reflected in its corporate landscape.

Even though the country is broadly open to foreign investment, there have long been numerous mechanisms allowing companies to keep outside influence under tight control.

Some Swiss companies require shareholders to be registered by name, with board approval needed for new registrations. This has led to cases where outsiders were refused registration – and “outsiders” can even include Swiss citizens from a different region.

Other companies cap voting rights per shareholder or maintain super-voting shares that remain tightly held by local incumbents…

…The 2023 reform of Swiss corporate law wasn’t widely noticed, not least because attention was focused on events in Ukraine and the aftermath of the pandemic.

Until then, a shareholder needed to represent 10% of share capital to add an agenda item for a vote at the annual general meeting.

For publicly listed companies, this threshold has now been reduced to just 0.5% – a far more attainable level.

Similarly, a shareholder with 5% can now requisition a shareholders’ meeting, compared to 10% previously.

Just as importantly, the broader acceptance of active shareholders has evolved…

…Finanz und Wirtschaft, Switzerland’s leading German-language business daily, carries significant influence among corporate executives. In an article published on 18 September 2025, the paper noted how “activist investors are transforming from bogeyman to catalyst”…

…Patrick Fournier is an active investor based in Zug. We met several years ago at his family home to discuss our shared interest in frontier markets.

Today, his focus has shifted closer to home.

He allowed me to share the following:

“We have progressively sold all our portfolio of foreign shares and are now focusing on Swiss small & mid cap. We see huge value opportunities on this segment. We intend to become a little ‘activist’ as it is now possible with only 0.5% of capital in a listed company (far lower than the previous 10%) to add some proposition at the agenda of the annual general meeting of shareholders. This will wake up the Board of several companies, including regarding the dividend (payout) policy. As a result, we are in front of a ‘rerating’ (multiple expansion) of this segment.”…

…BVZ held its annual general meeting on 8 April 2026, and the results were telling.

Some 287 shareholders attended, representing 110,328 out of 197,278 shares outstanding (with one shareholder alone holding 56,000 shares). Alarick’s proposal to increase the dividend from CHF 18 to CHF 50 received 14.5% support and was rejected by 83.8%. As a result, the board’s proposal to raise the dividend from CHF 16 to CHF 18 was approved. With earnings per share of CHF 151, this implies a payout ratio below 20%. The proposal to initiate a share buyback programme received 16.67% support and was rejected by 82%, and therefore did not pass.

What may sound like a defeat is, in fact, the equivalent of an earthquake. In Switzerland’s highly consensus-driven corporate culture, such a level of shareholder dissent represents a clear wake-up call for management.

The market agreed. On the day of the meeting, the share price closed at an all-time high of CHF 1,550, up 67% over the past 12 months.

As the recent share price performance suggests, even raising one’s voice in a constructive manner can create value for shareholders in Swiss companies.

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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. 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 05 April 2026:

1. Energy’s Moment – Abdullah Al-Rezwan

I had this naive assumption that since the US has now become a net oil exporter and China remains heavily dependent on imported oil, any oil shock would be net negative for China far more than it would affect the US.

So, it was surprising for me when I noticed that China was actually ahead of the US in terms of “total insulation factor” when it comes to global oil & gas shocks. The “total insulation factor” indicates the share of a country’s useful final energy that is less exposed to global oil and gas shocks. JPM calculated it by adding together a country’s reliance on four specific energy sources: domestically produced gas, domestically produced coal, nuclear power, renewables (such as biofuel, hydro, wind, solar, and biomass). China has a total insulation factor of 76%, while the US has a total insulation factor of 70%. China scores higher primarily because of its massive reliance on domestic coal (54% of useful final energy), which accounts for a larger share of its energy mix than the US’s primary domestic buffer: natural gas (44.5% of useful final energy).

Even though China is the world’s largest oil importer nation, oil imports make up 13% of China’s primary energy consumption. When you combine all oil consumption and imported gas, it only accounts for 20% of China’s primary energy. 

2. A Sinister Raise, a Bitter Press Release, and Five Other Weird SEC Filings – Andrew Walker

EMPD is a digital treasury company focused on Bitcoin. Like most digital treasury companies, they’ve traded for a discount to NAV basically this entire year; for most of the past month, they’ve been trading around 80-90% of NAV (you can see a real time NAV calc here).

Towards the end of March, the company announced a $25m equity raise. The raise was priced at a premium to both NAV (it priced at 103% of NAV) and the market price (again, the company trades for <90% of NAV); on top of that premium raise, the company noted they’d continued to buy back stock at a discount to NAV. Read that sentence again: a company trading at a discount is buying back shares and somehow raising capital at a premium to NAV? Nirvana for shareholders, right!?!?!

Au contraire, mon frère!

EMPD didn’t just issue stock in the equity raise; for every share they issued, they gave the buyer a four year warrant struck at $6.27/share (~20% above NAV). Those warrants have enormous value; BTC currently trades with ~50 vol. EMPD is a levered BTC treasury company, so it should have even more volatility than BTC (EMPD’s option chain is extremely thin but points to volatility well over 100). ChatGPT tells me that a four year warrant that’s 40% out of the money with 100 vol is worth ~65% of the spot price…. for EMPD, that means each warrant was worth ~$2.90/share. So, yes, the headline number EMPD raised at was $5.39/share, but if you adjust for the value of the warrant EMPD raised money at an effective price of ~$2.50/share while the stock was trading at ~$4.50/share. An absolutely awful trade.

Why would EMPD raise money like this? Well, I’m not in the board room, so I can’t tell you with absolutely certainty…. but I’d suggest it’s likely a board entrenchment maneuver. EMPD is currently in a rarely seen double proxy fight where two separate shareholder groups3 are trying to replace the board with a more shareholder friendly group4. EMPD’s press release announcing the raise notes the raise was bought by a “current institutional investor” in EMPD; my guess is EMPD went to a big shareholder and said “hey, agree to keep the current board in place, and we’ll give you a big slug of stock to vote and toss in a ton of warrants to make the whole thing worth your while.” …

…Today, BNTX has just shy of $20B in net cash, and while the COVID franchise is obviously dwindling the company has a ton of promising other drugs / readouts coming over the next few years….

Perhaps those readouts work, perhaps they don’t. I have no idea! But it’s a pretty promising set up…. which is why it’s so wild that BNTX announced that their co-Founders / top executives were leaving BNTX to start a “next-generation mRNA innovations” company. What’s even crazier is that BNTX will be contributing their mRNA assets to the new company!

Why is this so crazy? It’s absolutely ripe for conflicts of interest! BNTX could have spun out the mRNA assets to all their shareholders and put their founders / exec team in control of the new company. That would have been a fair and equitable way to do a start up. Instead, it seems BNTX will contribute the mRNA assets to the new company in exchange for a piece of that company. How are the mRNA assets going to get valued in that transaction? Given the founders / CEOs are going to the new company, it’s not hard to see how they might want to give the new company a boost by paying BNTX far under market value for the mRNA assets.

3. 2023 – Dean W. Ball

Intelligence is a tremendously useful capability, but it is not the bottleneck on all human progress, and, crucially, an extreme amount of intelligence does not equate to omniscience. Intelligence is not knowledge. Aristotle was surely more intelligent than I am, but he was not more knowledgeable, including even about many of the topics to which he devoted his treatises. This is why I am confident I would score better on a standardized test in biology or physics than Aristotle, despite him being one of the West’s originators of those fields of inquiry.

In a similar vein, imagine a newborn baby that was guaranteed to grow into an adult with an astoundingly high IQ (say, an IQ of 300, or 500, or 1000), but raised by Aristotle in Ancient Greece. Do you expect that the baby would mature into an adult that invents all modern science within the span of a few years or decades? Eliezer Yudkowsky does. Indeed, he describes contemporary humans trying to grapple with superintelligent AI as equivalent to “the 11th century trying to fight the 21st century.” I, on the other hand, strongly doubt that our imaginary high-IQ baby would invent all modern science from first principles. First principles do not have unbounded explanatory power.

In the end, most interesting things about the universe cannot be inferred from first principles. Imagine, for example, that you came upon a dry planet with mountain ranges but no bodies of water. But imagine that you knew, magically, that the planet would soon gain an atmosphere and thus precipitation, seasons, and the like. Suppose you have a superintelligent AI with you, and you show it the map of the planet as it is, and ask it to predict where all the planet’s rivers, lakes, and oceans will lie 50 years hence, after the planet gains regular precipitation. You don’t ask it to predict “generally speaking, where the bodies of water might end up,” but instead to predict exactly where they will be.

I would submit that there is no computational process which can arrive at the end of this natural process faster than nature itself. In other words, there is no pattern or abstraction you can create that allows you to speed ahead to the end of the process, and thus there is no amount of intelligence that gets you to the correct solution faster than nature on its own. You just have to wait the 50 years to find out. This is what the scientist Stephen Wolfram describes as “computational irreducibility.” Understanding this notion deeply is key, I think, to understanding the limits of intelligence. It should therefore come as no surprise that the best debate I’ve ever heard about AI existential risk was between Wolfram and Eliezer Yudkowsky.

Computational irreducibility comes into play anytime you are interacting with a complex system (though this is not to say that computational irreducibility is intrinsic to all interactions with a complex system). Every natural ecosystem, cell, animal, and economy is a complex system. While we have all manner of methods to predict what will happen when a complex system is perturbed (we call these things “physics,” “biology,” “chemistry,” “economics,” and the like), none of those methods is perfect, and often they are far from it.

The way we build better models of the world does not usually resemble “thinking about the problem really hard.” Generally it involves testing ideas and seeing if they work in the real world. In science these are generally called “experiments,” and in business sometimes we call these “startups.” Both take time and often money (sometimes considerable amounts of both); in the limit, neither of these things can be abstracted away with intelligence, no matter how much of it you have on tap. This is the central reason that I have written so much about, and even written into public policy, automated scientific labs that could run thousands of experiments in parallel; AI will increase the number of good predictions, but these are worth little without the ability to verify those predictions with experiments at massive scale.

There is one further observation that follows from the disentanglement of knowledge and intelligence. This is that knowledge itself is distributed throughout the world in highly uneven and imperfect ways. Anyone who thinks that “all the world’s knowledge” is on the internet is deeply mistaken. There is information that exists within a firm like Taiwan Semiconductor Manufacturing Corporation that is, first of all, not only unavailable on the internet but literally against Taiwanese law to make public. Even more importantly, though, there is knowledge within that firm that cannot be written down and is only held collectively. No single employee knows it all; it is the network—the meta-organism of TSMC itself—that holds this knowledge. It cannot be replicated so easily. This is all merely a restatement of the knowledge problem most memorably elucidated by the economist Friedrich Hayek.

The implicit, and sometimes even explicit, argument of “the doomers” is that intelligence is the sole bottleneck on capability (because any other bottlenecks can be resolved with more intelligence), and that everything else follows instantly once that bottleneck is removed. I believe this is just flatly untrue, and thus I doubt many “AI doom” scenarios. Intelligence is neither omniscience nor omnipotence.

What all of this means is that I am doubtful about the ability of an AI system—no matter how smart—to eradicate or enslave humanity in the ways imagined by the doomers. Note that this is not a claim about alignment or any other technical safeguard, even if a “misaligned” AI system wanted to take over the world and had no developer- or government-imposed, AI-specific safeguards to hinder it, I contend it would still fail. “Taking over the world” involves too many steps that require capital, interfacing with hard-to-predict complex systems (yes, hard to predict even for a superintelligence), ascertaining esoteric and deliberately hidden knowledge (knowledge that cannot be deduced from first principles), and running into too many other systems and procedures with in-built human oversight. It is not any one of these things, but the combination of them, that gives me high confidence that AI existential risk is highly unlikely and thus not worth extreme policy mitigations such as bans on AI development enforced by threats to bomb civilian infrastructure like data centers. “If anyone builds it, everyone dies” is false.

4. Beware of Simple Narratives – Alfred Lin

Consider a few narratives that shaped and misshaped technology investing:

• Winner takes all. In some markets, such as search and social networking, this proved largely correct, but enterprise software proved stubbornly multi-vendor. E-commerce never consolidated the way the narrative predicted. Even in cloud infrastructure, the oligopoly of AWS, Azure, and GCP defied the single-winner thesis. The narrative was a useful heuristic. Founders and investors who treated it as a law made expensive mistakes.
• First mover advantage. Google was not the first search engine. Facebook was not the first social network. The iPhone was not the first smartphone. The company that finds product-market fit in the right window wins. But “timing and execution matter more than sequence” is a harder story to tell than “be first.”
• AI will replace [x]. Today’s dominant narrative is directionally correct but operationally misleading. The simple version, that AI replaces humans in a neat, linear substitution, misses the more investable reality. Augmentation, new workflows, and entirely new categories of work tend to emerge alongside displacement. The companies building for the nuanced version of this future look very different from those building for the simple version…

…In 1997, I declared that Amazon would kill Walmart. Today, Walmart is 30 times larger than it was 30 years ago. With each quarter of declining mall traffic and each confirmed brick-and-mortar bankruptcy, the thesis held true. This was confirmation bias at work. The world was messier than the story. E-commerce companies also failed. Customer acquisition costs online kept rising. Certain categories had persistent try-before-you-buy dynamics. Physical presence created brand equity that digital alone could not. Those who treated the simple narrative as a settled truth missed the omnichannel reality that ultimately prevailed.

5. Javier Blas on Why Oil Could Go Much, Much Higher (Transcript here) – Tracy Alloway, Joe Weisenthal, and Javier Blas

Javier: You are absolutely right that what is really cushioning the market right now is a number of buffers that we are going through. One is regular inventories that every country, every refinery has to normal functioning. Then is also the strategic inventories that some countries own, particularly industrialized countries like the United States, Europe, Japan, and also China. Those have been mobilized, in most places have been released. And also we entered the crisis with a market that was over-supplied. There was even floating storage – that is when an oil tanker has been loaded, it’s on the high seas but it cannot find a buyer and just basically sits on the high seas looking for someone who will take the oil. We have quite a lot of that just going into the crisis. So there was quite an element of buffer through the system and probably a larger buffer than in normal circumstances because the market was over-supplied. That is helping to cushion or to mitigate the crisis.

Where we are seeing some actions by government is where countries are closer to the crisis, which is the Strait of Hormuz. So the closer that you are to that location, the more action you need to take because you typically depend more of that flow of oil coming from the Middle East and also because you are impacted earlier. If you are moving oil from say Saudi Arabia into India, that’s only a few days, at most a week, of sailing time. If you are moving that to say the Philippines, that’s about 15 days. It’s longer if you are moving that oil into Europe, probably around three weeks. And it’s even longer if you are moving that oil into say the United States where Saudi oil takes about 40 days. All of that means that the crisis is felt in some places quicker than in other places.

Also is how the global oil market works. And to put it in quite simple terms, I’m afraid that I have to go with colonial vocabulary. The oil market is divided in two large chunks. East of Suez and west of Suez. This is like the British Empire was still around and everything was east or west of the Suez Canal. Countries that are east of Suez, mostly Asia, rely a lot on Middle East oil these days and therefore they are impacted earlier on by the crisis. West of Suez, Europe, Western Europe, and the whole American continent, is a bit detached from that market and therefore the crisis will hit them much later…

…Javier: But if I may suggest, forget about the price of a barrel of oil. No one cares about the price of oil unless you are someone producing oil in Texas or Saudi Arabia, or you are someone who owns a refinery. Those are the people that care about the price of a barrel of crude. The rest of us, you and I, we care about the price of a refined product because that’s what we consume. We consume gasoline, we consume diesel, or we consume other refined products that they embed into a service that we are buying. Think about an airfare ticket, where inside that ticket there’s a big proportion of it that is jet fuel, or you are buying a cup made of plastic. You are buying effectively some kind of transformed naphtha and obviously the transformation and the retail margin and so on, but what matters really is the price of refined products, and there actually we are beginning to see, particularly in the Southeast Asian markets, some very extreme prices.

If you look at the price of crude or Brent or WTI or Oman, things look relatively contained. We are trading around $110 a barrel, that is well below the all-time high. If you look at the cost of diesel in Singapore, which is a benchmark for the Southeast Asian market, the price there is approaching $200 a barrel, which is something that we have never seen. The refined product is where really we are seeing the real tension.

Tracy: This is exactly what I wanted to ask you. If you look at the benchmark prices for crude oil, we’ve seen higher prices before, and relatively recently in 2022. But if you look at the refined products, we’re getting to places that we haven’t seen. What explains that disconnect? Back in 2022, why didn’t we see the higher cost of crude feed into refined products the way that we seem to be seeing now?

Javier: For two reasons. One is because we have lost not only a lot of crude oil production, but we have lost a significant chunk of refined production. The Middle East also has a lot of refineries which are export refineries. They are just devoted to the export market and the global trade of refined products is a lot smaller than the global trade of crude oil. So even a small reduction in supply could have a much larger impact. You think about the global market for crude oil which is 100 million barrels, around 60 million are traded globally. But if you look at the market for say jet fuel, that market is a lot smaller and we have lost a significant proportion of the refineries who are serving that international market for jet fuel and therefore prices are reacting much more stronger than we saw in previous crises.

There’s also the way that the world of refining works. Some refineries are slowing down intake of crude oil because there is not enough crude oil in the market but we have not really seen yet the consumers reacting the same way. So what is happening is the refining world is acting as a buffer between crude oil that is not there, and consumers that have not yet realized that the crude oil is not there. The refined market is trying to basically get those two together. The way that it can only do it is by extreme pricing and indicating to the consumers, “I don’t have enough crude to make these refined products, so please can you stop demanding the refined products?” The please is basically $200 a barrel diesel…

…Tracy: What’s going on with US natural gas? If you look there – we’re talking about muted market moves in the oil market, even though those have risen – if you look at nat gas, nat gas has actually come down.

Javier: Nat gas in the United States is trading almost at a six-month low, which considering what is happening in the global energy market, is almost incredible. The reason there is US shale. And the reason is that you cannot export gas easily. For exporting gas, you first need to cool it down, liquefy. That basically means having an enormous fridge that cools gas from room temperature to -160 celsius, then it liquefies and then you can put it on a tanker and send it to the rest of the market. Because we have limited liquefaction capacity, and it does increase quite quickly, that creates a bottleneck. That means that the US and Canadian gas is effectively trapped inside North America and that’s keeping prices completely detached from the global market. That is a huge difference from previous episodes of high energy prices. Even in 2022, the price of US natural gas went from around $3.50-$4 to almost $10 per British Thermal Unit. This time it’s staying at actually below $3 per MBTU.

That is incredible because it means that the heavy US industry, electricity generators, chemical companies, fertilizer companies, there is no crisis while everyone else in the world is suffering. The US is completely insulated…

…Javier: 2022 was a huge shock to the global food market because it affected a bread basket region of the world. If you look at Russia and Ukraine, at the time combined, they accounted for around a quarter of global exports of wheat and barley, around 15% of global exports of corn, and even much higher percentage for some vegetable oil like rapeseed and sunflower. The Russian invasion of Ukraine, the battleground was some of the richest fertile farmland in the planet. The battleground of the crisis in the Middle East is deserts and a piece of sea that we call the Strait of Hormuz. It doesn’t have the same impact in terms of global supply.

It does have an impact on fertilizer prices. It did also, the 2022 war between Russia and Ukraine which is still ongoing. But fertilizer prices require time to have an impact on food production. Also, while yes the numbers are very scary and you look at the global fertilizer market, just focusing on urea, you look at that market and say, “Oh boy it’s going up a lot, we are approaching the 2022 record high.” But that is a problem in many markets, it’s a problem that is not a food problem. It will be a fiscal problem and the reason is that urea fertilizer in particular is massively subsidized in the developing world, particularly in places like India and Pakistan. So the problem there is going to be for the Indian government – can it afford to spend billions of dollars extra subsidizing fertilizer? Less so is it going to be a food crisis in India because the fertilizer I think is going to be there. You are the finance minister in India, you have a big problem there. That’s how I’m seeing the problem.

Also the global food market is in a better position than almost anytime in the last two or three decades. Inventories of wheat are very high. Inventories of rice in particular at an all-time high. You mentioned rice, while we are worried about fertilizer prices, etc., etc., if you look at the most important benchmark for rice prices in Asia, it is about to hit at 19-year low…

…Javier: Oh boy, if we we didn’t have enough with the Middle East, here is Ukraine. You cannot blame Ukraine, it is fighting for survival. They are hitting Russia as hard as they can, wherever they can. And that means hitting their oil terminals. In the past, they were hitting the terminals in the south of the country. That’s the Black Sea. But they have found a corridor to send long-distance drones into the north, into the Baltic. I think the Russians were caught completely offguard. They didn’t think that Ukraine will be able to hit the terminals in the far north of Russian territory. So they were not very well protected, or you say Ukrainians were extremely good at it. But the terminals have been damaged significantly. We don’t know for sure the extent of the damage, but looking at the satellite pictures, it looks bad enough. So we may be also losing potentially 1 million barrels a day of Russian oil. Again you cannot blame Ukraine, but it’s not really the time when you want to be losing more oil…

…Tracy: Okay, one thing that people have talked about for I’m pretty sure the duration of all of our careers, are attempts to move away from pricing oil in dollars. If you think about the current situation, there’s something very perverse about seeing the dollar go up because there’s a scramble for barrels of oil because of an action taken by the United States. From your context in the oil market, is anyone talking about actual currency pricing for barrels at the moment? Is this something that is going to get renewed traction?

Javier: No, I don’t hear anyone. Certainly Iran may be happy to take other currencies. It has been relatively happy to take Chinese yuan, and also other currencies which has problems on convertibility. Everyone else will still want the dollar. The way that it was put to me by a leading producing country in the Middle East, and I was talking to the head of the central bank, I’m going to not name the country. But they said to me, “If I switch from the dollar to say the yuan, I move from a relatively high interest rate, to a low interest rate. I move from full convertibility to a lot of problems to convert. And I move from maximum liquidity to no liquidity whatsoever.” And then this central bank governor is like, “Why I would like to do that? Why I would like to really take a step back on my currency?” I think that the yuan is not there yet for oil producers. Everyone that is using other currencies than the dollar to price their oil or to invoice their oil, they are doing it because they are under American sanctions. They’re not doing it because they want to do it. They’re doing it because they have no other option than to do it. Just because they are on the naughty corner of the US Treasury.

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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 GCP), Amazon (parent of AWS), Microsoft (parent of Azure), 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 29 March 2026:

1. What the closure of the Strait of Hormuz means for the global economy – Lutz Kilian, Michael Plante and Alexander W. Richter

From the point of view of the rest of the world, a disruption of oil exports from the Persian Gulf is equivalent to a disruption of oil production in the Gulf. From the point of view of oil producers in the Gulf, the difference is largely academic because as soon as local oil storage fills up, oil producers have no choice but to shut in their oil wells if the oil cannot be stored or exported. This is why many oil producers, starting with Iraq and Kuwait, started curtailing their production in early March 2026.

A complete cessation of oil exports from the Gulf region amounts to removing close to 20 percent of global oil supplies from the market, about 80 percent of which is shipped to Asia. Oil importers unable to access oil from the Persian Gulf have to turn to other oil suppliers, putting upward pressure on oil prices worldwide…

…Major oil supply shortfalls driven by geopolitical events such as wars or revolutions previously occurred following the Yom Kippur War in 1973, the Iranian Revolution in 1979, the outbreak of the Iraq–Iran War in 1980 and the Persian Gulf War in 1990. What makes the closure of the Strait of Hormuz different from these earlier oil supply shortfalls is first and foremost its magnitude. For example, in 1973 and 1990 only a little more than 6 percent of global oil supplies was removed from the market and in 1979 and 1980 only about 4 percent. Today, we are concerned with a shortfall close to 20 percent, making this geopolitical event three to five times larger.

This is the first time the Strait has been closed. While some observers in 1990 grew concerned that Iraq would take over Saudi Arabia and control of the Persian Gulf, these concerns never materialized…

…Regardless of the likelihood of the Strait reopening in the future, the model implies that a closure of the Strait of Hormuz that removes close to 20 percent of global oil supplies from the market during second quarter 2026 is expected to raise the average West Texas Intermediate (WTI) price of oil to $98 per barrel and lower global real GDP growth by an annualized 2.9 percentage points in second quarter 2026 (Chart 2).

The subsequent effects depend on when oil shipments resume. For example, if the Strait reopens after one quarter, the oil price drops to $68 per barrel and growth increases 2.2 percentage points in third quarter 2026. While the oil price drop causes growth to recover, the level of real GDP remains 0.2 percent below its pre-closure level even by year-end 2026 and 0.1 percent below its initial level by year-end 2027. The positive growth response in third quarter 2026 reflects the increased availability of oil and the resulting decline in the price of oil.

When the oil supply shortfall lasts longer than one quarter, richer dynamics arise. Extending the closure to two quarters causes the oil price to rise further to $115 per barrel in third quarter 2026 before falling to $76 per barrel in fourth quarter 2026 (Tables 1, 2). The impact on real GDP growth only turns positive in fourth quarter 2026. If shipping resumes after three quarters, the oil price will rise even further before declining, reaching as high as $132 per barrel by year-end. The impact on growth will remain negative through year-end 2026…

…While the model underlying these scenarios is global, the case can be made that the effects of higher oil prices on U.S. GDP growth will be of similar magnitude to the global effects. Although the U.S. economy for many decades was heavily dependent on imported petroleum, since the shale oil boom the U.S. petroleum trade balance has been close to balanced. This makes the U.S. economy not so different from a global economy model in which there is no trade in oil by construction…

…One way the oil supply shortfall could potentially be reduced is by Saudi Arabia increasing the flow of oil on the East-West pipeline from the Persian Gulf to the Red Sea. The capacity of the Yanbu port would allow Saudi Arabia to redirect about 4 million barrels of oil per day from the Persian Gulf for transport by oil tankers from the Red Sea, corresponding to about one-fifth of the global supply shortfall.

One obvious concern with this approach is the port in question is within range of both Iranian and Houthi missiles from Yemen, as are the waterways in the Red Sea. The other concern is that shipping this oil south past the Bab el-Mandeb Strait to Asia exposes oil tankers to attacks by the Houthis, while shipping it north through the Suez Canal limits the tanker size and requires redirecting the oil toward Europe rather than Asia where it is most needed.

There is also a short pipeline in the United Arab Emirates bypassing the Strait of Hormuz to the port of Fujairah on the Gulf of Oman. That pipeline as well as the port, however, have already come under Iranian attack, making it difficult for the existing flows to be maintained, never mind increased.

2. Warren Buffett Case Study – Dirtcheapstocks

J. Paul Getty was the richest man in America in 1957.

Five years later, you could buy a piece of his oil company for 63 cents on the dollar.

Warren Buffett took notice…

…Buffett’s Getty shares were marked at $18 at year end…

…On the surface Getty didn’t look especially cheap. Sure, it traded at a large discount to book, but the ROE was low and the stock was selling for 17x earnings.

But there was a larger issue at play.

Getty owned large stakes in three publicly traded, related party companies: Mission Corporation, Mission Development Corp., and Tidewater.

Without going into too much detail, the nature of these businesses was to produce, refine and market oil and manage other assets of the “Getty Empire”…

…Getty’s market cap was only $287mm.

Getty’s share of these three assets alone was $259mm.

Buyers at $18 were paying almost nothing for Getty Oil.

And it’s not like Getty had a bad business. It was earning $14mm of net income and had a pristine balance sheet. This excludes its share of income from Mission, Mission Development and Tidewater.

Getty had $398mm of total assets and only $50mm of total liabilities…

…Buffett was actually paying 1.7x earnings and 30% of book for Getty.

On a look through earnings basis (Getty earnings plus share of minority-owned earnings), Buffett paid ~7.5x for his Getty investment. It was cheap any way you slice it.

Getty had a steady history of growth…

…That’s an 11% CAGR over 11 years in BVPS.

Companies compounding book value at double digits should not trade at a discount to book…

…In 1949, J. Paul Getty ignored his advisors and bought a barren strip of desert in Saudi Arabia.

That piece of land produced 15,000 barrels per day in 1956.

By 1962 it was up to 100,000 barrels per day.

Getty’s agreement with Saudi Arabia called for a fixed royalty structure, allowing Getty to capture the vast majority of the field’s value.

In 1962, Getty produced 10x the oil volume of a decade prior…

…I don’t know how long Buffett held his shares, but he probably made money.

Shares traded up to $27.50 in 1963 and $32 in 1964.

Getty Oil was bought by Texaco in 1984 for $10.1 billion.

Adjusting for splits, the shares Buffett owned at $18 in 1962, would have grown to $625 in 1984.

Excluding dividends, the stock compounded at 17.5% for 22 years.

3. Meta’s Agentic AI Ambitions – Abdullah Al-Rezwan

One of the interesting bits from the blog post is that Meta mentioned for long-horizon workflow autonomy, Meta built REA on an internal AI agent framework called “Confucius” which they elaborated further on this paper back in February 2026. Often, when tech companies try to improve AI coders, they focus on making the underlying AI models (like GPT or Claude) smarter. However, the paper argued that the “scaffolding” i.e. the software environment, memory systems, and tools built around the AI is just as important. When working on big codebases, AI agents frequently get overwhelmed by reading too much code, forget their original plan during long tasks, or repeat the same mistakes.

The most interesting takeaway from the paper is that a great setup can compensate for a less powerful AI. The researchers proved that a weaker model (Claude 4.5 Sonnet) using the Confucius scaffolding successfully fixed more bugs (52.7%) than a stronger, more expensive model (Claude 4.5 Opus) using Anthropic’s standard setup (52.0%). When powered by the GPT-5.2 model, Confucius Code Agent successfully resolved 59% of the real-world bugs on the SWE-Bench-Pro test, beating both prior academic research and the official corporate systems built by OpenAI and Anthropic under identical conditions. If such scaffolding itself can consistently beat the more expensive SOTA models, it can provide a ceiling on SOTA model developers’ ability to exercise pricing power. It remains to be seen whether such scaffolding can outperform more expensive SOTA models in a wide range of scenarios. Nonetheless, the key takeaway is quite encouraging for all the tech companies that will not have a SOTA model and those tech companies may still be able to capture value from better scaffolding.

4. The AI Supply Chain Runs Through a War Zone. Nobody in Silicon Valley Is Paying Attention – Veron Wickramasinghe

The physical supply chain that powers every artificial intelligence system on earth passes through a single chokepoint that has been effectively closed since early March. Not a data bottleneck. Not a software constraint. A 21-mile strait between Iran and Oman through which 30 percent of the world’s LNG and 20 percent of its oil once flowed…

…Helium is the second most abundant element in the universe and one of the rarest on Earth’s surface. It is produced by the radioactive decay of uranium and thorium deep in the planet’s crust. It migrates upward through rock over billions of years and accumulates in the same geological traps that hold natural gas. You do not manufacture helium. You extract it as a byproduct of natural gas processing, or you do not have it.

Qatar’s three helium plants at Ras Laffan produce approximately 2.3 billion standard cubic feet per year: Helium 1 (660 million scf, online 2005), Helium 2 (1.3 billion scf, the world’s largest, online 2013), and Helium 3 (400 million scf, online approximately 2021). That is roughly one-third of total global helium supply, according to the US Geological Survey’s 2026 Mineral Commodity Summaries, which puts Qatar at 33.2 percent of world production.

All three plants have been offline since March 2, when Qatar halted LNG production following the outbreak of hostilities. The helium plants cannot operate independently of the LNG facility because helium is extracted from the natural gas stream during cryogenic liquefaction. When the gas stops flowing, the helium stops flowing.

QatarEnergy CEO Saad al-Kaabi confirmed on March 24 that the missile strikes reduced helium output capacity by 14 percent, with repairs expected to take three to five years. The planned Helium 4 plant, targeting 1.5 billion standard cubic feet per year and over 50 percent engineered before the crisis, has no confirmed restart timeline…

…The bottom line: helium is genuinely critical for specific, high-value fabrication steps, particularly plasma etching, where no substitute exists. It is not equally irreplaceable across all semiconductor applications. But the applications where it is irreplaceable happen to be the ones that define whether a chip gets made or does not…

…South Korea imports 64.7 percent of its helium from Qatar, according to Korea International Trade Association data for 2025.

South Korea is home to Samsung Electronics and SK Hynix, which together dominate global memory production. SK Hynix commands 62 percent of the High Bandwidth Memory market by shipment volume as of Q2 2025, per Counterpoint Research. Samsung holds 33 percent of global DRAM market share. Combined, these two companies produce the majority of the memory chips that go into every AI training system, every data centre GPU, and every high-performance computing cluster on earth.

HBM is the single most critical constraint in the AI hardware supply chain…

…South Korea imports approximately 70 percent of its crude oil from the Middle East. The Strait of Hormuz has been effectively closed to commercial shipping since early March, when war risk insurance premiums made transit economically unviable. Seoul implemented mandatory fuel rationing on March 25: a one-day-per-week vehicle ban for 1.5 million government vehicles, enforced by licence plate number.

QatarEnergy declared force majeure on long-term LNG contracts with South Korea on March 24. Gas generates approximately 26 percent of South Korea’s electricity. Those contracted molecules, which were supposed to flow reliably for decades, now carry a force majeure notice that could last five years.

South Korea is losing three supply lines simultaneously. Oil. Gas. Helium. All from the same chokepoint…

…SK Hynix has publicly stated it has diversified supplies and secured sufficient inventory. Samsung has not issued a public reassurance but is understood to hold approximately six months of stockpile and has deployed its Helium Reuse System, which reduces consumption by approximately 18 percent. TSMC says it does not currently anticipate notable impact and maintains helium from multiple suppliers with over two months of stock on hand. The Korea Semiconductor Industry Association says short-term supplies are sufficient.

There are reasons to take these reassurances seriously. Major fabs are not naive about supply chain risk. Over 70 percent of fabs in Taiwan and Japan already operate helium recycling systems. Six months of Korean stockpile buys time…

…The United States produces 42 percent of global helium but cannot rapidly scale. The former Federal Helium Reserve in Amarillo was privatised in June 2024 and can no longer serve as a government strategic buffer. Russia’s Amur Gas Processing Plant has design capacity roughly equal to Qatar’s entire output but faces Western sanctions. Algeria produces only 5 to 10 percent of global supply. Tanzania’s emerging helium projects are years from commercial production.

Phil Kornbluth estimates a minimum three-month disruption to helium supply chains, plus two months for logistics normalisation. If the conflict extends beyond six months, the structural deficit has no easy solution…

…South Korea does not just make chips. It builds the ships that carry the gas that the rest of the world needs to replace Qatar’s output.

South Korean shipyards, HD Hyundai Heavy Industries, Samsung Heavy Industries, and Hanwha Ocean, delivered 248 LNG carriers between 2021 and 2025, versus 48 from China. That is an 83.8 percent share of LNG carrier deliveries over the past five years, per BusinessKorea. Korean yards currently hold approximately two-thirds of the global LNG carrier orderbook by value, with LNG vessels accounting for 52 percent of their total backlog at $71.3 billion, per VesselsValue.

A single 174,000-cubic-metre LNG carrier costs $220 to 260 million at current pricing. Construction takes 30 to 36 months from steel cutting to delivery. Korean yards have orderbooks extending through 2028. New orders placed today face delivery in late 2028 or 2029.

Korean vessel exports hit $31.8 billion in 2025. Gas carriers make up over 60 percent of order composition… 

…South Korea’s energy crisis, caused by the Hormuz closure and Qatar’s force majeure, puts pressure on the industrial base that builds the LNG carriers the world needs to transport replacement gas. If Korean industry faces sustained energy disruption, supply chain delays, or cost inflation, carrier construction timelines could slip. If carrier construction slips, the global LNG fleet grows more slowly at precisely the moment the world needs more ships. If there are not enough ships, the global gas shortage deepens. If the gas shortage deepens, energy prices rise further. If energy prices rise further, Korean industry takes a harder hit.

I want to be precise about the limitations of this argument. There are circuit breakers. South Korea is restarting five nuclear reactors and easing coal restrictions. Shipbuilding is moderately energy-intensive, far less than steelmaking or semiconductor fabrication. There is currently an oversupply of LNG carriers, with approximately 60 idle ships providing buffer. Any disruption to shipyard output today would only affect deliveries in 2028 to 2029, given build timelines.

5. Notes from the SaaS Funeral – Reid Hoffman

Just two weeks ago, a single tweet about Claude Code was enough to wipe five percent off SaaS stocks. I understand the instinct. But I think the inference most people are drawing is wrong, and it’s worth being precise about exactly where the logic breaks down…

…Most of the arguments here fundamentally misunderstand software businesses as just lines of code you generate once. They are living systems that require maintenance, verification, security, compliance, and ongoing refinement…

… A CRM company that ships a deeply intelligent set of agents that iteratively refine your sales workflow, that understands your pipeline more comprehensively than any human analyst, that comes with powerful backend libraries purpose-built for that domain has an extremely well-crafted moat…

…The business model will shift, too. We may see more models where customers prepay token budgets much like a utility. For example, a CRM company that reimagines its economic model around compute consumption and scale. We’ve experienced business model transitions like this before. We went from on-premises software to cloud SaaS and the world didn’t end; it expanded. We’re making a similar transition now, from cloud to AI-native…

…And Jevons’ Paradox will do what it always does… as the cost of building software drops dramatically, the demand for software will expand dramatically.

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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 Meta Platforms 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 22 March 2026:

1. Why Walmart and OpenAI Are Shaking Up Their Agentic Shopping Deal – Paresh Dave

Last year, OpenAI made a bet that it could boost revenue by charging a commission on purchases made through ChatGPT. It partnered with Walmart, Etsy, and other shops on an “agentic commerce” feature called Instant Checkout.

Walmart has made about 200,000 products available directly in chat responses, allowing consumers to provide their shipping and payment details to OpenAI and place their order within ChatGPT. For products like TVs, shoppers still have to open Walmart’s website to make a purchase the old-fashioned online way. Conversion rates—the percentage of users following through with a purchase of an item shown to them by ChatGPT—have been three times lower for the selection sold directly inside the chatbot than those that require clicking out…

…The approach solves what Danker says he believes is the biggest problem with Instant Checkout: It forces people to buy items individually. “They fear that when checkout happens automatically after every single item that they’re going to receive five boxes when they actually just want it all in one,” Danker says. “They generally don’t want to split the checkout experience, where it buys the one item, even though they had other items in their Walmart cart already.”…

…In the new experience, Walmart users log into Sparky the first time they encounter it in ChatGPT. Their basket from Walmart’s website or app and within ChatGPT will sync with another in the hopes of better reflecting people’s actual shopping habits. Consumers add peanut butter one day on the Walmart app, foil the next, and a birthday gift at the last second on the website before checking out…

…Walmart has good reason to want to get the experience in ChatGPT correct. The chatbot is now bringing in about twice the rate of new customers as search engines, Danker says. He suspects that’s because the power users of ChatGPT are not typical Walmart customers. But the retailer’s price, selection, and broad geographic footprint mean that its products are showing up in many ChatGPT responses.

Sparky was developed by Walmart, Danker says. But it relies on open source generative AI models combined with some retail-specific ones trained on decades of Walmart data. “We’re able to route certain questions to one model and certain questions to another because we find that the quality of answers differs,” Danker says. “It’s never stuck in any one.”…

…Sparky has been criticized by people purporting to work for Walmart on Reddit, and testimonials for the chatbot are difficult to find on social media. But half of Walmart app users have engaged with it, according to the company. While people typically use the app to search for staples such as milk and bananas, they ask Sparky about exotic items or for solutions to more complicated problems. Walmart US CEO David Guggina recently said Sparky users spend about 35 percent more per order than other shoppers.

Danker acknowledges that Sparky is slow and generates weak responses often enough that some consumers might dismiss it as unreliable. Danker says the priority this year is training Sparky to be more proactive, getting it to learn more about individual shoppers, and making it helpful across more of Walmart’s many departments, such as the pharmacy.

2. Uzbekistan is gathering pace – what to look at now – Swen Lorenz

Uzbekistan has recently been attracting growing attention from investors.

One reason is the country’s remarkable demographics. With a fertility rate of 3.5 children per woman – far above the replacement level of 2.1 – Uzbekistan has one of the fastest-growing populations outside Africa.

When the people of a nation with a 100% literacy rate decide to have many children, it’s usually a sign that they are optimistic about the future of their country…

…In 2019, I was part of one of the first organised investor trips to Uzbekistan. The country had only just begun to move beyond the legacy of the Soviet Union and its late dictator, Islam Karimov. As I described at the time in an extensive three-part article, there were strong indications that Uzbekistan would embark on a programme of capital market reforms and privatisations.

However, the process proved slower than expected. It’s difficult to say whether Covid, domestic policies, or a combination of both slowed the reform momentum…

…Recently, however, circumstances have begun to change, both for frontier markets in general and for Uzbekistan in particular. The country’s demographics have also attracted growing attention from investors, amid the global debate about low birth rates and their knock-on effects on economies and asset prices. Over the past four years, Uzbekistan’s population has grown by an average of 700,000 people per year – more than the population of the country’s second-largest city, Samarkand…

…In Uzbekistan, Uzum may do just that.

The company began as an e-commerce marketplace but has since expanded into financial services, consumer lending, and express food delivery. Its integrated ecosystem could eventually resemble the “super app” model that has delivered spectacular investment successes elsewhere.

Today, Uzum’s ecosystem reaches about 20 million users – more than half of Uzbekistan’s adult population.

Early investors in the company will be delighted.

Founded less than five years ago, Uzum is already valued at USD 2.3bn. On 10 March 2026, it announced a new funding round at a valuation 53% higher than the one completed just seven months earlier.

Still described as a “startup” in media reports, Uzum generated revenue of USD 691m in 2025, up from USD 505m the previous year. Net income reached USD 176m.

3. Agents Over Bubbles – Ben Thompson

You need agency to use agents, and yes, the number of people who will have that agency are probably far fewer than those who might use a chatbot. Of course you can make the (almost certainly accurate) case that chatbots will become agent managers in their own right, but the more critical observation is that by abstracting humans away from direct model management any one single human can control multiple agents.

What this means in terms of compute — and by extension, economic impact — is that it actually won’t require that many people with agency to drastically increase the amount of compute that is actively utilized to create products with meaningful economic impact. In other words, the rise of agents doesn’t just mean a dramatic increase in compute, but also a narrowing of the need for widescale adoption by humans for that demand to manifest. Yes, AI still needs agency; it just doesn’t need agency from that many people for its impact to be profound…

… Most consumers mostly do just want to consume content (which, I would add, means he should be more worried about the Neo, not less). This is why your favorite productivity application always ends up pivoting to the enterprise: it is companies who are willing to pay for productivity, because they are the ones actually paying for the workers who they want to be more productive.

It’s reasonable to expect this to apply to AI as well: the most compelling consumer applications of AI, at least in the near term, are Google and Meta’s advertising businesses, which sit alongside content. By the same token, it was always unrealistic for OpenAI to think that it could convert more than a small percentage of consumers into subscribers; that’s both why an ad model is essential, and also why that won’t be enough to pay the bills. It’s definitely the case that most people don’t want to pay for AI; it remains to be seen if they want to use it enough to make the ad model work.

That is another way of saying that Anthropic got it right by focusing almost entirely on the enterprise market: companies have a demonstrated willingness to pay for software that makes their employees more productive, and AI certainly fits the bill in that regard. What makes enterprise executives truly salivate, however, is the prospect of AI not simply eliminating jobs, but doing so precisely because that makes the company as a whole more productive.

It’s always been the case, even in large companies, that a relatively small number of people actually move the needle and drive the company forward in meaningful ways. That drive, however, has been filtered through a huge apparatus, filled with humans, who accelerate the effort in some vectors, and retard it in others. That apparatus makes broad impact possible, but it carries massive coordination costs.

Agents, however, will tilt much more heavily towards pure acceleration, making those drivers of value much more impactful. I’m sympathetic to the argument that the best companies will want to use AI to do more, not simply save money; the reality of large organizations, however, is that the positive impact of AI will not be in eliminating jobs, but rather replacing hard-to-manage-and-motivate human cogs in the organizational machine with agents that not only do what they are told but do so tirelessly and continuously until the job is done.

This only makes the argument that we are not in a bubble that much more compelling:

• First, all of the weaknesses of LLMs are being addressed by exponential increases in compute.
• Second, the number of people who need to wield AI effectively for demand to skyrocket is decreasing.
• Third, the economic returns from using agents aren’t just impactful on the bottom line, but the top line as well.

In this context, is it any wonder that every single hyperscaler says that demand for compute exceeds supply, and that every single hyperscaler is, in the face of stock market skepticism, announcing capex plans that blow away expectations?…

… I noted above that what made Opus 4.5 compelling was not the model release itself, but changes to the Claude Code harness that made it suddenly dramatically more useful. What this means is that model performance isn’t the only thing that matters: the integration between model and harness is where true agent differentiation is found.

This is a very big deal when it comes to figuring out the future structure of the AI industry and where profits will flow, because profits flow away from modular parts of the value chain — which are commoditized — and flow towards integrated parts of the value chain, which are differentiated. Apple is of course the ultimate example of this: its hardware is not commoditized because it is integrated with their software, which is why Apple can charge sustainably higher prices and capture nearly the entirety of the PC and smartphone sector profits.

It follows, then, that if agents require integration between model and harness, that the companies building that integration — specifically Anthropic and OpenAI (Gemini is a strong model, but Google hasn’t yet shipped a compelling harness) — are actually poised to be significantly more profitable than it might have seemed as recently as late last year. And, by the same token, companies who were betting on model commoditization may struggle to deliver competitive products…

…What matters in terms of this Article, however, is that if agents are making Anthropic and OpenAI the point of integration in the value chain, then the bubble argument that these companies are overvalued, or that the massive investments other companies are making on their behalf in data centers is unwarranted, may not be correct.

I must, in the end, address my opening parenthetical: I’ve long maintained that there is no need to be worried about a bubble as long as everyone is worried about a bubble; it’s the moment when caution is flung to the wind and assurances are made that this is definitely not a bubble that we might actually be in one. And, well, I think the rise of agents means we are not in a bubble. The capex is warranted, and Anthropic and OpenAI look more durable than ever. If my declaring there is no bubble means there is one, then so be it!

4. The “secret” share that allows you to invest in North Korea right now (part 2) – Swen Lorenz

Chung Ju-young was the founder of Hyundai, THE South Korean conglomerate (“chaebol”) in the decades after the Korean War. Today, it’s Samsung that takes the crown among South Korean companies. But back in the 1970s and 1980s, Hyundai was the country’s biggest and most powerful corporation.

Hyundai suffered mightily under the 1997-98 Asian debt crisis and a seemingly never-ending family feud. However, this never dented Chairman Chung Ju-young’s passion for helping to make amends between the two Koreas.

In 1998, he led a herd of 500 cows over a North/South Korean border crossing as a symbol of future economic collaboration between two countries…

…That same year, Chung Ju-young and one of his sons, Chung Mong-hun, started offering tours to North Korea’s famous iconic Kungmangsan Mountain. With special permission from North Korea’s regime, their tourist groups initially traveled to the country’s mountain area by sea. Later, they even got permission to take South Korean visitors across the infamous Demilitarized Zone (DMZ).

The crowning glory of the Hyundai family’s efforts to bring both countries together, though, was the construction of the Kaesong Industrial Region, a special administrative region that was carved out as a place where South Korean companies could operate using cheap North Korean labor. The industrial park attracted 124 companies and grew to employ over 50,000 North Korean workers. It is located ten kilometers (six miles) to the North of the DMZ…

…In 2008, a South Korean tourist got shot and killed by a North Korean soldier. The tragic incident led to all further tours getting canceled until further notice.

Kaesong is currently closed, too. North Korea’s ballistic missile tests in 2016 made the South Korean government ask all companies to shut down operations. The site was professionally mothballed, i.e., it’s maintained but not currently open.

Tragedy struck in the family, too. Not only did Hyundai Group’s founder die of old age in 2001. His son, Chung Mong-hun, committed suicide in 2003 after it was revealed that he had used company funds to pay bribes in North Korea.

Thus ended the drive for economic reunification that Chung Ju-young had mostly focused under the umbrella of one of the family companies, Hyundai Asan…

…Back in the days of the late founder and his late son, Hyundai Asan negotiated agreements that went way further than merely operating tour groups and the Kaesong Industrial Park.

Hyundai Asan also has “exclusive business rights” to the following areas of the North Korean economy:

Electricity: Construction of power plants and expansion of existing ones.

Communication: Establishing and operating wireless services.

Rail: Reconnecting railroads between specific regions of both countries.

Airport: Construction of an airport in the tourist region of Kumgangsan.

Dam building: Construction of a dam near the Imjin River.

Water resources: Supply of water from the Kumgangsan Dam to the South.

Tourism: Development of tourism at specific, significant historic sites.

These are precisely the kind of large-scale infrastructure projects that the leaders of both Koreas have identified as priority areas for the potential future economic development of North Korea. Actually, the reopening of the Kaesong Industrial Complex and the construction of railway lines were a high priority part of the agenda of this week’s bilateral summit.

These contracts were all signed between Hyundai Asan and the North Korean government, which makes them both compelling and questionable. The North Korean government could decide not to honor the contracts. However, a country that is seemingly getting ready to welcome international investment back into the fold would be ill-advised to start the process by screwing one of its longest-standing allies in economic development.

It’s highly likely that there are still close contacts between the Hyundai family and North Korea’s dictator, Kim Jong-un. The widow of Chung Mong-hun is chairing Hyundai Asan, and she has made a point of keeping the vision of the company becoming a trailblazing investor in North Korea alive.

5. Rory Johnston on How Oil Could Surge to Over $200 a Barrel | Odd Lots (Transcript here) – Tracy Alloway, Joe Weisenthal, and Rory Johnston

Rory: What we talk about when we talk about the blowout in the product market is we’re talking about – so crude oil has a supply and demand curve as you see in econ 101. Then each individual product – gasoline, jet fuel, diesel, naphtha, petrochemical feed, everything else, shipping fuel – they all have their own specific supply and demand curves which this market becomes fractally complicated very quickly.

But to simplify what we’re talking about, it’s a refinery taking let’s say a barrel of oil for $100 which is roughly where we’re trading right now in Brent. We’re kind of jumping to another side of $100. They take a barrel of oil of $100 and they refine into a bunch of different products. The premiums they get for those products are what we typically call the crack spread, or the difference between crude and a refined product that is yielded from a refinery. And the refinery margin is essentially the weighted average blend of all those crack spreads, plus other costs and everything else.

But what’s happening right now, and the reason that we’re actually seeing the refined product market jump ahead of the consequences in the crude oil market, is that the worst thing for a refinery is literally running out of crude feed stock. And actually full credit to June Goh of Sparta Commodities for educating me more on this because I would have thought, “Wow, product markets are going insane. Refiners must be chasing as hard as they can, running as fast as they can, to capture those exceptionally high margins.” But the issue is that for them, shutting down a facility is the worst case scenario. This is basically a giant flowing chemistry set that if you turn it off, it’s really really hard to turn back on properly and it takes a lot of time and money and downtime and then you’re not capturing any of those margins.

So what the refiners are doing – these are the refineries in Asia that basically have a massive 20 million barrel a day gap coming towards them in the market in terms of feed stock – they’re preemptively reducing activity, reducing the rate of runs so they can extend their runway basically for how long they can remain in the market at all. So this means that with crude oil 2 weeks ago, we still had crude flowing out of the Gulf. It takes a month or two for those cargos to get to where they’re going. It’s only then that we’ll really start to feel the consequence and the supply loss and the inventory drain down. But with the refiners in Asia in particular, preemptively adjusting down their run rates, we’re seeing the impacts in Asian product markets immediately…

…Joe: Talk to us a little bit more about the sort of relationship between the duration of the war and the ability to flip the switch back. Because the president’s communication does seem to be like, we’re paying a price right now, but it’s going to be worth it and then prices are going to come down. As this goes on longer and longer, to what degree does everything compound and make it more difficult to go back to normal?

Rory: I was listening to actually your podcast on the Strait of Hormuz flow with the shipping experts exactly on this topic. I think you guys nailed it there, that this gets worse every single day it goes on. But let’s talk through the ways it gets worse.

When we talk about the Strait of Hormuz, you could think of it very simply as the world’s largest pipeline, or a big giant garden hose through which 20 million barrels of petroleum flows. When the Strait was closed initially for the first day, 2, 3 days, it’s like a kink in the garden hose. If the conflict had ended then, which is honestly when I expected it to end, you would unkink the garden hose and things would get back to normal pretty quickly. No harm, no foul. Some issues, but you can make that up pretty quickly.

But now, 10-plus days into this, we now have the equivalent of a 200 million barrel air gap in the global flow of petroleum. First of all – not to mention that in addition to this kind of kink in the garden hose – that pressure has built up because these countries can’t export out of this region anymore. Countries like Iraq and Kuwait in particular, both of which lack sufficient domestic storage capacity because they just export the stuff all the time for decades and decades, they have been forced to shut in production. Iraq as of yesterday shut in over 3 million barrels a day of production from its southern Basra fields. That is just Iraq alone so far. That is the same size as the feared loss of Russian supply in April of 2022 that sent the market ripping higher above $120 Brent. Just for perspective – and we didn’t end up losing that supply in the Russia case – we only lost one briefly and it came back. But in Iraq we’ve already lost, Kuwait we’ve already lost it, in the Emirates and Saudi Arabia, they have more storage capacity and a bit more optionality. There’s a pipeline to the west coast in the Red Sea in Saudi Arabia that can divert some of the flow. Similarly with the United Arab Emirates, you can divert some flow out the port of Fujairah. The pipeline to the west coast of Saudi Arabia can get bombed, if we get to an existential battle, this keeps grinding. Same with the ports of Fujairah, I think. These systems can all be broken. So you’ve lost that. You’ve lost supply structurally at least for weeks, potentially a month, even if the thing resumed, even if flow resumed tomorrow. That’s on the exporter, the supply side.

On the demand side, on the importers in Asia. Like I said, you’ve already begun to lose refining runs. Jet fuel is very particular, I think rightfully so. You don’t store as much of it typically. I think part of that giant spike in fuel prices, in jet fuel in particular, was this sudden loss of supply, not a lot of inventory cover and all of a sudden, you had all of these airlines all across Asia like, “Wow, I’m not hedged for this. I need to get every barrel I can right now.” So I think even if this resolved, which it doesn’t look like it’s going to, but even if it did, now we have a big air gap in the system that’s going to need to work itself out. And all of these different supply chains will probably end up taking 2-3 months minimum to get back to something resembling normal. And it doesn’t look like we’re about to resume flow through the Strait of Hormuz right now, despite what the White House says.

Tracy: I have what is perhaps a silly question, but does demand destruction actually exist when it comes to higher oil prices? I know that airlines will go bankrupt eventually because of high oil prices. But it feels like it is one of those things that you want to keep using for as long as you are physically or financially capable of doing so.

Rory: I’ll talk about three different angles here. The first is the difference between the elasticity of price versus the elasticity of income. When we typically think about demand destruction, we think primarily through the lens of “Prices got too high, so I’m not going to drive to work today.” There’s also the angle of prices got so high, they crashed the economy and you lost your job so you no longer have to drive to work. That is one angle if this goes on for much longer. We’re talking serious recession, if not outright global depressionary conditions if the Strait remains closed for a month-plus, two months.

I agree. I’m not going to stop driving my kid to school. I have a fairly high tolerance for high prices. But we live in wealthy advanced societies. I think what you saw for instance in 2022 I think is illustrative of this in the LG market when there was a very, very high-profile event when a contracted LG tanker that was supposed to land in Pakistan got diverted and ended up in Europe because the Europeans were willing to pay way way more and basically the LG supplier broke the contract to service that, which economics dictated. But I think the human cost was very real. Pakistan just couldn’t afford it.

So what you’re going to see here, let’s say in this horrible scenario where the Strait of Hormuz remains closed until 2027, this is what the world would look like. What you would end up seeing is massive demand destruction from lower income countries that can no longer afford to get those barrels and attract them to their shores in the first place. You and I would see this as massively surging prices at the pump and we would grumble and it would it would sap our consumer-spending-energy, etc., etc. But the barrels would likely be there. We are in the countries that will attract the most supplies because we’re willing to pay the highest prices. But other lower income countries in the world, it’s not going to be a price issue for them. It’s going to be an outright shortage. And that I think is how demand destruction in this particular instance would work…

…Tracy: I don’t think we’ve mentioned OPEC once in this conversation, which probably says something about OPEC’s relevance today. But to what extent can OPEC respond with a big supply increase and maybe shift some production away from the Gulf and start firing up output elsewhere?

Rory: It’s a great question and unfortunately the Strait of Hormuz is a risk concept, shortcircuits the OPEC’s normal reaction. When you’re talking about spare capacity, virtually all the spare capacity in OPEC is on the wrong side of the Strait of Hormuz. It’s in Iraq, Kuwait, Saudi Arabia, and the UAE. All of that is currently caught up in this. I think that’s part of the challenge and why the Strait of Hormuz was always the boogeyman scenario. There’s no real normal way that the market can get around it.

The one major producer that’s within OPEC that is likely the single greatest beneficiary of this is actually Moscow. The Trump administration has put a lot of pressure on what I call the Big Sanctioned Three. You’ve got Iran, Venezuela, and Russia. Venezuela we have a regime change. Iran was in the process of doing so or trying to. And then in Russia, they said that they were prioritizing the war in Ukraine and they were at various points. But now they had actually been putting a lot of pressure on the Russian oil trade. India, which was one of the largest importers of Russian crude, largest seaboard importer of Russian crude after the invasion with the price cap and everything else, they got under increasing pressure on two fronts. One, the Trump administration issued blocking sanctions, really really tough sanctions that were on Iran, issued those on Roseneft and Lukoil, which are Russia’s two largest crude oil exporting companies. The Indians didn’t like that and they started pulling back purchases there because they’re afraid of the sanctions risk. But in addition, Trump actually imposed a specific punitive 25% tariff on India for being such large importers of Russian oil. So between October and say January, we saw Indian imports of Russian crude drop from over 2 million barrels a day to about 1 million barrels a day. That Russian oil, a little bit was going to China, but it wasn’t finding many other buyers. So Tracy mentioned that we were building up lots and lots of oil and water. That’s where a lot of this was ending up. So the prices for these, the discounts that were suffered by Russian barrels were exploding, they were building up on water. The oil industry was on its back foot and probably going to start contracting pretty meaningfully if that continued.

Now what are you seeing? All of a sudden one of the major places that has any incremental supply at all to share around the world is Russia. India’s back in the market for Russian crude and the White House actually explicitly gave them a waiver for those sanctions that I mentioned previously. So they’re going to start importing a lot more Russian crude because they need to. Even the Europeans have started clamoring about easing sanctions or reopening flow on the Druzbha pipeline to Eastern Europe and into Germany. It’s a mess. It’s a mess that overwhelmingly serves the interests of the Kremlin above any other single national actor in this oil market…

… Rory: Let’s use an example of the US Gulf Coast which is the major refining hub of the United States where you have all of the outlet from the Permian and all the rest of the oil fields and directly into that refining hub, much of which is exported. You see a lot of diesel exports, about a million, million and a half barrels a day of diesel exports out of the region, largely going to Mexico, Latin America and other areas. If you banned exports, let’s say across the board, what you would do is you would start building those inventories at that pace in the US Gulf Coast. You would start overflowing your tanks of diesel. Diesel prices would crash. That would be great briefly for your drivers of big diesel trucks and shipping etc. That’s great.

But eventually you reach the stage where it’s the same kind of thing as you’re seeing from the Gulf exporters. You run out of storage space and all of a sudden you can’t produce any more diesel. You can’t put it anywhere. That begins to overflow your tanks. You need to cut runs. That’s when things get bad because then you’re starting to lose gasoline supply. You’re starting to lose everything else as well. And all of a sudden you’re going to get turned into an importer of various fuels.

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

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

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

Here are the articles for the week ending 15 March 2026:

1. The subtle art of not selling stocks – Chin Hui Leong

My co-founder, David Kuo, has an investing rule that some of you may find peculiar: He never sells any stock he buys…

…Before you dismiss this idea as reckless, consider what this commitment actually demands.

If you know you will never sell a stock, every purchase becomes a permanent decision. You can’t afford to be casual. You can’t buy on a whim and figure it out later…

…David treats his stock purchases the same way. By removing the option to sell, he raises the bar for every stock that enters his portfolio. The result is a collection of businesses he knows deeply and trusts completely…

..Most selling decisions are driven by emotion, not analysis. When a stock drops, fear kicks in…

…Daniel Kahneman, the Nobel laureate and father of behavioural finance, would recognise this pattern. In his parlance, your reflexive brain (called System 1), built for snap decisions and danger avoidance, often overwhelms your analytical (System 2) brain you before you have a chance to think things through…

…Back in January 2007, I bought shares of Netflix at a split-adjusted US$0.33 per share. Over the past two decades or so, the stock has soared, crashed and soared again.

Along the way, I sold half my position. At the time, it felt like the prudent thing to do. Lock in the gains; reduce risk; be sensible.

But here’s what “sensible” cost me: I estimate that the shares I sold would have gained over 14,000 percent had I held on. That’s the equivalent of holding 140 stocks that went to zero.

And the chances of finding another Netflix are slim. My remaining shares are up over 300 times my original investment. The half I kept is doing the heavy lifting: the half I sold become my most expensive lesson.

For David, his eyes are on the dividend stream his shares produce, not the stock price…

…You don’t have to adopt David’s rule as a rigid requirement. There are legitimate reasons to sell: A business may suffer permanent deterioration. Your original thesis may be proven wrong. Management may stray in ways that betray your trust…

…The art of not selling isn’t really about not selling. It’s about becoming the kind of investor who doesn’t need to react to every bit of news.

2. Ergodicity and Investing – Eugene Ng

The average investor made money. The average investor also does not exist. There is no average investor. There is only you, your portfolio, your decisions, and your one path through time. Finance forgot that. Ergodicity remembers it…

…A system is ergodic if the average outcome over many people (ensemble average) equals the average outcome of a single person over time (time average). When those two diverge, the system is non-ergodic, because you are not the group.

Imagine 100 people each play Russian Roulette once. One bullet, six chambers in a pistol, spin the chamber, and fire the pistol. Survivors get a huge prize. The group average survival rate looks seemingly acceptable (83% = 1 – 1/6). That is the ensemble average. The expected value is 0.833 (5/6 x 1 + 1/6 x 0). A classical economist would say, positive expected value, rational to play. If the prize is $1 mil, $10 mil, or $100 mil, does the size of the prize matter? Would you still play such a game?

Now, imagine that one person can only play 100 rounds of Russian Roulette sequentially. They are dead with near certainty (~99.999999%). While in round 1, the probability of death is 16.7% (i.e., 1/6), which rapidly increases as more rounds are played. Probabilities grow rapidly to 60%, 84%, 97%, 99% after 5, 10, 20, 30 rounds, respectively. This is the time average.

It’s the same game, but over time results in a completely different outcome…

…Maximize growth that first conserves survival. Game-overs cause non-ergodicity. Do not maximise growth over survival. When permanent game-overs are possible, don’t rely on averages. Focus on not being wiped out permanently first.

Avoid a total loss and irreversibility at all costs. Never allow a single negative event to maximise short-term returns, rendering long-term maximisation irrelevant. If you are going to play a game where, after many rounds, you are almost certainly going to be dead. Avoid playing all games that are not repeatable at infinity…

…Survival beats performance. Performance is always subordinate to survival. The longer the time horizon, the more true this becomes. To be among the best over time, you need to keep playing the game, rather than being kicked out.

Focus on being antifragile, not fragile. To determine whether something is fragile or antifragile, expose it to volatility and see how it responds. Fragile things are harmed by volatility, and antifragile things benefit from volatility. Fragility is non-ergodic. Antifragility is ergodic. Fragility has limited upside and unlimited downside. Antifragility has a limited downside and unlimited upside…

…We avoid margin/leverage at all costs. Brokers can offer up to 100x leverage, but never take it. A 1% move against you could wipe you out. A Monte Carlo simulation of 20 sequential scenarios with 20X leverage, using 8% p.a. returns and 18% annualized volatility, shows that ~90-100% of the time, one will eventually be permanently wiped out (with a cumulative loss of -5%)…

…Don’t agree with redistribution, particularly for investing. Trimming your winners to feed your losers is incorrect, as it assumes the same likelihood of returns. Winners tend to keep winning, and losers tend to keep losing. Persistence tends to be more likely at both the right tails (winners) and left tails (losers). As long as the risk is overly significant, one should first let your winners run high, second, don’t trim them, and third, add to them.

3. Good news: AI Will Eat Application Software – Alex Immerman and Santiago Rodriguez

Yes, AI is a big deal. But the conclusion that AI is going to kill the vertical and functional software business model simply makes no sense. The truth is that AI simply isn’t going to kill software companies: after all this panic has passed, we’ll see that AI is the best thing that ever happened to the software industry…

..The bear case rests on a basic misunderstanding of what software companies actually sell. The market is treating “software” as though it were a commodity input—as if the value of a software company resided in its code, and cheaper code meant more competition and therefore cheaper companies. But code is never where the value has lived: if code is where the value was, these companies would have never gotten so big in the first place. They would have been killed years ago by open-source software or by competition from cheap software engineering labor in developing countries…

…AI might increase competition; but it’ll also dramatically expand what software companies can do, how fast they can do it, and how large the markets they serve can become. The end result won’t be margin compression to zero. Software will be a much bigger industry, with durable competitive advantages for the companies that earn them…

…The classic contemporary book on business moats is Hamilton Helmer’s Seven Powers. He lists seven distinct ways in which companies develop robust competitive advantages: Scale, network effects, counterpositioning, switching costs, brand, cornered resources, and process power…

…Switching costs are perhaps the one moat that really is going to change. It’s definitely true that AI is changing the friction and the cost-benefit analysis associated with switching vendors: agents can assist with a lot of migration work that used to be a headache…

…Network effects are a classic moat. And they aren’t going away…

…On the surface, Salesforce is a CRM database; but anyone who has worked in an enterprise setting knows that Salesforce is also an ecosystem. When everyone uses one platform, the network becomes self-reinforcing: you use Salesforce because everyone uses Salesforce. And the more companies use Salesforce, the more valuable the ecosystem of third party applications built on top of Salesforce and platform administrators experts in Salesforce…

…Scale was never the defining moat in software—it’s just not as important for Salesforce as it is for a cloud provider or for an industrial company. But to some extent, it may matter more for AI applications where compute spend exceeds labor costs, driving a unit cost advantage to the larger consumers of tokens. In addition, there are places where scale will still help: it’s a straightforward economy of scale to concentrate that maintenance burden in one place, since productivity gains from specialization don’t go away in an AI world…

…Cornered resources, like high-quality proprietary data, aren’t going to stop mattering either. If friction goes to zero, simply consolidating publicly available data into a usable interface becomes less valuable, because anyone can do it. But if AI enables doing much more with high-quality data than you could before, then the stuff that you can’t get easily becomes extremely valuable…

…And perhaps the strongest moat of all in this new era is process power—or as George Sivulka of Hebbia calls it, “process engineering.” Application software can be thought of as a stored process—it encodes opinions about how the function of an organization should operate, and those opinions calcify over years and decades of use into something that is inseparable from the organization itself. Successful app software companies are the ones that co-evolve with their clients around these workflows. As those workflows penetrate ever-deeper into an organization, process engineering only becomes more important. And more difficult for challengers to replicate…

…Counterpositioning is a kind of power that can be summoned and wielded by new entrants to a market. It’s when the new company has a business model which, for whatever reason, is unattractive for the incumbent company to compete against. Disruption theory from Clay Christensen is a classic type of counterpositioning, but it doesn’t always have to be “low cost” as the differentiated counterposition. In software, a new technology stack could create the opening for a startup to create new kinds of products and business models that are difficult for incumbents to replicate – like Databricks and their “Lakehouse” model.

The “agent” model of doing work and replacing tasks is certainly going to create some counterposition opportunities for new startups to challenge incumbents. There’s been a lot of ink spilled on the disruption of “per seat pricing” at the hands of agentic upstarts with value-based pricing. Let’s take customer service as an example. Decagon prices its customer support product per conversation handled, not per agent seat, and will eventually price per resolution achieved: that’s fundamentally a better alignment of incentives between vendor and buyer. An incumbent like Zendesk can’t easily make that same move without cannibalizing its own seat-based revenue. Just as Blockbuster couldn’t match Netflix’s subscription model without destroying its existing economics or Peoplesoft couldn’t match Workday’s SaaS model without upending its monetization. Companies that start with the new business model don’t face that dilemma, and it’s the core reason why platform shifts so reliably produce new winners.

But guess what? The total amount of “end state pricing power” in the market didn’t necessarily decrease; it just means customers now have a choice of business models they’d like to subscribe to, and the better one will win. That’s how competitive markets have always worked! AI is not the first time that a wave of creative destruction has rearranged markets and shifted the playing field. But here’s the thing: the business models that result almost always dwarf the old ones in the scale of the total opportunity…

…AI isn’t going to destroy the software industry; it’s going to split it into two parts. There really will be some categories of software companies that face genuine pressure. Frontend tools that serve primarily as thin wrappers around commodity functionality and do relatively little beyond presenting data in a slightly more convenient format are vulnerable. Incumbent systems of record that still operate on archaic interfaces but raise prices every year should be worried. So should software companies that have an outdated pricing model and value proposition that’s just inferior to what AI-native competitors can offer. The companies that win in this environment will be the ones delivering genuine value, not the ones that built the highest walls around their customer base.

4. THE NDFI BOMB – Dirt Cheap Banks

Here is a sentence that should terrify you: the single fastest-growing loan category in American banking is one that most investors have never heard of, most analysts don’t understand, and most banks can’t fully explain.

That category is loans to Non-Depository Financial Institutions, or NDFIs.

An NDFI is any financial company that lends money but doesn’t take deposits. Think mortgage companies, private equity funds, hedge funds, subprime auto lenders, fintech lenders, insurance companies, business development companies (BDCs), and the sprawling private credit universe. These are the shadow banks. The firms that exist in the regulatory gray zone between Wall Street and Main Street.

Here’s where it gets dangerous: traditional banks are funding the shadow banks. When Bank of America extends a $500 million credit line to a private credit fund, or when a regional bank in Indiana provides warehouse lines to a dozen mortgage companies, those are NDFI loans. The bank is one step removed from the actual borrower, lending to the lender, and often has limited visibility into what’s happening with the money downstream.

As of Q1 2025, U.S. banks held $1.14 trillion in outstanding NDFI loans, according to the Federal Reserve Bank of St. Louis. But that’s only the money that’s already been lent. The International Monetary Fund estimates banks have an additional $900+ billion in undrawn credit commitments to NDFIs. That’s money NDFIs can draw down at any time, for any reason. In a crisis, they will.

Total potential exposure: north of $2 trillion.

And it’s growing at a pace that should make every risk manager in America lose sleep. NDFI lending has grown at approximately 26% annually since 2012, according to the St. Louis Fed. In 2025, it surged more than 50% year-over-year according to Federal Reserve data, the largest jump in records going back to 2016.

To put that in context: total bank loans grew roughly 4% annually over the same period. NDFI lending has been growing at six times the rate of everything else…

…The FDIC now requires banks with over $10 billion in assets to break their NDFI lending into five categories. Here is where the $1.14 trillion actually goes, based on Q4 2024 call report data:

Mortgage Credit Intermediaries (23% of all NDFI loans, roughly $219 billion): These are loans to non-bank mortgage companies. The bank provides a “warehouse line” that the mortgage company uses to fund home loans. Once the mortgage is originated, the mortgage company sells it to Fannie Mae, Freddie Mac, or Ginnie Mae and pays back the warehouse line. The end-borrower is a homebuyer. The risk to the bank is that the mortgage company goes bust before it can sell the loans, or that the loans don’t qualify for agency purchase and the collateral is worth less than the advance. This is generally considered the lowest-risk form of NDFI lending because the collateral is agency-eligible mortgages with a ready secondary market.

Private Credit Intermediaries (23%, roughly $202 billion in private equity fund loans plus additional business credit intermediary exposure): These are loans to private credit funds, business development companies, and leveraged lending vehicles. The bank provides subscription lines (backed by investor capital commitments), NAV facilities (backed by the fund’s loan portfolio), or direct credit lines. The end-borrowers are mid-market and lower-middle-market companies, often highly leveraged, that couldn’t get financing from traditional bank channels. These companies typically carry 4x to 6x debt-to-EBITDA, and in some cases higher. The bank’s collateral is ultimately the fund’s portfolio of leveraged loans to these companies.

Business Credit Intermediaries (21%): Loans to companies that in turn provide business financing. This includes BDCs, equipment leasing companies, specialty finance firms, and factoring companies. The end-borrowers are small and medium businesses.

Consumer Credit Intermediaries (9%): Loans to non-bank consumer lenders. This is where subprime auto lending lives. Tricolor Holdings, the company whose collapse kicked off the NDFI panic in September 2025, was a consumer credit intermediary. It sold cars and provided financing largely to borrowers with little credit history. JPMorgan, Fifth Third, and Barclays all had warehouse-style exposure. The end-borrowers are consumers who can’t qualify for traditional bank financing.

Other NDFIs (24%, roughly $395 billion): A catch-all category that includes insurance companies, pension funds, broker-dealers, investment banks, bank holding companies, and securitization vehicles. JPMorgan classified its entire $133 billion NDFI book as “other”, declining to break out subcategories, citing “organizational risk” associated with reporting different values to the FDIC and the Fed, according to the Financial Times.

The bottom line: 46% of all bank NDFI loans fund mortgage origination and private credit lending. The end-borrowers are homebuyers on one side and highly leveraged companies on the other. The remaining 54% funds everything from subprime auto loans to hedge fund margin lending to insurance company investment portfolios. 

5. All of the Jobs That No Longer Exist – Ben Carlson

Heading into the 19th century, about 70-80% of all jobs in the industrial world were in agriculture.

Most people were farmers.

By 1870, more than half of all men owned or performed labor on farms.

Today less than 1% of the U.S. population works in agriculture…

…There are plenty of jobs over the years that have been taken out by technology…

…There used to be people who would light all of the gas lanterns on the street by hand. They were replaced by electricity.

Before alarm clocks, people called knocker-ups used to go around tapping windows to wake people up…

…Before computers were around, NASA used human computers who literally did calculations by hand…

…It used to be someone’s job to set up the bowling pins by hand…

…There used to be video store clerks who would be forced to rewind the videos you forgot to rewind (and charge you for their troubles).

I could continue.

All of this job displacement and more has occurred yet the unemployment rate over the past 80 years or so has averaged less than 6%…

…There will certainly be a painful transition for many white-collar roles as AI is integrated into the workflow. I’m sure there are jobs out there that will be impacted by AI that we’re not even considering right now.

But new roles will also be created. AI will make so many people better at their current roles. That’s going to lead to more opportunities.

For many workers and businesses, AI will lead to more customers. Lawyers will be able to file more lawsuits. Tax accountants will be able to file more taxes. Financial advisors will be able to handle more clients. When bottlenecks are removed, output increases.

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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 Netflix and Salesforce. Holdings are subject to change at any time.

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

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

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

Here are the articles for the week ending 08 March 2026:

1.Iran: The Day After – Tomas Pueyo

Persia’s Shah used to be aligned with the West: He modernized the country, invited foreign investments, built a lot of infrastructure, improved literacy and healthcare…

The radical Islamists didn’t like this modernization, so they allied with the local Left to gain power, and succeeded in 1979.1 This means the entire legitimacy of the regime is based on opposing the US, its allies, and its values.

This would not have been a problem if Iran had limited itself to hating the US and Israel. Instead, they’ve threatened to attack and eliminate them for the last 47 years, and they haven’t limited themselves to empty threats. They’ve developed ballistic missile and nuclear weapon programs to be able to obliterate Israel, and maybe attack the US too.

For the last few decades, the US and Israel have tried to manage the situation, but the closer Iran is to getting nuclear weapons, the less they can tolerate it. Until recently, they were forced to because Iran was quite strong, with proxies in Palestine, Lebanon, Syria, Iraq, and Yemen. But after October 7th 2023, Israel has systematically eliminated most of them, so it and the US saw an opening last year to weaken Iran and its nuclear program, and took it. But that was just a delay. The truth is they will only be safe when this regime falls.

The problem is that achieving regime change is going to be very difficult…

…The recent strikes have killed the existing Supreme Leader, but there’s a long chain of command to replace him and any other leader killed through strikes. Then, there’s Khamenei’s Bayt, a group of 4,000 close employees who manage Khamenei’s affairs and power, and work as a shadow government mirroring the official one…

…Through this body, Khamenei controlled the BMEE and AQR2, huge conglomerates of over 200 companies with interests in real estate, construction, industry, mining, energy, power, food, agriculture, tourism, transportation, IT, media…

Khamenei’s Bayt was also able to infiltrate the military and the IRGC (Islamic Revolutionary Guard Corps), a kind of Praetorian Guard with over 125,000 members sourced from the Basij militia, a bigger group of ~400,000 poor, Shia radical volunteers (and 25 million members!!) who police the country on behalf of the government…

…45% of the Iranian government’s income comes from oil.3 If the US and Israel prevent Iran from selling its oil, its income will dry up, and it won’t be able to pay salaries. My guess is the Iranian regime will prioritize IRGC, Basij, and military salaries, but even then, losing 50% of your income can’t be easy. Unfortunately, this takes some time to bite, as the government will use other resources to pay its forces for as long as possible, and people can sometimes withstand some time without a salary…

…The vast majority of Iranians are tired of their government.

They are now celebrating the bombings on the streets.

The first consequence is oil. Iran has closed the Strait of Hormuz, many oil pumping stations and refineries have been hit in the area, and oil has stopped flowing. This will put pressure across the world too as oil prices increase…

…Saudi Arabia can ramp up supply, and employ an east-west pipeline that should be able to bypass the strait. It won’t be enough to counter the entire drop in supply, but it might end up benefiting Saudi Arabia through higher oil prices.

Meanwhile, the biggest consumer of Iranian oil is China, but it has historically high oil and gas reserves, so it might be able to withstand the war if it’s short enough…

…Four years ago, China had collected anti-US friends in Russia, Iran and its proxies in Syria, Lebanon, Hamas and the Houthis, Venezuela, Cuba, and a host of satellites considering whether to join them or not. Israel took care of Iran’s acolytes. The US neutralized Venezuela, Cuba is isolated and cut off from oil, Russia is bogged down in Ukraine, and Iran is at risk of falling. Virtually every friend that China has cultivated over the last few years is crumbling.

Not only that, but China’s standing as a provider of technology and military power is completely exposed. If China won’t come to the rescue of its allies, and its weapons can’t stop the US, who will want to side with them?

Then there’s the oil. Venezuela and Iran together accounted for 17% of China’s oil imports.

This is a bad day for China…

…Iran has 90M people, nearly twice South Korea’s population. 42% of them are under 25, and they have a 98% literacy rate. The country birthed one of the oldest civilizations on Earth, the first empire, and has seen a succession of successful ones through the ages. Its diaspora in the world—especially in the US—is educated, rich, and powerful. It could fund and provide the leadership for a renaissance in the country.

But only if the current regime falls.

2. A Munger PA Investment – Joe Raymond

The Alfred C. Munger Foundation (named for Charlie’s father) sold 10,000 shares of Black Hills Corporation (BKH) for $23 each in June 2009, resulting in a short-term gain of 29%…

…A reasonable assumption based on this filing is that Charlie purchased this specific lot of 10,000 shares for $18 apiece in early 2009 and sold in June 2009 around $23.

He could have been buying the stock before that and holding shares after.

The only thing we know with reasonable certainty is that Charlie thought Black Hills was a good buy in 2009 at $18 per share…

…Black Hills is a utility company based in South Dakota.

It was formed in 1941 through a combination of several existing utility companies serving the Black Hills region. The earliest predecessor traces its roots back to 1883…

…Black Hills could be described as a decent and predictable business in the years leading up to Charlie’s purchase. ROE was in the low double digits and book value per share growth (adding back dividends) averaged 11% from 2002 to 2008.

Simple, clean, predictable, decent quality…

…Black Hills earned $105 million in 2008 ($2.75 per share). It paid $1.40 of dividends that year and finished the year with $27.19 of per share book value…

…I think the thesis here was pretty simple.

A durable, safe business that earns double digits on equity shouldn’t trade for 66% of book value.

The crashing economy wasn’t going to kill the utility business. People still needed to turn their lights on and fire up the stove…

…BKH’s average price three years later in 2012 was $33.66 per share, good for a return of 98% (25% CAGR) before dividends…

…The Black Hills case isn’t terribly exciting, but I do find it interesting and useful.

If I had to nail it down to one simple idea it would be this:

Buying an adequately capitalized business, that should earn at least a high-single-digit return on its common equity, at a substantial discount to book value often works very well over short- and medium-term time frames.

3. Anthropic’s AI tool Claude central to U.S. campaign in Iran, amid a bitter feud – Tara Copp, Elizabeth Dwoskin, and Ian Duncan

As planning for a potential strike in Iran was underway, Maven, powered by Claude, suggested hundreds of targets, issued precise location coordinates, and prioritized those targets according to importance, said two of the people. The pairing of Maven and Claude has created a tool that is speeding the pace of the campaign, reducing Iran’s ability to counterstrike and turning weeks-long battle planning into real-time operations, said one of the people. The AI tools also evaluate a strike after it is initiated, the person said.

Claude has also been used in countering terror plots and in the raid that captured Venezuelan president Nicolás Maduro. But this is the first time it has been used in major war operations, according to two of the people…

…“It is notable that we’re already at the point where AI has gone from hypothetical to supporting real-world operations being conducted today,” said Paul Scharre, executive vice president at the Center for a New American Security, and who has written about AI in warfare. “The key paradigm shift is that AI enables the U.S. military to develop targeting packages at machine speed rather than human speed.”

The downsides, he said, are “AI gets it wrong. … We need humans to check the output of generative AI when the stakes are life and death.”

The Pentagon began to integrate Anthropic’s Claude chatbot into Maven in late 2024, according to public announcements. The system has been used to generate proposed targets, to track logistics and provide summaries of intelligence coming in from the field. The Trump administration has vastly expanded the use of Maven into many other parts of the military, with over 20,000 military personnel using it as of last May…

…Ben Van Roo, the CEO and cofounder of Legion Intelligence, a defense software startup, said that in his work over the last two and half years integrating generative AI into software systems at the Department of War, “the baseline use case is chat and advanced search functions — essentially summarizing information.”

It’s not highly integrated into weapons or mission critical systems, he said. He said that he wasn’t aware of its use in Iran, but wondered how it built on existing software that is already able to prioritize targets.

4. The Coase Conjecture in AI Inference Markets – Soren Larson

In 1972 Coase posed a simple question: If a monopolist owns all the land––assumed to be homogenous in kind and quality––in the world, at what price does he sell it?…

…Coase’s argument is interesting and simple. Normally a monopolist would set quantity sold where marginal revenue equals marginal cost. For convenience, let’s say marginal cost is zero.

Once the monopolist land owner has sold a bit of land, he sees the remaining land is also still available, but not monetized. Maybe he should sell a bit more––it’d generate pure profit! To do that, however, he’d have to lower the price to meet demand at the price it’s willing to pay.

Doing this annoys the original buyers.

The land is now worth less than what they paid. Eventually, however, the market catches on. Candidate buyers know the monopolist can’t resist selling more land (marginal cost of selling is zero!) and so they wait.

While the monopolist technically has no competitors, he ends up with one he didn’t expect––his future self. In situations like this, the market can guess a monopolist’s future behavior, so it holds out waiting for the “future self” monopolist to depress his own prices…

…At first glance, Coase seems to apply directly: the monopolist can’t resist selling more inference, buyers anticipate this, and prices unravel.

At first glance it could appear that Coase implies that frontier labs can’t sustain monopoly prices because they can’t resist selling more and more inference at what end up being lower prices.

This, of course, is incomplete in that every inference customer can choose to buy inference from cheaper open source models. It turns out the existence of open-source alternatives protects the monopolist’s pricing power by giving customers a reason to exit the frontier market rather than wait for discounts…

…In cases where buyers have an Outside Option––where they can defect from the monopolist’s market and buy some alternative––the Coasian monopolist unraveling doesn’t happen. The monopolist can sustain the monopoly price indefinitely.

Empirically, this appears to be happening in the inference market…

…Effectively, the outside option is a self-selection device that relieves the monopolist from price-sensitive waiters who’d pressure prices downward over time. The monopolist loses some customers but gets to keep pricing power. This is broadly what we see today…

…There are clear extensions to this setting in inference markets. Suppose you’re considering developing new software using AI: for you, waiting for Anthropic to lower prices could prove costly. A competitor who pays full price today could lock in customers before you enter the market. This dynamic is likely what explains today’s inference market structure: buyers would prefer to pay full price or defect to Minimax M2.5 or GLM 4.7 today than wait and let competitors eat their lunch.

The other extension, of course, is that Outside Options keep getting better. Open source models are improving every quarter: A buyer who defects today to a mediocre alternative might have waited for a better one in a quarter––returning us to the original Coase setting…

…Suppose now that the monopolist wins on all counts: open source improvement is slow enough that buyers don’t bother to wait. Open-source capability might even plateau. The Board and Pycia result holds and the monopolist charges its optimal price at equilibrium.

Is our beloved monopolist now safe?

So far we’ve only discussed pricing power, but what about market capture? Even if the monopolist preserves its pricing power, it could be that so much of the market defects to the Outside Option that pricing power is practically irrelevant.

Consider the buyer’s problem. The inference buyer only pays the monopolist pricing premium if the frontier model offers enough additional value over the open source alternative to justify the price. When open source closes the gap it reduces the collection of buyers for whom the frontier premium is worth the price. These two dynamics compound: a shrinking price corresponding to a lower marginal benefit of frontier v Outside Option mixed with a shrinking customer base means the monopolist’s total revenue erodes faster than the capability gap closes.

Of course, this argument depends on inference buyers actually connecting their buying decisions to value actually delivered.

The market may not be doing this today––many preferring to build Tool Shaped Objects. In fairness, model capabilities are jagged and it’s a reasonable  strategy for firms to keep buying frontier, irrespective of underlying value proposition while the technology matures. On the other hand, as the technology matures and firms begin to connect their inference consumption to value delivered, demand shifts from “just buy the best” to “maximize margins” or “buy what’s worth paying for.” In this world, the monopolist’s value proposition reduces to its incremental value over the Outside Option. And that shrinks even as open-source improves…

…Board and Pycia explain why margins are high: outside options remove the price sensitivity of buyers. High margins are an artifact of the Coase selection mechanism, not evidence of a durable business.

The labs clearly can and are charging high margins today. That’s not the question. It’s whether they will be charging high margins in three years. 

If open source keeps closing the gap, the answer from Board and Pycia––and from Ronald Coase––is probably not.

5. Biggest AI Prediction & Why I’m Allocating $200,000 to it – ContraTurtle

I categorize the AI stack into six levels:

• Level Zero: Energy (GE Vernova, Cameco Corp, Constellation Energy, etc.)
• Level One: Chips (TSMC, Nvidia, AMD, ASML, Broadcom, etc.)
• Level Two: Infrastructure & Data Centre (Equinix, Arista Networks, Vertiv, Amazon, Google, Microsoft, etc.)
• Level Three: AI Foundation Model Companies (OpenAI, Anthropic, Google DeepMind, Mistral, etc.)
• Level Four: AI Software Infrastructure (Amazon Web Services, Google Cloud Services, Microsoft Azure, Palantir, Snowflake, Databricks, etc.) – Enterprise platforms enabling AI deployment, orchestration, and data pipelines
• Level Five: AI Applications, Apps and Services (Meta, Google, Microsoft, Amazon, ServiceNow, Shopify, Axon, Netflix, etc.) – Companies delivering end user value and capturing economic surplus from AI optimisation

I will be focusing on Level Five in this article because this is where economic validation happens.

You can have:

• The most advanced GPUs
• The cheapest energy
• The largest data centres
• The most powerful foundation models

None of it matters if end users do not generate ROI that justifies capex deployed upstream.

Level 5 determines whether the entire AI stack earns an adequate return on capital.

Over the long term, the bulk of economic surplus accrues to the layer closest to the customer. Historically in technology cycles, infrastructure enables value creation, but applications capture pricing power.

This layer is still early…

…But there is one use case where AI ROI is already direct, measurable, and immediate and that is – Advertising.

Let me explain.

Ads share two structural traits with coding (a use case that has shown the most promise in enterprise):

• Low cost of failure with hallucination, yet provide high ROI
• Built-in verification mechanisms

In coding, hallucinated outputs are caught through testing frameworks. Unit tests, integration tests, and runtime checks validate whether the generated code works. If it fails, it does not ship.

Advertising works similarly.

An advertiser can generate five variations of an AI-created image, headline, or video and deploy them simultaneously. Performance is verified empirically through A/B testing across metrics such as:

• Click-through rate
• Conversion rate
• Return on ad spend

Poor-performing creatives are automatically filtered out by the market. Strong performers scale.

Advertising is therefore a near-perfect commercial application of probabilistic AI.

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Disclaimer: The Good Investors is the personal investing blog of two simple guys who are passionate about educating Singaporeans about stock market investing. By using this Site, you specifically agree that none of the information provided constitutes financial, investment, or other professional advice. It is only intended to provide education. Speak with a professional before making important decisions about your money, your professional life, or even your personal life. We currently have a vested interest in Alphabet (parent of Google), Amazon, ASML, Meta Platforms, Microsoft, Netflix, Shopify, 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 01 March 2026:

1.OpenAI Boost Revenue Forecasts, Predicts $111 Billion More Cash Burn Through 2030 – Sri Muppidi and Stephanie Palazzolo

As revenues climb, rising computing costs will weigh on OpenAI’s bottom line. Last year, the company burned $8 billion in cash, about $500 million less than it forecast in the summer. However, the company expects to burn $25 billion this year and $57 billion next year, about $30 billion more in total than previously predicted.

The company still expects to turn cash flow positive in 2030, when it expects to generate nearly $40 billion in cash…

…OpenAI has told investors the costs of running its AI models, a process known as inference, quadrupled in 2025. As a result, the company’s adjusted gross margin—defined as revenue minus the costs of inference—fell to 33% from 40% the year prior. That’s lower than the gross margin expectations of 46% it had set for itself for 2025. It’s also below half the 70%-plus gross margins of best-in-class software companies.

OpenAI has lowered its gross margin forecasts for the next five years, as its inference costs increase. In that period, the measure will range between 52% and 67%, according to the forecasts; previously the company had expected margins to hit 70% by 2029…

…OpenAI’s revenue more than tripled last year to $13.1 billion, $100 million more than its prior projection.

The new forecasts show OpenAI now expects revenue to rise to $30 billion this year and about $62 billion next year, slightly higher than prior forecasts, with its ChatGPT consumer business the largest driver…

…Last year, OpenAI spent more than $8 billion on the costs of running its AI models for its users, with roughly $4.5 billion on inference for paying users. Its inference costs are expected to rise to roughly $14 billion this year and $26 billion next year, or about $8 billion more in total than was earlier predicted.

The company expects to spend even more on computing costs to train its models. Last year, OpenAI spent $8.3 billion, about a billion less than it expected from its summer forecast. It plans to increase its training costs to $32 billion this year and $65 billion next year, or about $44 billion more than previously expected. These training costs add up, totaling nearly $440 billion through 2030.

2. Software bear case push back (and the real risk that I see) – Drew Cohen

There is a lot of talk of the competitve pressures on SaaS companies, but what about the AI Model businesses?

I think the key thing to remember is that the AI models have their own competition and they are all fighting for market share right now.

Partnering with existing incumbents is an easy way for them to win distribution…

…Users of these SaaS companies are already becoming a source of revenue for the AI companies. This greatly reduces the benefit of creating a product AND business support to specifically go after each vertical…

…I think in some specific cases there is a risk of internal IT departments creating their own software, but I don’t think that will be standard practice. We are already seeing that the AI companies themselves all use a variety of software vendors…

…The transition over the past decade has been for companies to outsource server maintenance to the cloud because they can’t run it as efficiently or introduce new features as quickly. It doesn’t make sense for them to run thin internally just as they often outsource facility maintenance. Unless a business has a benefit for maintaining their own software (which I can’t see), they will want to outsource this…

…I think what AI really does is it allows software companies to enter new verticals adjacent from their’s, which increases competition—I don’t think the competition is going to come directly from the AI companies though.

This is similar to the newspaper industry 20 years ago. The increase in competition didn’t come from “the internet”, but rather what the internet enabled, which was many new ways to get news.

The other risk is pricing pressure and the seat model collapsing. I think as long as the value these companies give their customers is as good, or higher, than before, they will be able to transition this.

3. A Level Headed Look at State of Software – DB

Business software as an industry is small in China and India because labor is a direct competitor to packaged software. Historically in these lower cost labor markets with exceptional technical talent, DIY has been the go to solution. Most western company leaders would be shocked to find that technically savvy Asian tech companies not only are able to in-house their own business applications, but even databases, BI and infrastructure technology.

Is this the direction the world is headed? When the token costs decreases 100x, its tempting to think that the math becomes:

Token to generate code + 2 SWE < annual cost of CRM license

But in reality, the decision is a trade-off of management bandwidth. If a vendor CRM breaks, a customer can expect a SLA for it to be brought back up. If there is a security vulnerability, thats the vendor’s responsibility. In fact, the extreme examples of DYI are only found in the most sophisticated technology companies in Asia. I fully expect AI Labs to experiment with DIY everything but with IT at ~5% of US GDP, I would consider this an edge case…

…I think the barrier to agentic success today is primarily because companies simply dont know how to implement the tools available. This is an area where AI Labs will find collaboration with traditional software businesses to be in their best interest.

This is a long way of saying AI Labs will be selective about which first party applications they themselves will go build. But they have a distinct advantage in that know the billions of questions being prompted each day. Personal health, personal finance, coding, improving writing skills/education, etc. are on the top of that list. And I were to bet, the focus of first party apps will be in these areas…

…Yes agents will be transformational, but i’d bet a good portion of the agents will come from the boring old companies you already know today

Oh wait, there’s more than a business process than code

The reality of a regulated industry is that the value proposition is the sheer volume of dirty work that needs to be done in the background to present a customer with something simple. While it may be true that a payment portal can be generated in hours vs. months now, the moat of a payment company is dealing obtaining bank licenses, putting in place a AML/KYC program with the adequate controls for SARs and fraud detection (just ask CZ at Binance). Same can be said healthcare, telcos and a variety of industries. Not only is there no value for DIY, the risk of doing so far outweighs the reward…

…Several things can be true at once:

• Software companies need to be able to adapt, and some will do it exceptionally well while others won’t
• New companies will be created
• Pricing may be compressed
• Most software companies are too bloated

At the end of the day, what the capital market is doing is applying a higher discount rate to the interim 10 year likelihood of previously forecasted cash flows and the terminal value after those 10 years.

4. Blue Owl Fouls the Nest for AI Financing – Ken Brown

Private market lender Blue Owl is living through the downturn part. The struggles of the firm, which has been a big funder of the AI build-out, could affect the flow of capital into data center developers and cloud providers that need to raise cash…

…Last year, it made at least $5.6 billion of equity investments into data centers and raised $64 billion in debt for those projects, according to internal figures…

…The firm’s effort to manage rising redemptions in one of its smaller funds backfired and appears to have tainted the whole firm. Private lenders live and die on their access to capital and deal flow, both of which are at risk of drying up for Blue Owl.

The firm’s troubles are significant because it sits at the nexus of two important funding sources for the AI build-out—private capital and individual investors. If worries about Blue Owl spread, some projects will be funded at a higher cost—or might not get funded at all…

…Last year, a $1.6 billion private fund that it runs for small investors was facing redemption requests. The firm decided to address the issue by merging the fund with a $16.5 billion publicly traded fund it also runs.

The problem was, the bigger fund was trading at a 20% discount to the value Blue Owl was placing on its assets. The smaller fund, because it wasn’t publicly traded, was priced at the value of its assets. That meant investors in the smaller fund would see the value of their investments fall by 20% when the deal got done. That didn’t make them happy…

…Blue Owl called off the merger, but the damage was done. The deal drew attention to the perennial problem of valuing private assets…

…Fast-forward to last week, when Blue Owl came up with another flawed solution to its problems. It would sell $1.4 billion of assets to three big institutional investors and to an insurance company that it has a deep financial relationship with. That money would fund investor redemptions.

One problem is that when a fund with illiquid holdings sells assets, investors assume it is selling the highest-quality and most liquid ones, meaning what’s left will be harder to sell. That makes further redemptions tougher and gives investors a signal to get out…

…Another issue: Blue Owl selling assets into the insurer, Kuvare Holdings, could indicate that there were no other buyers and that it stuck Kuvare with bad assets…

…That became clear on Friday, when Business Insider reported that Blue Owl had trouble raising funds for a $4 billion data center in Pennsylvania.

The project is relatively speculative as these things go, so there could be other reasons why Blue Owl couldn’t raise the cash. The firm said it has considered outside funding and ultimately didn’t need it.

5. History Rhymes: Large Language Models Off to a Bad Start? – Michael Burry

While mining old newspapers on a quiet Saturday – a hobby of mine – I came upon a story from June 19, 1880, that I found relevant to our modern anxieties about AI.

It is the story of Melville Ballard, who, as a child without language, spied with his eyes a tree stump and asked himself if the first man rose out of it.

This 144-year-old case study – presented at the Smithsonian Institute no less – provides a potentially devastating critique of today’s Large Language Models and the spending behind them. With a simple human story, it boldly announced that complex thought exists in the silence before words…

…There are actually two stories of interest in that old newspaper. Let’s start with the one in the middle. This is Page 3 of this edition of the New York Times, and I see a story called Thought without Language…

…The story concerns one Professor Samuel Porter, of the National Deaf-Mute College at Kendall Green, who presented a paper at the Smithsonian Institution. The paper title, “Is There Thought Without Language? Case of a Deaf Mute.”

At first discussion of deaf-mutes and children having no form of mental action that distinguishes them from brutes, well, understanding has changed a lot, and I was ready to dismiss.

The case study is of a teacher at the Columbia Institute for the Instruction of the Deaf and Dumb. This particular teacher, Melville Ballard, is also a deaf mute and a graduate of the National Deaf Mute College.

Mr. Ballard says that in his infancy he communicated with his parents and brothers by natural signs or pantomime. His father, believing that observation would help to develop his faculties, frequently took him riding.

He continues that it was during a ride two or three years before he was initiated into the rudiments of written language that he began to ask himself the question, “How came the world into being?” and his curiosity was awakened as to what was the origin of human life, its first appearance, the cause of the existence of earth, sun, moon, and stars. At one time, seeing a large stump, he asked himself the question, “Is it possible that the first man that ever came into the world rose out of that stump? But that stump is only a remnant of a once magnificent tree; and how came that tree? Why, it came only by beginning to grow out of the ground, just like these little trees now coming up;” and he dismissed from his mind as absurd the connection between the origin of man and a decaying old stump…

…One of the presentation’s attendees notes, significantly, how Ballard’s eyes conveyed meaning perfectly, without misunderstanding, above all else.

One of the most interesting features of this meeting was Mr. Ballard, by signs, explaining how his mother informed him that he was going a long way to school, where he would read from a book, write and fold a letter, and send it to her, &c., and also, by pantomime, reciting how a hunter, after killing a squirrel, accidentally shot and killed himself. Mr. Ballard’s signs and gestures, with the expression of the eyes and face, conveyed his meaning perfectly to the audience, and, in the words of a member, the expression of the eye was language which could not be misunderstood.

Let us consider these two statements:

• “That by which we understand all things must be essentially superior to anything else that is understood by it.”
• “…in the words of a member, the expression of the eye was language which could not be misunderstood.”

In sum,

1. Language without the Capacity for Reason fails at Understanding
2. Only with Capacity for Reason does Language unlock Understanding.
3. Understanding, fully realized, transcends Language.

By putting language first, LLMs build a primitive form of reason purely through logical inference, but this form of reason has been shown flawed and prone to hallucination due to limitations at the many ragged edges of knowledge.

The capacity for reason never existed. Therefore, language cannot scale through reason to understanding.

The professor suggests, in his work with deaf and mute people, he has discovered that a capacity for true reason must exist first, before language, so language can unlock understanding — the product of that capacity for true reason and language.

“The expression of the eye is the language which cannot be misunderstood.”

To wit, expression of the eye is what flawless understanding looks like, without the need for language.

Large Language Models, by putting language first, before the capacity for true reason, can never attain understanding…

…The original approach to AI was to generate a true capacity for reason first, but it was never realized, and the field pivoted to language first because it was easier.

This ‘bad start’ has led to a “parameter trap,” where brute-force language processing powered by zillions of power-hungry chips has become an incredibly ironic bottleneck.

As my conversation with Klarna’s Sebastian Siemiatkowski highlighted, the future lies in compression—leveraging ‘System 2’ reasoning-first to work off the redundancy of information and the relatively finite query sets produced by humans to drastically reduce compute needs.

This new line rejects singularity through language models talking to each other in an infinite mirror as a directionless waste of resources made impossible by lack of a basis in economic realities.

While frontiers like Google’s AlphaGeometry and Meta’s Coconut are finally moving toward this ‘reason-first’ architecture, they are essentially rediscovering what was presented at the Smithsonian 144 years ago: that language is the output of understanding, not the engine of reason…

…I mentioned there was another story of interest, and it is on the same page. More relevant to the first story than anyone in 1880s may have guessed it would be in 2026.

This article is “San Francisco’s Wealth, A Population of Bonanza Speculators.”

This story was written June 1 in San Francisco, and only published in the New York Times on June 19th…

…California was pre-eminently the paradise of the man of small capital. To satisfy the craving for speculation, the peculiar open-board system was adopted, whereby the man who had $50 to invest, by purchasing a share therein, could acquire a small interest in a mine at a dollar a share, or two shares at 50 cents, or any number at varying prices.

A “boom” existed here in certain stocks, seemed not to reach beyond the desire to do so “just once more” it seemed to excite the same gambling fever in San Francisco, and for lines lost by the bonanza firm was eagerly grasped by the people of San Francisco, and of the “boom” having been accompanied and by speculative losses on the part of the people, the “boom” disappeared and stocks fell to their normal condition.

The story closing hits hard for reality today.

The People of San Francisco seem to have become educated to the idea that they must leap into fortune at once, and their big bonanza at Virginia City having failed, they do appear to be willing to exert themselves to hunt for wealth in other directions, such as the development of manufacturing, trade, and agricultural interests. Almost the entire population is imbued with the passion for speculation, and if a new bonanza as big as the one in Nevada were to be discovered either there or near here, stocks would mount again to absurd figures, and San Francisco would again pass through the period of flush times to again suffer as she has during the past two years.

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

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

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

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

Here are the articles for the week ending 22 February 2026:

1. Google Is Exploring Ways to Use Its Financial Might to Take On Nvidia – Raffaele Huang, Kate Clark, and Berber Jin

The company’s chips are gaining wider adoption for AI workloads, including with startups such as Anthropic, but Google is dealing with myriad challenges as it seeks to grow. The issues include bottlenecks at manufacturing partners and limited interest from cloud-computing rivals that are among the largest buyers of Nvidia processors, according to people familiar with the matter.

To expand its potential market, Google is increasing its financial support to a network of data-center partners that can provide computing power to a broader swath of customers, people familiar with its plans said.

The company is in talks to invest around $100 million in cloud-computing startup Fluidstack, part of a deal that values it at around $7.5 billion, people familiar with the discussions said. Fluidstack is one of a growing number of so-called “neocloud” companies that offer computing services to AI companies and others…

…Google has also held discussions about expanding its financial commitments to other data-center partners that could lead to additional TPU demand, people familiar with the talks said. Google has backstopped financing for projects involving Hut 8, Cipher Mining and TeraWulf, which are former crypto-mining companies that are now developing data centers. Cipher Mining declined to comment. Hut8 and TeraWulf didn’t respond to requests for comment.

Some managers at Google’s cloud-computing division recently refreshed a longstanding internal debate about restructuring the TPU team into a stand-alone unit, people familiar with those discussions said. Such a plan could potentially allow Google to expand its opportunities to invest, including with outside capital.

One challenge for any potential stand-alone unit is that Google’s cloud business relies heavily on Nvidia chips, some of the people said…

…In 2018, Google started selling access to TPUs through its cloud services. The company has traditionally signed up TPU users through its cloud-computing unit, but it is also selling the TPU chips directly to external customers, according to industry research group SemiAnalysis…

…However, interest from major cloud-service providers appears to be tepid, partly because they consider Google a competitor, according to industry participants. Amazon Web Services, Amazon.com’s cloud unit, has also developed its own chips for AI.

2. 10 Years Building Vertical Software: My Perspective on the Selloff – Nicolas Bustamante

Vertical software is software built for a specific industry. Bloomberg for finance. LexisNexis for legal. Epic for healthcare. Procore for construction. Veeva for life sciences, etc.

These companies share a defining characteristic: they charge a lot and customers rarely leave. FactSet charges $15,000+ per user per year. Bloomberg Terminal costs $25,000 per seat. LexisNexis charges law firms thousands per month. And retention rates hover around 95%.

I would say that there are ten distinct moats. LLMs are attacking some of them while leaving others intact…

…Knowledge workers pay to not relearn a workflow they’ve spent a decade mastering. The interface IS a big part of the value prop…

…LLMs collapse all proprietary interfaces into one Chat…

…Vertical software encodes how an industry actually works. A legal research platform doesn’t just store case law. It encodes citational networks, Shepardize signals, headnote taxonomies, and the specific way a litigation associate builds a brief.

This business logic took years to build. It reflects thousands of conversations with domain experts. When I built Doctrine, the hardest part wasn’t the technology. It was understanding how lawyers actually work: how they research case law, how they draft documents, how they build a litigation strategy from intake to trial. Encoding that understanding into working software was a huge part of what made vertical software valuable—and defensible.

LLMs turn all of this into a markdown file…

…A massive portion of vertical software’s value proposition was making hard-to-access data easy to query. FactSet makes SEC filings searchable. LexisNexis makes case law searchable. These are genuine services. SEC filings are technically public, but try reading a 200-page 10-K in raw HTML. The structure is inconsistent across companies. The accounting terminology is dense. Extracting the actual numbers you need requires parsing nested tables, following footnote references, reconciling restated figures.

Before LLMs, accessing this public data required specialized software and significant engineering scaffolding. Companies like FactSet built thousands of parsers, one for each filing type, each company’s idiosyncratic formatting. Armies of engineers maintained these parsers as formats changed. The code to turn a raw SEC filing into queryable data was a genuine competitive advantage…

…LLMs make this trivial. Frontier models already know how to parse SEC filings from their training data. They understand the structure of a 10-K, where to find revenue recognition policies, how to reconcile GAAP and non-GAAP figures. You don’t need to build a parser. The model IS the parser. Feed it a 10-K and it can answer any question about it. Feed it the entire corpus of federal case law and it can find relevant precedent…

…At Doctrine, hiring was brutal. We didn’t just need good engineers. We needed engineers who could understand legal reasoning: how precedent works, how jurisdictions interact, what grounds for appeal to the supreme court look like. These people barely existed. So we built our own. Every week, we held internal lectures where lawyers taught engineers how the legal system actually worked. It took months before a new engineer was productive. The talent scarcity was a genuine barrier, not just for us, but for anyone trying to compete with us.

At Fintool, we don’t do any of that. Our domain experts (portfolio managers, analysts) write their methodology directly into markdown skill files. They don’t need to learn Python. They don’t need to understand APIs. They write in plain English what a good DCF analysis looks like, and the LLM executes it. The engineering is handled by the model. The domain expertise, which was always the abundant resource, can now become software directly without the engineering bottleneck.

LLMs make the engineering trivially accessible, which means the scarce resource (domain expertise) is suddenly abundant in its ability to become software. This is why the barrier to entry collapses so dramatically…

…Vertical software companies expand by bundling adjacent capabilities. Bloomberg started with market data, then added messaging, news, analytics, trading, and compliance. Each new module increases switching costs because customers now depend on the entire ecosystem, not just one product. S&P Global’s acquisition of IHS Markit for $44B was exactly this strategy. The bundle becomes the moat…

…LLM agents break the bundling moat because the agent IS the bundle…

…Some vertical software companies own or license data that doesn’t exist anywhere else. Bloomberg collects real-time pricing data from trading desks worldwide. S&P Global owns credit ratings and proprietary analytics. Dun & Bradstreet maintains business credit files on 500M+ entities. This data was collected over decades, often through exclusive relationships. You can’t just scrape it. You can’t recreate it.

If your data genuinely cannot be replicated, LLMs make it MORE valuable, not less…

…The test is simple: Can this data be obtained, licensed, or synthesized by someone else? If no, the moat holds. If yes, you’re in trouble…

…The irony is that LLMs accelerate the bifurcation. Companies with proprietary data win bigger. Companies without it lose everything…

…HIPAA doesn’t care about LLMs. FDA certification doesn’t get easier because GPT-5 exists. SOX compliance requirements don’t change because Anthropic released a new plugin…

…In fact, regulatory requirements may slow LLM adoption in exactly the verticals where compliance lock-in is strongest. A hospital can’t replace Epic with an LLM agent because the LLM agent isn’t HIPAA certified, doesn’t have the required audit trails, and hasn’t been validated by the FDA for clinical decision support…

…Some vertical software becomes more valuable as more industry participants use it. Bloomberg’s messaging function (IB chat) is the de facto communication layer for Wall Street. If every counterparty uses Bloomberg, you have to use Bloomberg. Not because of the data. Because of the network.

LLMs don’t break network effects. If anything, they might make communication networks more valuable. The information flowing through these networks becomes training data, context, signal…

…Some vertical software sits directly in the money flow. Payment processing for restaurants. Loan origination for banks. Claims processing for insurance companies. When you’re embedded in the transaction, switching means interrupting revenue. Nobody does that voluntarily.

If your software processes payments, originates loans, or settles trades, an LLM doesn’t disintermediate you. It might sit on top of you as a better interface, but the rails themselves remain essential…

…LLMs don’t directly threaten system of record status today. But agents are quietly building their own.

Here’s what’s happening: AI agents don’t just query existing systems. They read your SharePoint, your Outlook, your Slack. They collect data on the user. They write detailed memory files that persist across sessions. And when they perform key actions, they store that context. Over time, the agent accumulates a richer, more complete picture of a user’s work than any single system of record.

The agent’s memory becomes the new source of truth. Not because anyone planned it, but because the agent is the one layer that sees everything. Salesforce sees your CRM data. Outlook sees your emails. SharePoint sees your documents. The agent sees all three, and remembers…

…The real threat isn’t the LLM itself. It’s a pincer movement that vertical software incumbents didn’t see coming.

From below, hundreds of AI-native startups are entering every vertical. When building a credible financial data product required 200 engineers and $50M in data licensing, markets naturally consolidated to 3-4 players. When it requires 10 engineers and frontier model APIs, the market fragments violently. Competition goes from 3 to 300…

…From above, horizontal platforms are going deep into vertical territory for the first time. Microsoft Copilot inside Excel now does AI-powered DCF modeling and financial statement parsing. Copilot inside Word does contract review and case law research. The horizontal tool becomes vertical through AI, not through engineering…

…For any vertical software company, ask three questions:

1. Is the data proprietary? If yes, the moat holds. If no, the accessibility layer is collapsing.

2. Is there regulatory lock-in? If yes, LLMs don’t change the switching cost equation. If no, switching costs are primarily interface-driven and dissolving.

3. Is the software embedded in the transaction? If yes, LLMs sit on top of you, not instead of you. If no, you’re replaceable.

Zero “yes” answers: high risk. One: medium risk. Two or three: you’re probably fine.

3. Rebuttal to Nicolas – Unemployed Capital Allocator

I used to work for a relatively large long only shop.

We switched from Factset to Bloomberg + CapIQ.

We spent approximately 0 seconds discussing the UI change…

…Where does learned UI really matter? Tools with tons of degree of freedom, and where action per minute actually does matter. Professional workflow tools. Modelling software. Video editing software. Ones where knowing the shortcut is a decent part of the job.

A text box isn’t replacing this.

The idea is quite alluring – to those that don’t know the UI. Look! You can just tell it to do something and … it does it!

Until you need to do it multiple times. Then you start to go – man, I wish there was a quick way for me to send this prompt, to do this exact thing I want it to do. Oh and remember all the info I’m supposed to provide so that I get back exactly what I want. Maybe I can map it to a button and a keyboard shortcu…

Oh wait – that’s UI.

Text is amazing because it’s universal. Text is also absolutely horrible because it has infinite degree of freedom, and introduces another level of abstraction. This is not what you want when you need to do a lot of specific things, quickly.

Oh and btw – these ‘legacy providers’ with pesky, hard to learn UI and custom codes? They can very easily tack on a text box to help new users – or power users that are doing a new workflow. While providing the flexibility of getting shit done when you need to…

…There’s zero chance that a complex web of markdown files is going to replace business logic entirely.

The reason is quite simple. You do not want to introduce a layer of unpredictability and degree of freedom to your core business logic. This is stuff of nightmares even at simple levels. When you introduce complexity and interdependency, it’s straight line to system failure and bankruptcy…

…I am not sure why an agent would choose one vendor for alerts functionality and another for watchlist and 3rd for news – or how it would even go about doing this – or why this would save money. Maybe these will all be new providers? Maybe the model will just vibe code point solutions as needed? Maybe there will be perfect interoperability between all the modules? Or maybe LLM will learn to translate them all perfectly? I don’t know…

…SoRs exist as the core, singular database of truth that the whole org agrees is the truth.

Why are we splitting this across thousands of markdown files???? With no way to audit, reconcile, track … basically all the things we need a SoR to do????

4. The Golden Age of Software – Unemployed Capital Allocator

There’s a classic CS exercise: write instructions for making a PB&J sandwich, then watch someone follow them literally. “Put peanut butter on the bread” — and they place the sealed jar on top of the loaf. The lesson: every instruction you write is full of assumptions the other person doesn’t share.

This is what’s happening every time you prompt an LLM. You say “build me a user dashboard” and the model fills in hundreds of implicit assumptions about the world that you never specified. And here’s the thing: it’s really good at this. Good enough that the code runs, the demo looks great, and you feel like a genius. But those decisions are educated guesses. The model built you a PB&J. It doesn’t know that you’re allergic to peanuts.

When you’re vibe coding a demo or a small CRUD app, none of this matters. You’re on the happy path, everything works, nobody cares about code quality. It’s beautiful. But enterprise software in the real world is about every path but the happy one — a world where failure on one of those paths means losses that dwarf annual costs…

…So what happens when the market gets carpet-bombed with new products and DIY builds — in a market where customers ask “who else uses this?” as a standard question?

Decision fatigue. Procurement asking, “Who even are these guys?”

In a world where production becomes free, the existing distribution relationship becomes the chokehold. And this is what every incumbent has. Yes — this is the tired old distribution vs. product debate. But I’d argue the current moment makes it more true than it’s ever been, precisely because the supply explosion makes trust, brand, and existing relationships much more valuable…

…While existing relationships holds the line, incumbents also get to play offence.

Your development team now has a new source of leverage. Properly harnessed, everything from research to product creation to debugging and maintenance gets faster. “Where is this logic?” stops being a week-long archaeology expedition. You simply do more with the same team.

In addition, the value ceiling of software today is dramatically higher than it was two years ago. Stuff that was “too expensive,” “too custom,” or “not worth the engineering time” suddenly becomes shippable. LLMs and VLMs have unlocked capabilities that were science projects two years ago…

…What about agents taking over corporate workflows and becoming a key user of software products? Doesn’t that leave a lot of products open to disintermediation?

I have three pushbacks.

First — a lot of workflow shifting to agents is not the same as all workflow shifting to agents. The gap between those two things is enormous, and the bear case tends to hand-wave right past it.

Second — agentic workflow is still a pipeline. And when you have a working production pipeline, you don’t rip out a key component to save a couple thousand bucks. But this isn’t just an inertia argument — it’s a structural one. The agent replacing that component needs to match the accumulated production knowledge baked into the existing solution: every edge case, every integration quirk, every failure mode discovered over years of real-world use. That’s not a matter of writing code. It’s a matter of replicating hard-won context that doesn’t exist in any training set. The idea that agents will vibe code an alternative for a critical piece of a high-speed production system isn’t just unlikely because of switching costs — it’s unlikely because the agent literally doesn’t know what it doesn’t know.

Third — non-humans using software is not a new thing. There’s a whole class of software that is mostly consumed by other software, and these still make amazing businesses. The identity of the user changing from human to agent doesn’t inherently destroy the value of the product.

5. How will OpenAI compete? – Ben Evans

“Jakub and Mark set the research direction for the long run. Then after months of work, something incredible emerges and I get a researcher pinging me saying: “I have something pretty cool. How are you going to use it in chat? How are you going to use it for our enterprise products?” 

– Fidji Simo, head of Product at OpenAI, 2026

“You’ve got to start with the customer experience and work backwards to the technology. You can’t start with the technology and try to figure out where you’re going to try to sell it”

– Steve Jobs, 1997

It seems to me that OpenAI has four fundamental strategic questions.

First, the business as we see it today doesn’t have a strong, clear competitive lead. It doesn’t have a unique technology or product. The models have a very large user base, but very narrow engagement and stickiness, and no network effect or any other winner-takes-all effect so far that provides a clear path to turning that user base into something broader and durable. Nor does OpenAI have consumer products on top of the models themselves that have product-market fit. 

Second, the experience, product, value capture and strategic leverage in AI will all change an enormous amount in the next couple of years as the market develops. Big aggressive incumbents and thousands of entrepreneurs are trying to create new features, experiences and business models, and in the process try to turn foundation models themselves into commodity infrastructure sold at marginal cost. Having kicked off the LLM boom, OpenAI now has to invent a whole other set of new things as well, or at least fend off, co-opt and absorb the thousands of other people who are trying to do that.

Third, while much of this applies to everyone else in the field as well, OpenAI, like Anthropic, has to ‘cross the chasm’ across the ‘messy middle’ (insert your favourite startup book title here) without existing products that can act as distribution and make all of this a feature, and to compete in one of the most capital-intensive industries in history without cashflows from existing businesses to lean on. Of course, companies that do have all of that need to be able to disrupt themselves, but we’re well past the point that people said Google couldn’t do AI.

The fourth problem is expressed in the quotes I used above…

…There are something like half a dozen organisations that are currently shipping competitive frontier models, all with pretty-much equivalent capabilities. Every few weeks they leapfrog each other…

…There is no equivalent of the network effects seen at everything from Windows to Google Search to iOS to Instagram, where market share was self-reinforcing and no amount of money and effort was enough for someone else to to break in or catch up.

This could change if there was a breakthrough that enabled a network effect, most obviously continuous learning, but we can’t plan for that happening…

…The one place where OpenAI does have a clear lead today is in the user base: it has 8-900m users. The trouble is, there’re only ‘weekly active’ users: the vast majority even of people who already know what this is and know how to use it have not made it a daily habit. Only 5% of ChatGPT users are paying, and even US teens are much more likely to use this a few times a week or less than they are to use it multiple time a day. The data that OpenAI released in its ‘2025 wrapped’ promotion tells us that 80% of users sent less than 1,000 ‘messages’ in 2025. We don’t know how that changed in the year (it probably grew) but at face value that’s an average of less than three prompts per day, and many fewer individual chats. Usage is a mile wide but an inch deep…

…OpenAI’s ad project is partly just about covering the cost of serving the 90% or more of users who don’t pay (and capturing an early lead with advertisers and early learning in how this might work), but more strategically, it’s also about making it possible to give those users the latest and most powerful (i.e. expensive) models, in the hope that this will deepen their engagement. Fidji Simo says here that “diffusion and scale is the most important thing.” That might work (though it also might drive them to pay, or drive them to Gemini). But it’s not self-evident that if someone can’t think of anything to do with ChatGPT today or this week, that will change if you give them a better model. It might, but it’s at least equally likely that they’re stuck on the blank screen problem, or that the chatbot itself just isn’t the right product and experience for their use-cases no matter how good the model is.

In the meantime, when you have an undifferentiated product, early leads in adoption tend not to be durable, and competition tends to shift to brand and distribution. We can see this today in the rapid market share gains for Gemini and Meta AI: the products look much the same to the typical user (though people in tech wrote off Llama 4 as a fiasco, Meta’s numbers seem to be good), and Google and Meta have distribution to leverage. Conversely, Anthropic’s Claude models are regularly at the top of the benchmarks but it has no consumer strategy or product (Claude Cowork asks you to install Git!) and close to zero consumer awareness…

…So: you don’t know how you can make your core technology better than anyone else’s. You have a big user base but one that has limited engagement and seems really fragile. The key incumbents have more or less matched your technology and are leveraging their product and distribution advantages to come after the market. And, it looks like a lot of the value and leverage will come from new experiences that haven’t been invented yet, and you can’t invent all of those yourself. What do you do?

For a lot of last year, it felt like OpenAI’s answer was “everything, all at once, yesterday”. An app platform! No, another app platform! A browser! A social video app! Jony Ive! Medical research! Advertising! More stuff I’ve forgotten!  And, of course, trillions of dollars of capex announcements, or at least capex aspirations…

…As we all know, OpenAI has been running around trying to join the club, claiming a few months ago to have $1.4tr and 30 gigawatts of compute commitment for the future (with no timeline), while it reported 1.9 gigawatts in use at the end of 2025…

…But, again, does that get you anything more than a seat at that table? TSMC isn’t just an oligopolist – it has a de facto monopoly on cutting edge chips – but that gives it little to no leverage or value-capture further up the stack. People built Windows apps, web services and iPhone apps – they don’t build TSMC apps or Intel apps.

Developers had to build for Windows because it had almost all the users, and users had to buy Windows PCs because it had almost all the developers (a network effect!). But if you invent a brilliant new app or product or service using generative AI, or add it as a feature to an existing product, you use the APIs to call a foundation model running in the cloud and the users don’t know or care what model you used. No-one using Snap cares if it runs on AWS or GCP. When you buy an enterprise SaaS product you don’t care if it uses AWS or Azure. And if I do a Google Search and the first match is a product that’s running on Google Cloud, I would never know…

…As I’ve written this essay, I’ve returned again and again to terms like platform, ecosystem, leverage and network effect. These terms get used a lot in tech, but they have pretty vague meanings. Google Cloud, Apple’s App Store, Amazon Marketplace, and even TikTok are all ‘platforms’ but they’re all very different.

Maybe the word I’m really looking for is power. When I was at university, a long time ago now, my medieval history professor, Roger Lovatt, told me that power is the ability to make people do something that they don’t want to do, and that’s really the question here. Does OpenAI have the ability to get consumers, developers and enterprises to use its systems more than anybody else, regardless of what the system itself actually does?…

…Foundation models are certainly multipliers: massive amounts of new stuff will be built with them. But do you have a reason why everyone has to use your thing, even though your competitors have built the same thing? And are there reasons why your thing will always be better than the competition no matter how much money and effort they throw at it? That’s how the entire consumer tech industry has worked for all of our lives. If not, then the only thing you have is execution, every single day. Executing better than everyone else is certainly an aspiration, and some companies have managed it over extended periods and even persuaded themselves that they’ve institutionalised this, but it’s not a strategy.

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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 Google Cloud), Amazon (parent of AWS), Apple, Meta Platforms, Microsoft (parent of Azure), Salesforce, and TSMC. Holdings are subject to change at any time.

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

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

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

Here are the articles for the week ending 15 February 2026:

1. Before the market declared SaaS dead, it should have tested Anthropic’s new tools first. We did – Jim Wagner

Lawyers are not early adopters by temperament, and they don’t grade on a curve. A tool that reviews a contract and misses a material protection doesn’t get classified as “promising but incomplete.” It risks being shelved. Permanently. The standard is binary: either the tool is reliable enough that I can build a workflow around it, or it isn’t. There is no middle ground where a legal team says “it caught seven out of ten critical issues, so let’s use it for now.”

This is especially true in regulated environments — clinical trials, financial services, healthcare — where a missed clause isn’t an aesthetic problem. It’s a liability exposure, a regulatory finding, or a damaged institutional relationship. The question isn’t whether AI can review contracts. It can. The question is whether it can do so at the threshold required for a professional to rely on it…

…A clinical trial agreement is a different animal. It’s longer, more technically complex, and touches regulatory frameworks — HIPAA, FDA reporting obligations, IRB oversight, 21 CFR Part 54 financial disclosure — that require genuine domain expertise. The provisions interact with each other in ways that matter: a change to monitoring visit procedures can impact confidentiality obligations; a publication review period needs to account for patent deferral timelines; a subject injury provision needs to include a safe harbor for protocol deviations made to protect patient safety.

Once again, we gave Claude the identical playbook TCN uses — one specifically structured for AI consumption, with clear logic and well-defined positions — and ran both systems against the same clinical trial agreement.

The gap didn’t narrow. It widened.

TCN made 101 insertions of required protective language and 62 targeted deletions — 163 substantive changes in total. Claude made 7 insertions and 4 deletions. Tellingly, Claude’s changes were largely find-and-replace-level revisions: substituting “immediately” with “promptly,” replacing “sole” with “reasonable,” increasing an insurance figure, and adding pandemic language to a force majeure clause. These are real edits. They are also the edits a first-year associate would make in the first twenty minutes of review…

…These results are not a reflection of Claude’s quality as a language model. Claude is an extraordinarily capable general-purpose AI, and we use it daily in our own work. The gap is a reflection of architecture and ambition.

Claude’s legal plugin reads an entire agreement and an entire playbook, then attempts to produce all of its analysis and redlines in a single pass. This is analogous to asking a lawyer to read a thirty-page contract and a fifty-topic playbook simultaneously, then dictate every markup from memory in one sitting. Issues inevitably get lost — not because the lawyer lacks ability, but because the task exceeds what any single-pass process can reliably accomplish.

A purpose-built system works differently. Each playbook position is matched against the agreement independently and analyzed in a dedicated step with only the relevant clause text and guidance in front of it. Nothing competes for attention. Every position in the playbook is programmatically guaranteed to be evaluated. The system doesn’t need to “remember” to check a provision — it cannot skip one.

This also explains why the gap widened on the longer, more complex clinical trial agreement. The more provisions, the more playbook positions, and the more regulatory context a single-pass system must hold in working memory simultaneously, the more it drops. A purpose-built pipeline scales linearly. A single-pass approach degrades…

…The stock market’s reaction treated Anthropic’s announcement as if a general-purpose model with a vertical plugin is architecturally equivalent to purpose-built vertical software. It isn’t — and the evidence is now available for anyone willing to run an actual test.

But there’s a more fundamental point. Nothing Anthropic announced addresses multi-document congruence, multi-party collaboration, or institutional workflow orchestration. A Claude user reviewing a clinical trial agreement operates in a single chat window with a single document. The protocol, consent form, budget, and coverage analysis — all of which must be internally consistent with the contract — exist nowhere in that workflow. Imagine five users with five separate skills in five disconnected chat windows, each trying to keep their work coordinated, cross-checked, and accurate. There is no shared data model. No audit trail. No collaboration layer. No mechanism to ensure that a change to the protocol ripples correctly through the budget, the consent form, and the contract.

The natural counterargument is that agentic AI frameworks — autonomous agents that chain tasks, manage state, and coordinate across documents — will close this gap. They will have an impact, we use them ourselves and we take that seriously. But agentic frameworks don’t arrive pre-built with plug-and-play domain solutions. They are tools, not answers. An agent orchestrating clinical trial study startup still needs deep context understanding of the subject matter, the stakeholder requirements, and the interconnectedness of every document and every party involved. It needs to know that a change to a protocol’s schedule of events must ripple through the budget, the consent form, and the coverage analysis — and it needs to know how. That’s not something you install. It’s something you build — substantial work that relies on deep expertise with respect to the subject matter and AI implementation, refined across thousands of agreements. The same architectural principles that separate a plugin from a platform will separate a generic agent from a team of purpose-built ones.

2. As AI enters the operating room, reports arise of botched surgeries and misidentified body parts – Jaimi Dowdell, Steve Stecklow, Chad Terhune and Rachael Levy

In 2021, a unit of healthcare giant Johnson & Johnson announced “a leap forward”: It had added artificial intelligence to a medical device used to treat chronic sinusitis, an inflammation of the sinuses. Acclarent said the software for its TruDi Navigation System would now use a machine-learning algorithm to assist ear, nose and throat specialists in surgeries.

The device had already been on the market for about three years. Until then, the U.S. Food and Drug Administration had received unconfirmed reports of seven instances in which the device malfunctioned and another report of a patient injury. Since AI was added to the device, the FDA has received unconfirmed reports of at least 100 malfunctions and adverse events.

At least 10 people were injured between late 2021 and November 2025, according to the reports. Most allegedly involved errors in which the TruDi Navigation System misinformed surgeons about the location of their instruments while they were using them inside patients’ heads during operations…

…In May 2023, Dean was using TruDi in another sinuplasty operation when patient Donna Fernihough’s carotid artery allegedly “blew.” Blood “was spraying all over” – even landing on an Acclarent representative who was observing the surgery, according to a lawsuit Fernihough filed in U.S. District Court in Fort Worth against Acclarent and several manufacturers. One of Fernihough’s carotid arteries was damaged. She suffered a stroke the day of the surgery, according to her suit.

Acclarent “knew or should have known that the purported artificial intelligence caused or exacerbated the tendency of the integrated navigation system product to be inconsistent, inaccurate, and unreliable,” the suit alleges.

Acclarent has denied the allegations in both suits, which are ongoing, according to court filings. The company says it did not design or manufacture the TruDi system but only distributed it, according to court filings. Acclarent’s owner, Integra LifeSciences, told Reuters there’s no evidence of a link between the AI technology and any alleged injuries…

…Reuters found that at least 1,401 of the reports filed to the FDA between 2021 and October 2025 concern medical devices that are on an FDA list of 1,357 products that use AI. The agency says the list isn’t comprehensive. Of those reports, at least 115 mention problems with software, algorithms or programming.

One FDA report in June 2025 alleged that AI software used for prenatal ultrasounds was misidentifying fetal body parts. Called Sonio Detect, it uses machine learning techniques to help analyze fetal images.

“Sonio detect software ai algorithm is faulty and wrongly labels fetal structures and associates them with the wrong body parts,” stated the report, which does not say that any patient was harmed. Sonio Detect is owned by Samsung Medison, a unit of Samsung Electronics. Samsung Medison said the FDA report about Sonio Detect “does not indicate any safety issue, nor has the FDA requested any action from Sonio.”…

…The FDA requires clinical trials for new drugs, but medical devices face different screening. Most AI-enabled devices coming to market aren’t required to be tested on patients, according to FDA rules. Instead, makers satisfy FDA rules by citing previously authorized devices that had no AI-related capabilities, says Dr. Alexander Everhart, an instructor at Washington University’s medical school in St. Louis and an expert on medical device regulation.

Positioning new devices as updates on existing ones is a long-established practice, but Everhart says AI brings new uncertainty to the status quo.

“I think the FDA’s traditional approach to regulating medical devices is not up to the task of ensuring AI-enabled technologies are safe and effective,” Everhart told Reuters. “We’re relying on manufacturers to do a good job at putting products out. I don’t know what’s in place at the FDA represents meaningful guardrails.”

3. Clouded Judgement 2.13.26 – Build vs Buy – Jamin Ball

The cost of creating software is going to zero. The risk isn’t that someone will vibe code a internal CRM replacement…The risk is that 10 companies could now create a new CRM, from the ground up, built for a new end user in mind (agents vs people), with a business model for the AI world (consumption / usage vs seats), and now all of a sudden the market is flooded with offerings and the legacy space commoditizes.

This, to me, is the real risk. Software broadly commoditizes, with a new crop of software / value emerging. A big constraint to the development of software is engineering resources. Before the cloud, a constraint was how quickly could you stand up racks of servers to support user growth. In the cloud era that was commoditized, and engineering resources became the constraining factor (how quickly could you develop software). With AI, that constraining resource (engineering velocity) is going away.

So what happens from here…The world is about to be flooded with software. For companies that can’t innovate and capture this next S-Curve of innovation, they will slowly fade to irrelevance. The will be valued as companies in a post-growth industry, and receive a post-growth valuation multiple (see ya revenue multiples…). For those who can, a new vector of growth lays ahead of them…

…If we bring this back to the “is software dead” conversation, many are pointing to the recent Q4 earnings reports (we’re in the middle of earnings season right now) as “evidence” that AI isn’t eating software. For the most part, earnings have been good! Retention figures don’t seem to show any sign of cracking. However, I found an awesome graphic floating around X this week (copied below). It showed an index of newspaper companies stock performance and earnings over time (starting in 2002). What you’ll see, is the voting machine of the market saw the disruption coming from the internet, and started to discount the newspaper stocks right away. From 2002 to 2009 those stocks basically went down in a straight line. However, if you look at earnings estimates for that same set of companies, they actually grew for about 5 straight years! During that time, the stocks continued to drop. It wasn’t until 2007 when the earnings really started to get disrupted. Earnings then fell off a cliff. All of this to say – don’t take too much comfort in the short term quarterly results 🙂 Disruption generally takes a bit longer

4. Earnings Drive Stocks – Matt Cerminaro

Below I’m showing you the net income share vs the market cap share of each sector within the S&P 500 since 2005…

…Each color represents a sector. Net income share is on the left and market cap share is on the right.

Let’s start on the left.

See how the Technology Sector’s net income share has grown over time? It’s the light blue shade at the bottom of the chart.

Now look at the chart on the right.

That same light blue shade rising over time is the market cap share of Tech growing concurrently with the net income share.

Energy, the orange shade, used to command a larger share of the S&P 500’s overall net income, but it has shrunk over time.

Its market cap share has done the same.

5. AI and the Economics of the Human Touch – Adam Ozimek

The player piano, or pianola, was invented by Edwin Votey in 1895. At first it was a stand-alone machine that would be pushed up against an existing piano, like the one shown below.

Within a few years, player pianos could be built into the pianos themselves. The machines “read” music that was encoded onto rolls of paper. The notes were represented as holes in the paper that directed pneumatic airflow, which then pushed down the levers that depressed the piano keys.

The only role for humans to play in the functioning of a player piano was to pump the pneumatic foot pedals to keep the piano playing. No need for a skilled human piano player.

And yet, despite the technology to fully automate the job having been invented more than a century ago, people still make a living playing the piano today.

The job is not just limited to piano players performing in ticketed concert events, which of course are quite common. Hotels, bars, and restaurants continue to hire live piano players to provide background music as if it was 1894, the year before the invention of the pianola, which itself is hardly ever used anymore.

Listeners simply prefer music from a piano player rather than a player piano…

…In 2007, a restaurant entrepreneur named Jack Baum was teaching an executive MBA program at Southern Methodist University. He challenged the class to come up with a way to help restaurant customers pay their bill faster than simply waiting for the server to bring the check. Three students arrived at such a compelling answer that the four of them turned it into a company called Ziosk.

Ziosk’s tabletop ordering system provides customers with a tablet that allows them to order, pay, play games, enter coupons, and much else. Thus was born the ability to automate away the job of waiter.

The tablet debuted at 125 Chili’s locations in 2013, and today they are in thousands of restaurants. Ordering devices like this are much more commonplace today, including QR codes that allow customers to order from their own smartphones.

On paper, the job of waiter has been fully automated for over a decade. And yet, today there remain 1.9 million waiters across the US. It’s true that this number has dipped recently, and is slightly below the historical peak. Under the pressure of automation, the BLS forecasts that it will further decline within the next decade… by 1 percent. Is that the worst that full automation can do to this job?…

…Consider first that even some restaurants that have implemented automation nevertheless have wait staff. At Olive Garden, you can order and pay from a provided tablet at any point, but you still have a waiter who greets you, offers to take your order if you don’t want to use the tablet, and checks in on you throughout the meal. If you wait long enough, they will even bring the check. That is a strong signal that the waiter is adding value above and beyond automation…

…If productivity surges from AI, the United States will become a far richer country per capita. It’s not clear whether this will translate into much faster income growth for the median workers. In recent decades, after all, median wage growth has lagged mean wage growth — likely reflecting the trend that overall productivity growth has exceeded the growth in productivity of the typical worker.

Median wage growth has been positive, so it is not true that the typical workers fails to benefit from faster productivity growth. But the benefit for the typical worker is not proportional to the economy-wide growth in productivity, raising the spectre that future productivity growth could be even less proportional.

The result would be rising income inequality — which can straightforwardly be offset with policies that redistribute income. Redistribution might be expensive, but the same AI-driven economic growth that generated the rising inequality would also create the fiscal space needed to offset it. In short, spreading income around is a political challenge, not a policy or economic challenge.

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