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What We’re Reading (Week Ending 22 February 2026)

The best articles we’ve read in recent times on a wide range of topics, including investing, business, and the world in general.

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.

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.

What We’re Reading (Week Ending 15 February 2026)

The best articles we’ve read in recent times on a wide range of topics, including investing, business, and the world in general.

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!

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.

Disclaimer: The Good Investors is the personal investing blog of two simple guys who are passionate about educating Singaporeans about stock market investing. By using this Site, you specifically agree that none of the information provided constitutes financial, investment, or other professional advice. It is only intended to provide education. Speak with a professional before making important decisions about your money, your professional life, or even your personal life. We currently have no vested interest in any company mentioned. Holdings are subject to change at any time.

What We’re Reading (Week Ending 08 February 2026)

The best articles we’ve read in recent times on a wide range of topics, including investing, business, and the world in general.

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!

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

1. Software Is Dead. Long Live Software – Eugene Ng

SaaS software stocks have declined significantly since Oct 2025 amid broader concerns that software is in decline, disrupted, displaced, and replaced by AI. Companies will use AI to redesign and unbundle their workflows over time, and the markets are effectively pricing in a software apocalypse.

The selloff has been almost indiscriminate, and the market is overly pessimistic…

…Software is a digital tool. It does not make sense to keep reinventing tools (e.g., a calculator or a hammer). If there are new tasks that have not yet been automated and can now be automated with software, now is the best time. Software is a TAM accelerator, and companies can create new and more products in shorter time frames.

The future appears to be agentic, with agents constituting the new digital workforce for humans, working for us and with other agents on exploratory, low-value, and repetitive tasks, thereby allowing us to focus on higher-value creative and strategic tasks.

The fact that everyone has a pen or a keyboard does not mean that we will have a rush of great writers, authors, or coders. The best work will still be done by the select minority, not the vast majority. Writing code is easy. Shipping a basic V1 is just 1% of the work. 99% of building enterprise software is about writing code that actually works and keeps working, maintaining it, iterating on it, securing it, and scaling it, and that is where the real difficulties lie. Vibe coding might be incredible for prototypes, internal tools, and new products, but it is not replacing a proven tool.

It is the same with AI. It does not mean that, if one can code faster with AI assistants, one can write great code or develop a great product. It still requires deep understanding, intent, judgment, and taste. And that’s where the bottleneck lies. Try getting a first-year coder to “vibe-code” and build a massive CRM database, and you will soon realise that it is not as easy. Automation scales whatever structure already exists. Agents tend to work best when intent is explicit and stable, and struggle when it is implicit and judgment-intensive.

SaaS is heterogeneous, not homogeneous. One cannot simply be lazy and lump everything into a single category of thought. The idea that enterprises will dump all software to “vibe-code” their own software with AI agents is wildly optimistic. Larger, more complex SaaS platforms with substantial codebases, deep workflows, extensive API connectors/regulatory licenses, strong network effects, and extensive hardware infrastructure are likely to be more insulated.

Deterministic systems where precision is critical, non-negotiable, requiring it to be 100% all the time, are more likely to be more insulated, as “close enough” is simply unacceptable. Probabilistic systems, conversely, tend to tolerate some errors and accept good-enough performance, and are primarily focused on pattern recognition, content generation, basic automation, and simple decision-making. If an LLM can replicate your probabilistic product with 90% of the quality at 10% of the cost, you are likely not to have a sound business model any longer. Even having a great UI or UX won’t save you.

High-value, mission-critical, must-have software is likely to be more insulated than low-value, non-mission-critical, good-to-have software. Functions such as cybersecurity, payments, and infrastructure are likely to remain robust. Because when these go down, the business stops. Customers should continue to be willing to pay premium prices for quality and peace of mind, remain highly sticky, and rarely switch because the cost of failure is too high. They tend to have high gross retention (customers don’t leave), high net retention (customers spend more over time), and are willing to pay more as their business grows.

2. The Utilities Analyst Who Says The Data Center Demand Story Doesn’t Add Up (Transcript here) – Tracy Alloway, Joe Weisenthal, and Andy DeVries

Tracy: Interesting. One of the reasons we wanted to talk to you is because you have that contrarian take on the data center built out, and we wrote it up in the Odd Lots newsletter, which everyone should subscribe to. It got a lot of attention. Your analysis, interestingly, is just based on some pretty simple math. So why don’t you, just to start out with, why don’t you walk us through the calculations that you’re actually making to try to analyze how much capacity the utilities are taking on to actually power data centers?

Andy: As you said, it’s pretty simple math here. So data centers now are consuming around 45 GW of power. And you can switch between capacity and throughput – I’m going to stick with capacity. So 45 GW of power. And then there’s lots and lots of third party estimates for where they’re going to be in 2030, and they are centered around this, 90 GW, 95 GW. So you need to add 50 GW. For 2035, there’s a lot fewer estimates. You come around 160 GW. These estimates, they’re all over the place, they come from sell-side banks, they come from consultants, they come from everyone. BNEF has one. They’re I think one of the best out there.

Joe: Thank you.

Andy: We use them a lot. So that’s on the demand side on where you’re going to come out on these. Then you look at the supply – and everyone talks about the demand right – but then you look at the supply and all these tech bros are too cool to actually look at the supply and do utility analysis. Who wants to be a utility analyst? You were making fun of us before. So you look at the supply and these utilities are tracking all these data centers connecting to the grid because they’ve got to do a lot of work. Spend a lot of money on transmission, distribution, new substations, transformers, it’s a lot of work. But it boosts their earnings growth so they’re happy to talk about this. You look at where they’re at and where they see things coming, they’ve got around 140 GW of near-term supply. Kudos to the utilities, they break out what’s firm, committed, signed, contracted, versus pipeline behind it. Because there’s a lot of double, triple, quadruple counting. If you’re going to build a data center in the Southeast, you’re going to tell Duke, you’re going to tell Southern, you’re going to tell Dominion, you’re going to build one. So that’s the pipeline potential. But looking just at the firm, committed, whatever they want to call it, around 140 GW.

Now you got to PUE adjust that. When you connect a data center to the grid, you’ve got lights, you’ve got cooling. Those third party estimates I gave you are just for raw compute.

Tracy: Why did you split those out though? All data centers are going to need to be cooled down, right? What’s the point of splitting it out?

Andy: I’m not splitting up. I’m just adjusting it downward, because the third-party estimates are just compute. So you’re connecting to the grid, you’re going to ask for the lights, the cooling and everything. I want to go apples to apples versus the third party.

Joe: What does PUE stand for?

Andy: Power usage efficiency. So they’re at 140 GW. So that power is down to 110 GW on apples to apples. Just to go back, you only need 50 GW on the demand side between now and 2030. The utilities are working at connecting 110 GW, so the utilities are working on already connecting almost as much as you need by 2035. Again, just to make sure we are on the same page, third party estimates 45 GW for data centers now, going to 95 GW. That’s 50 GW. Utilities are working on 110 GW. They don’t give timing for that. Some of it’s going to be past 2030. What I’m trying to say is there is a lot of supply of data centers coming and it’s very unclear if there’s going to be demand for this…

…Tracy: The wild card to me seems to be the demand forecasts. We’re already seeing those change pretty wildly. I know you mentioned Bloomberg NEF – they’ve raised their forecast, because of the data center buildout. They’ve raised their forecast of how much energy is actually needed. How much confidence do you have in those demand numbers, and how could they change over time?

Andy: Moderate confidence. Look where we’re right now. OpenAI built all the ChatGPT using 2 GW. All the big tech hyperscalers, they haven’t given their 2025 volumes yet, but if you take their 2024 volumes and then double it – and this is output, so I’m going to transfer it back to capacity – and you assume a 60% capacity factor, all the hyperscalers combine around 15 GW. That’s got to be over half the data center demand. To talk about 95 GW – it’s a staggering number. Then you get more advances and Nvidia chip efficiency – obviously Jevon’s Paradox kicks in, you’ve had numerous guests talk about that – it’s just a lot of power.

Tracy: Can you just remind us 1 GW is enough to power what? I like these comparisons.

Andy: A million homes. It depends if you’re in Florida or the northeast. But generally speaking, that’s where you’re at…

…Andy: But then you don’t need as many new power plants as everyone’s saying. Constellation’s CEO said on a call the other day. He said, “Use the Texas market.” He said, “87 GW peak market, you could add 10 GW to Texas tomorrow, which would be the equivalent of sending every single Nvidia chip for an entire year to Texas and running them 24/7. That’s 10 GW. You could run it right now, existing grid, existing plants for all but 40-50 hours a year.” We stress tested it. There are some coal plants that could ramp up capacity factor. There’s plenty of gas plants that can. I don’t know if it’s 40 hours, 100 hours, 140 hours, but it makes more sense to pay someone else not to run their chemical company, the refinery company, for 40-50 hours a year, rather than have the utilities go out and spend $10 billion connecting faraway wind farms. That’s the argument. We’ve come in the middle of it, but there is plenty of existing capacity on the grid that could ramp up to meet it. Then other guests have pointed out at Odd Lots, the peak demand of the grid is 850 GW. The overall size of the grid is are 1,200 GW and then you’re adding 50 GW a year of solar, and then you’re going to start adding 20 GW of gas. We’re going to handle it. I’m not really worried about any brownouts or anything.

3. Incentives > Intelligence: The Real Barrier(s) to Agentic AI – Abdullah Al-Rezwan

Such “disingenuous yet clever” strategy is actually a good glimpse of the barrier to agentic AI’s adoption. While most of us focus too much on technical capabilities of AI, we may still be underestimating the challenges related to (lack of) incentives of incumbents as well as legal frameworks for agentic AIs to flourish. “Ghosts of Electricity” had a very good piece explicitly laying out couple of real headaches:

“we highlight two main obstacles that stand in the way of AI agents becoming true digital partners. The first has to do with the design of the internet itself–the interface of nearly every website was meticulously optimized for humans. But what works for humans does not necessarily work for AI agents. Until AI can truly emulate every aspect of a human being, we will likely need to design a parallel internet for agentic commerce to work. But there’s reasons to suspect that this will not happen soon: some firms have little to gain, and potentially much to lose, from investing and facilitating a machine-readable web. This leads us to the second obstacle, which is even simpler: many use-cases for AI agents are illegal, or at least legally ambiguous. The rights around AI agents need to be clarified and developed in order for agents to participate meaningfully in economic transactions and interactions.”

In the piece, they substantiated these headaches with a couple of examples. Some excerpts below:

“Let’s say you tell your favorite AI tool (ChatGPT Atlas, Perplexity Comet, Claude, Gemini Antigravity) to purchase a concert ticket for you or to shop on Amazon. Take seat selection. The agent reaches the seat map and gets stuck because it can’t tell what’s actually available or what counts as a “good” choice. The map isn’t a simple list: seats change color when you hover, prices only appear after clicking, and availability updates every second as other people buy tickets. While the agent pauses to figure out what to do, the seat disappears, the page refreshes, and it loses its place. Every pause, waiting for pages to load, retrying after errors, handing control back to you, adds friction. What takes a human a few minutes to do turns into a brittle, ten-minute ordeal

4. The Slow Singularity – Abdullah Al-Rezwan

To understand why the future might be sluggish, the authors first had to decode the past. In a methodological twist that fits the subject matter perfectly, they employed OpenAI’s Deep Research to dig through economic history and construct a dataset of 150 essential tasks over the last century. This analysis revealed a counterintuitive “Zero Productivity Paradox” as switching a task from labor to capital contributes zero to Total Factor Productivity (TFP) growth at the exact moment it happens. This is because firms switch exactly when the costs are equal. The growth comes entirely from what happens after the switch: the task is now performed by a machine that improves exponentially faster than a human.

They estimate that while machine productivity on automated tasks grows at a blistering 5% annually, human task efficiency grows at a meager 0.5% and in some sectors, human efficiency appears to be declining. To prove how vital this dynamic is, they calculated a “frozen” counterfactual: if we had stopped automating new tasks in 1950, but allowed computers to keep getting faster at the things they were already doing, US economic growth would have essentially flatlined for the last 70 years…

…The same logic explains why the AI “singularity” is likely to be a slow burn rather than an explosion. The economy operates on a “weak link” principle. Production requires a chain of complementary tasks; you need high-speed coding, but you also need management, legal compliance, physical logistics etc. Because these tasks are interlinked, the economy is constrained by its slowest components. Even if AI automates cognitive tasks with infinite speed, total output remains bottlenecked by the essential tasks that still require slow-improving human labor.

5. The Hidden Book Value of Community Banks: Why Call Reports Matter More Than Public Financials – Dirt Cheap Banks

Call Reports exist for safety and soundness, not for investors. They are not designed to be friendly, summarized, or marketed. They are designed to tell regulators whether a bank can survive stress, fund itself, and absorb losses. That is exactly why they are so valuable.

The first thing to understand is structure. When you buy stock in a small community bank, you are almost always buying the holding company, not the bank itself. The holding company often has no real operations. It owns one asset, the bank. It might have a little cash, maybe some legal expenses, sometimes holding company debt, but that is it. The bank owns the loans, the deposits, the securities, the real estate, and the earnings power…

…Public financial statements typically show the holding company only, and often only once a year…

…Call Reports are different. They are filed quarterly by the bank itself. They show the full balance sheet, income statement, and capital position of the operating bank. If the bank earns money and retains it, equity goes up in the Call Report immediately, whether or not a dividend is paid to the parent. If securities move and AOCI changes, you see it. If credit costs rise, you see it. If loan growth accelerates, you see it.

When people ask which book value is the real one, the answer from decades of bank investing is simple. The bank level equity in the Call Report is the economic book value. That is what generates earnings. That is what a buyer would pay for in a sale. That is what regulators protect. The parent level equity is just an accounting wrapper…

…West Shore Bank Corporation $WSSH is a textbook case of how public financials can materially misstate economic reality for small community banks, and why Call Reports create an information advantage…

…At December 31, 2024, the consolidated balance sheet shows:

Total stockholders’ equity of approximately $48.2 million.

This is the number scraped by data aggregators. It is the number displayed on OTC Markets. It is the number most investors implicitly anchor to when thinking about book value.

With a current market capitalization of roughly $45 million, West Shore appears to be trading at or near book value based on these public financials. To a casual observer, the stock looks fairly valued. There is no obvious discount screaming off the page…

…In the Call Report, under Total bank equity capital, the number is dramatically higher.

As of the most recent Call Report dated 9/30/2025, total bank equity capital is approximately $73 million.

This is the capital base regulators use to determine whether the bank is well capitalized. It reflects retained earnings, balance sheet growth, and changes in AOCI on a quarterly basis.

Nothing magical happened between these two documents. There was no recapitalization. No asset sale. No accounting maneuver.

The difference exists because the two statements are answering different questions.

The annual report answers:

What does the holding company’s GAAP equity look like at year end?

The Call Report answers:

How much capital does the operating bank have today?

Those are not the same question, and in small community banks, the answers often diverge significantly over time.

Using the same $45 million market capitalization:

Based on public financials, West Shore appears to trade at roughly 0.9x to 1.0x book value
Based on Call Report data, West Shore is trading at approximately 0.6x bank-level book value

That is the entire disconnect.

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 Gemini) and Amazon. Holdings are subject to change at any time.

What We’re Reading (Week Ending 01 February 2026)

The best articles we’ve read in recent times on a wide range of topics, including investing, business, and the world in general.

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!

Here are the articles for the week ending 01 February 2026:

1. Anthropic Lowers Gross Margin Projection as Revenue Skyrockets – Sri Muppidi

Anthropic last month projected it would generate a 40% gross profit margin from selling AI to businesses and application developers in 2025, according to two people with knowledge of its financials. That margin was 10 percentage points lower than its earlier optimistic expectations, though it’s still a big improvement from the year before…

…If Anthropic also counted inference costs for Claude chatbot users that don’t pay for a subscription, its gross margin would be about 38%, or a few percentage points lower than for paid users, based on The Information’s analysis…

…Anthropic has previously projected gross margins above 70% by 2027, and OpenAI has projected gross margins of at least 70% by 2029, which would put them closer to the gross margins of publicly traded software and cloud firms. But both AI developers also spend a tremendous amount on renting servers to develop new models—training costs, which don’t factor into gross margins—making it more difficult to turn a net profit than it is for traditional software firms.

The inference costs are in addition to costs from training the models. Anthropic last month expected its costs for training its AI models for 2025 to be roughly $4.1 billion, up roughly 5% from its summer projections. OpenAI, meanwhile, expected to spend $9.4 billion on compute for training its AI models last year.

2. A business that scales with the value of intelligence – Sarah Friar

We launched ChatGPT as a research preview to understand what would happen if we put frontier intelligence directly in people’s hands…

…As ChatGPT became a tool people rely on every day to get real work done, we followed a simple and enduring principle: our business model should scale with the value intelligence delivers…

…Looking back on the past three years, our ability to serve customers—as measured by revenue—directly tracks available compute: Compute grew 3X year over year or 9.5X from 2023 to 2025: 0.2 GW in 2023, 0.6 GW in 2024, and ~1.9 GW in 2025. While revenue followed the same curve growing 3X year over year, or 10X from 2023 to 2025: $2B ARR in 2023, $6B in 2024, and $20B+ in 2025. This is never-before-seen growth at such scale. And we firmly believe that more compute in these periods would have led to faster customer adoption and monetization.

3. 50x in 5 Years – Joe Raymond

I discovered Cable Information Systems (CIS) in a page of one-liner descriptions of companies in the OTC edition of Moody’s Manual.

It was trading for a dollar per share…

…Believe it or not, Cable Information Systems had 50,000 subscribers in 1980 which placed them in the top 10 U.S. cable companies.

The company had about 1 million shares outstanding which were inactively trading at a dollar a share in the pink sheets.

At the time, it was said that cable subscribers were going to be worth $1,000 each to an operator of cable services. Thus, it became apparent that Cable Information with 1 million shares outstanding was worth $50 million although it was selling at a market value of only $1 million.

A second way of valuing a cable company was to apply the then-going multiple to cash flow, deduct debt, and divide by outstanding shares. Doing that I also came up with $50 a share.

So, using the two ways of valuing a cable company at the time, I found a $1 stock worth $50.

I asked Peter if he knew of anyone that cared to sell shares, and he told me that some of the employees were shareholders and, from time to time, some of them were interested in selling. I asked him if he would give them my name and number and he said gladly, they’d be happy to know of me.

Over a period of months, some of these employees called and asked if I would buy their shares. I said yes, I am glad to pay the current market price of approximately $1 per share.

Before buying, I told any caller offering shares to me, “Look, I want to make clear to you that I’m buying because I think the shares are worth a heck of a lot more than a dollar and if I were you, I would not be eager to sell.”

As employees, I wanted them to know I felt strongly it was not a good idea to sell. After questioning them and explaining why they should not sell, some people still sold me their shares…

…Late in the year, 1981, Peter telephoned me to tell me that he was selling out at $48 in cash to John Malone, who was the biggest cable operator in the United States.

My first reaction was, “Wow, two dollars short of what we had calculated it was worth.”

But Peter told me that there were two dollars being put into escrow and they will probably be paid to shareholders as well, bringing the total consideration to $50…

…Here’s what Larry was looking at in Moody’s Manual back in 1977:

Sales were growing double digits and accelerating. Margins were expanding.

The stock traded between $0.38 and $1.00 in 1977. The normalized P/E ratio was 1x on the low end and 3x on the high end.

There was some debt, as was common with fast growing cable companies at the time. The EV/EBITDA at $1 per share was 5x.

4. My Interview With Andy Jassy: OpenAI, Trump, Power and the Future of AWS – Jessica E. Lessin and Andy Jassy

Andy Jassy: I think that we’re excited about agentic commerce. I think that it has the chance to make it easier for customers to find what they want. If you know what you want, it’s pretty hard to find a better experience than popping onto Amazon and searching and finding it.

But the one place still where physical retail has some advantages, in my opinion, is the ability to go in, not know what you want, ask questions, refine those questions, have somebody point you to different things. And I think agents are going to help customers with that type of discovery. And it’s part of why we’ve invested so much in Rufus, which is our shopping assistant, which has really gotten quite good.

And I think that over time that we will work with other third-party agents as well. I think today the experience hasn’t been great yet. You know, I think that a lot of these third-party agents, they don’t have your buying history, they don’t have what you like, a lot of the information about pricing and the product is off.

But over time, I do believe that will get better. I also think there needs to be the right value exchange between the agents and between the retailers themselves, but I am optimistic that those will work out. We’re having conversations with lots of people and I’m very bullish on agentic commerce…

…Jassy: As you know, the chips are such an important part of the performance and the cost structure for people running technology infrastructure. We learned in the CPU side of the business, we had this deep relationship with Intel, which we still do. But when you have a significant leader, it’s not always their priority to take price performance down for customers.

And one thing we learn about customers over and over and over again is they want better price performance. And so we built Graviton, our own custom CPU silicon, which is about 40% more price performance than the leading other x86 processors. And that has been really great for our customers and business.

And about 90% of our top 1,000 customers now use Graviton in a very significant way. And we just saw this same movie happening in the AI space. And we have a very deep partnership with Nvidia, and we will for as long as I can foresee. But customers badly want better price performance. And so that’s why we built Trainium.

Our Trainium2 chip has been fully subscribed. Anthropic runs hundreds of thousands of Trainium2 chips as they’re training their next model of Claude on top of it. It’s a multi-billion dollar business. And we just released Trainium3 which is our next version of chip, which is 40% more price performant than Trainium2.

And Trainium2 was about 30% to 40% more price performant than the other leading GPUs out there. If you want to allow customers to be able to use AI as expansively as they want, you must take the cost of inference down. And the chip is a big piece of it…

…[Jassy:] I think we’re just in this stage right now where there is so much demand. And, you know, we’re not at this point, we’re not just trying to guess whether there’s demand. We have so much demand. I think the industry would tell you as a whole, there is still not enough capacity, even though it’s gotten better than it was 18 months ago, we could still be growing faster if we had more capacity…

…[Jassy:] We’re in this really interesting stage of AI adoption, in my opinion. It’s very bar-belled.

You have a lot of use by the AI labs who are consuming gobs and gobs of compute right now, and maybe a runaway app or two like ChatGPT. Then the other side of the barbell are enterprises who are really using AI for cost avoidance or productivity. Customer service, business process automation, things like that.

But the middle of that barbell are all the enterprise workloads in production that are not using inference yet. That will. We’re still at this relatively early stage. I believe that the middle part of the barbell is going to be the largest absolute segment. And I think when enterprises get to deploying their production apps using inference and AI, they’re going to want those applications to run close to the rest of their other applications and where their data is.

And just the largest amount by a fair bit, resides in AWS. And so we’re making it easier and easier for customers to be able to run their core workloads with their AI workloads.

5. An Early Buffett Partnership Investment – Joe Raymond

The first investment Buffett disclosed in his partnership letters was Commonwealth Trust in 1958…

…Buffett started buying Commonwealth at $50 and thought it was worth $125…

…Warren was paying 5x earnings and 80% of book value. Seems like a good deal for a bank earning 20% on equity.

The second is the nature of the bank.

Commonwealth Trust had $50 million of deposits and only $20 million of loans, most of which were residential mortgages. It also had $21 million of government securities.

The asset mix appeared highly conservative, at least from a credit perspective.

While the assets looked solid, there was little equity in the business ($2 million of equity on $53 million of assets). You don’t see this sort of leverage today, but it was common practice amongst small thrifts in the ’50s…

…A sharp increase in reserves, coinciding with rising interest rates, caused a big hit in 1954. This was magnified by the fact that Commonwealth’s equity was only 4% of assets going into the year. Book value per share fell 34%.

By the time Buffett was buying in 1957, interest rates were moderating, reserves were healthy, and earnings and equity were about to resume their growth…

…Warren didn’t hold long.

He sold his shares for $80 apiece about a year after buying them.

This was a 25% premium over the prevailing market price at the time and represented a profit of 57% for the partnerships…

…Buffett said the buyer at $80 could expect to do well over time and that he was selling to recycle the proceeds into a better opportunity (Sanborn Map)…

…About a year after Buffett sold, Commonwealth Trust merged with Hudson County National Bank (HCNB). It was a share-for-share deal, and the combined bank kept the Hudson County name…

…Over the next eight years, HCNB grew its book value from $135 to $183 per share (4% CAGR) and paid $57 per share of dividends. The average stock price in 1968 was $228 (1.25x book value).

So, the buyer from Buffett at $80 in 1958 had $228 by 1968 plus $58 of dividends.

Including dividends, the total annual return was in the mid-teens…

…This is a good example of successful value investing.

Corporate performance was mediocre, but big follies were avoided. Equity grew slowly and dividends were paid.

A cheap entry price and average exit price produced a mid-teens IRR over more than a decade.

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

What We’re Reading (Week Ending 18 January 2026)

The best articles we’ve read in recent times on a wide range of topics, including investing, business, and the world in general.

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!

Here are the articles for the week ending 18 January 2026:

1. “The Compute Theory of Everything” – Abdullah Al-Rezwan

Albanie referred two seminal essays by Hans Moravec: “The Role of Raw Power in Intelligence” (1976), and “When will computer hardware match the human brain?” (1998)

I glanced through the first essay, but read the second one. I was moved just by reading the abstract of the paper:

“This paper describes how the performance of AI machines tends to improve at the same pace that AI researchers get access to faster hardware. The processing power and memory capacity necessary to match general intellectual performance of the human brain are estimated. Based on extrapolation of past trends and on examination of technologies under development, it is predicted that the required hardware will be available in cheap machines in the 2020s.”…

…Despite acknowledging valid reasons to harbor skepticism, Moravec relied on his simple observations on computing:

“Computers doubled in capacity every two years after the war, a pace that became an industry given: companies that wished to grow sought to exceed it, companies that failed to keep up lost business. In the 1980s the doubling time contracted to 18 months, and computer performance in the late 1990s seems to be doubling every 12 months…

…At the present rate, computers suitable for humanlike robots will appear in the 2020s. Can the pace be sustained for another three decades? The graph shows no sign of abatement. If anything, it hints that further contractions in time scale are in store. But, one often encounters thoughtful articles by knowledgeable people in the semiconductor industry giving detailed reasons why the decades of phenomenal growth must soon come to an end.”

2. Venezuelan Historical Primer: Friend, Foe, Vassal – Collapse Intelligence Agency

Before the US Shale Revolution (Fracking) in ~2010, the consensus view among energy majors was that US domestic light sweet oil was dying.

It was thought the world had burned all the easy, high-quality oil. Future reserves were geographically concentrated in the Middle East or were “Trash Grade” (Canadian Bitumen, Venezuelan Extra-Heavy, Mexican Maya).

US Refiners (Valero, Chevron, LyondellBasell) decided that to stay profitable, they had to spend billions upgrading their facilities to process the “Trash Grade” oil that nobody else wanted. They built massive Delayed Cokers and Hydrocrackers.

By building machines that could eat $10/barrel sludge and turn it into $50/barrel gasoline, they guaranteed massive margins that simple refineries in Europe couldn’t touch…

…Meanwhile, Gulf Coast refiners weren’t building just for Venezuela; they were building for the neighborhood.

In the 90s, Mexico’s massive Cantarell Field was pumping huge volumes of “Maya” crude (heavy/sour)

Venezuela had Orinoco (extra heavy/sour).

The logic was that the Gulf of Mexico basin was destined to be the global hub for processing heavy oil. Refiners poured tens of billions of dollars into capital expenditures (CapEx) to optimize specifically for this metallurgic sludge…

…When fracking exploded in 2010, the US flooded the market with Light Sweet Crude (LTO).

The US refiners looked at all this light oil and realized, “We can’t use it efficiently.”

If you put Light Oil into a refinery built for Heavy Sludge, you run the equipment inefficiently. You under-utilize the coker units (billions in wasted sunk costs).

The US exports its own high-quality light oil to Asia/Europe (who have simple refineries) and must import heavy oil to satisfy the diet of the Gulf Coast processing complex.

The capacity exists because Venezuela effectively paid to build it (via Citgo) and US executives in the 90s bet the house that heavy oil was the only game in town. Formerly permissive national economic policies supercharged the technological development.

The recent US military operation isn’t just about seizing new resources; it’s about feeding a starving industrial monster that was specifically designed to eat only what Venezuela produces. And that industrial monster must feed the US economy because now the shale party is about to end. The US administration knows this. They have made a 100% rational decision to force a bloody showdown with Venezuela to fund US energy needs.

3. The AI revolution is here. Will the economy survive the transition? – Michael Burry, Dwarkesh Patel, Patrick McKenzie, and Jack Clark

Jack: Yes, something we say often to policymakers at Anthropic is “This is the worst it will ever be!” and it’s really hard to convey to them just how important that ends up being. The other thing which is unintuitive is how quickly capabilities improve—one current example is how many people are currently playing with Opus 4.5 in Claude Code and saying some variation of “Wow, this stuff is so much better than it was before.” If you last played with LLMs in November, you’re now wildly miscalibrated about the frontier…

…Dwarkesh: The million-dollar question is whether the METR productivity study (which shows that developers working in codebases they understood well had a roughly 20% decrease on merging pull requests from coding tools) or human equivalent time horizons of self-contained coding tasks (which are already in the many-hours range and doubling every four to seven months) is a better measure of how much speedup researchers and engineers at labs are actually getting. I don’t have direct experience here, but I’d guess it’s closer to the former, given that there isn’t a great feedback verification loop and the criteria are open-ended (maintainability, taste, etc.).

Jack: Agreed, this is a crucial question—and the data is conflicting and sparse. For example, we did a survey of developers at Anthropic and saw a self-reported 50% productivity boost from the 60% of those surveyed who used Claude in their work. But then things like the METR study would seem to contradict that. We need better data and, specifically, instrumentation for developers inside and outside the AI labs to see what is going on. To zoom out a bit, the massive and unprecedented uptake of coding tools does suggest people are seeing some major subjective benefit from using them—it would be very unintuitive if an increasing percentage of developers were enthusiastically making themselves less productive…

…Michael: Do you think the podium will keep rotating? From what I’m hearing, Google is winning among developers from both AWS and Microsoft. And it seems the “search inertia” has been purged at the company.

Dwarkesh: Interesting. Seems more competitive than ever to me. The Twitter vibes are great for both Opus 4.5 and Gemini 3.5 Pro. No opinion on which company will win, but it definitely doesn’t seem settled.

Jack: Seems more competitive than ever to me, also!…

…Jack: Coding has a nice property of being relatively “closed loop”—you use an LLM to generate or tweak code, which you then validate and push into production. It really took the arrival of a broader set of tools for LLMs to take on this “closed loop” property in domains outside of coding—for instance, the creation of web search capabilities and the arrival of stuff like Model Context Protocol (MCP) connectivity has allowed LLMs to massively expand their “closed loop” utility beyond coding.

As an example, I’ve been doing research on the cost curves of various things recently (e.g. dollars of mass to orbit, or dollars per watt from solar), and it’s the kind of thing you could research with LLMs prior to these tools, but it had immense amounts of friction and forced you to go back and forth between the LLM and everything else. Now that friction has been taken away, you’re seeing greater uptake. Therefore, I expect we’re about to see what happened to coders happen to knowledge workers more broadly—and this feels like it should show up in a diffuse but broad way across areas like science research, the law, academia, consultancy, and other domains.

Michael: At the end of the day, AI has to be purchased by someone. Someone out there pays for a good or service. That is GDP. And that spending grows at GDP rates, 2% to 4%—with perhaps some uplift for companies with pricing power, which doesn’t seem likely in the future of AI.

Economies don’t have magically expanding pies. They have arithmetically constrained pies. Nothing fancy. The entire software pie—SaaS software running all kinds of corporate and creative functions—is less than $1 trillion. This is why I keep coming back to the infrastructure-to-application ratio—Nvidia selling $400 billion of chips for less than $100 billion in end-user AI product revenue.

AI has to grow productivity and create new categories of spending that don’t cannibalize other categories. This is all very hard to do. Will AI grow productivity enough? That is debatable. The capital expenditure spending cycle is faith-based and FOMO-based. No one is pointing to numbers that work. Yet.

There is a much simpler narrative out there that AI will make everything so much better that spending will explode. It is more likely to take spending in. If AI replaces a $500 seat license with a $50 one, that is great for productivity but is deflationary for productivity spend. And that productivity gained is likely to be shared by all competitors…

…Michael: At some point, this spending on the AI buildout has to have a return on investment higher than the cost of that investment, or there is just no economic value added. If a company is bigger because it borrowed a lot more or spent all its cash flow on something low-return, that is not an attractive quality to an investor, and the multiple will fall. There are many non-tech companies printing cash with no real prospects for growth beyond buying it, and they trade at about 8x earnings…

…Michael: Well, value accrues, historically, in all industries, to those with a durable competitive advantage manifesting as either pricing power or an untouchable cost or distribution advantage.

It is not clear that the spending here will lead to that.

Warren Buffett owned a department store in the late 1960s. When the department store across the street put an escalator in, he had to, too. In the end, neither benefited from that expensive project. No durable margin improvement or cost improvement, and both were in the same exact spot. That is how most AI implementation will play out.

This is why trillions of dollars of spending with no clear path to utilization by the real economy is so concerning. Most will not benefit, because their competitors will benefit to the same extent, and neither will have a competitive advantage because of it.

I think the market is most wrong about the two poster children for AI: Nvidia and Palantir. These are two of the luckiest companies. They adapted well, but they are lucky because when this all started, neither had designed a product for AI. But they are getting used as such.

Nvidia’s advantage is not durable. SLMs and ASICs are the future for most use cases in AI. They will be backward-compatible with CUDA [Nvidia’s parallel computing platform and programming model] if at all necessary. Nvidia is the power-hungry, dirty solution holding the fort until the competition comes in with a completely different approach…

…Jack: The main thing I worry about is whether people succeed at “building AI that builds AI”—fully closing the loop on AI R&D (sometimes called recursively self-improving AI). To be clear, I assign essentially zero likelihood to there being recursively self-improving AI systems on the planet in January 2026, but we do see extremely early signs of AI getting better at doing components of AI research, ranging from kernel development to autonomously fine-tuning open-weight models…

…Michael: If I had the ear of senior policymakers, I would ask them to take a trillion dollars (since trillions just get thrown around like millions now) and bypass all the protests and regulations and dot the whole country with small nuclear reactors, while also building a brand-new, state-of-the-art grid for everyone. Do this as soon as possible and secure it all from attack with the latest physical and cybersecurity; maybe even create a special Nuclear Defense Force that protects each facility, funded federally.

This is the only hope of getting enough power to keep up with China, and it is the only hope we have as a country to grow enough to ultimately pay off our debt and guarantee long-term security, by not letting power be a limiting factor on our innovation.

4. Is Venezuela’s Oil Worth the Hassle? – Tomas Pueyo

This depends on how much oil can be extracted from Venezuela. Today, it’s ~1.1M barrels per day.

A barrel of oil is currently worth about $60:

But Venezuela’s oil is worse quality than most, so it sells for cheaper, ~$8 less as of today, or $52…

…But how much does it cost to extract a barrel of Orinoco oil and transport it and treat it to be sellable?

So of these $52, about $23 are hard costs, and each barrel yields around $29 in profit…

…The oil [in the Orinoco Valley] is extremely dense (heavier than water), extremely viscous (like pitch or molasses) and extremely dirty (over 5% sulfur and masses of metals like vanadium). The only deposit like this elsewhere in the world is Canada’s Athabasca oil sands.

To extract the oil, you have to first pump large amounts of steam into the formation, to melt the hydrocarbons, then use electrical pumps at the surface or in the bottom of the well, up to a kilometer deep, to lift it to the surface. Once there, the “oil” is far too viscous to transport by pipeline or ship, and far too heavy and dirty for most refineries to tackle. So it is diluted by mixing with a much lighter crude oil, or the “condensate” liquids from a gas field, or refined naphtha (a solvent which you can buy as “white spirit” in UK DIY stores). The resulting diluted crude oil (DCO) is exported as Merey blend. This is still one of the heaviest, dirtiest crude oils in the world (16 API, 3.5% sulfur, high acidity and metals content), but it flows just well enough to be transported if kept warm, and some of the world’s more complex refineries can handle it, and make transport fuels from it, although usually alongside other lighter crudes…

…The two best estimates suggest it would take tens of billions to maintain the existing infrastructure, and tens of billions more to go beyond that.

5. A Few Things I’m Pretty Sure About – Morgan Housel

I think the majority of society problems are all downstream of housing affordability. The median age of first-time homebuyers went from 29 in 1981 to 40 today. But the shock this causes is so much deeper than housing. When young people are shut out of the life-defining step of having their own place, they’re less likely to get married, less likely to have kids, have worse mental health, and – my theory – more likely to have extreme political views, because when you don’t feel financially invested in your community you’re less likely to care about the consequences of bad policy…

…There’s a long history of Americans cycling through how they feel about government and how politicians treat each other.

The 1930s were unbelievably vicious. There was a well organized plot to overthrow Franklin Roosevelt and replace him with a Marine general named Smedley Butler, who would effectively become dictator. The Great Depression made Americans lose so much faith in government that the prevailing view was, “hey, might as well give this a shot.”

It would have sounded preposterous if someone told you in the 1930s that by the 1950s more than 70% of Americans said they trusted the government to do the right thing almost all the time. But that’s what happened.

And it would have sounded preposterous in the 1950s if you told Americans within 20 years trust would collapse amid the Vietnam War and Watergate.

It would have sounded preposterous if you told Americans in the 1970s that within 20 years trust and faith in government would have surged amid 1990s prosperity and balanced budgets.

And equally absurd if you told Americans in the 1990s that we’d be where we are today.

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 (parent of AWS), and Microsoft. Holdings are subject to change at any time.

What We’re Reading (Week Ending 11 January 2026)

The best articles we’ve read in recent times on a wide range of topics, including investing, business, and the world in general.

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!

Here are the articles for the week ending 11 January 2026:

1. Ten things about Venezuela: on oil, geopolitics and drugs – Michael Cembalest

Venezuela is not a large part of the global oil production picture, at least not right now. The impact on global oil markets from the US invasion/arrest of Maduro should be minor…

…The US is still highly reliant on petroleum for 90% of transport energy consumption with the remainder mostly made up of natural gas and biomass, and for ~33% of industrial production (mostly high temperature heat and industrial feedstocks). The amounts of oil used for residential and commercial heating is pretty negligible, less than 10% of the respective totals…

…The oil intensity of GDP is gradually declining in most of the world. At some point, this ratio may drop low enough that disruptions in oil supplies will be less of an issue for growth and consumer spending…

…While US oil production is tilted towards light oil, US refining capacity is more even split among light, medium and heavier grades. Note how heavy and medium normalized oil production in Venezuela aligns better with US refining gaps…

…Venezuela also possesses largely untapped reserves of critical minerals like coltan (niobium-tantalum), rare earth elements (REEs), nickel, gold, bauxite and iron ore. The Orinoco Mining Arc, which spans 111,843 sq km, contains documented deposits of coltan (tantalum ore), cassiterite (tin ore), rare earth elements, bauxite, gold, and lithium reserves. Coltan is used for manufacturing tantalum capacitors used in advanced electronic systems, including military communications equipment, missile guidance computers and radar systems. Rare earth elements enable permanent magnets required for precision-guided munitions, aircraft actuators and electromagnetic systems. Cassiterite provides tin for solder in electronics assembly, including defense systems while bauxite feeds aluminum production for aerospace applications…

…Iran and Venezuela have exchanged oil, gold and infrastructure assistance using Iran’s Islamic Revolutionary Guard Corps and Hezbollah-linked front companies for money laundering and sanctions evasion…

…Over 120 Russian troops reportedly operate in Venezuela and lead the “Equator Task Force.” Russian advisers provide training across multiple domains including infantry, drone operations, special forces, military intelligence, signals intelligence, armor, aircraft, artillery and domestic surveillance

China has extensive ties with Venezuela; note the disproportionate amount of Chinese loans to Venezuela vs other Latin American countries (most of these loans were originated over a decade ago). China’s military connections with Venezuela involve arms sales (missiles, jets, naval vessels), defense cooperation and strategic support; it’s not clear what the benefit has been for Venezuela, at least based on last week.

2. Steam, Steel, and Infinite Minds – Ivan Zhao

My co-founder Simon was what we call a 10× programmer, but he rarely writes code these days. Walk by his desk and you’ll see him orchestrating three or four AI coding agents at once, and they don’t just type faster, they think, which together makes him a 30-40× engineer. He queues tasks before lunch or bed, letting them work while he’s away. He’s become a manager of infinite minds…

…With AI agents, someone like Simon has graduated from riding a bicycle to driving a car.

When will other types of knowledge workers get cars? Two problems must be solved.

First, context fragmentation. For coding, tools and context tend to live in one place: the IDE, the repo, the terminal. But general knowledge work is scattered across dozens of tools. Imagine an AI agent trying to draft a product brief: it needs to pull from Slack threads, a strategy doc, last quarter’s metrics in a dashboard, and institutional memory that lives only in someone’s head. Today, humans are the glue, stitching all that together with copy-paste and switching between browser tabs. Until that context is consolidated, agents will stay stuck in narrow use-cases.

The second missing ingredient is verifiability. Code has a magical property: you can verify it with tests and errors. Model makers use this to train AI to get better at coding (e.g. reinforcement learning). But how do you verify if a project is managed well, or if a strategy memo is any good? We haven’t yet found ways to improve models for general knowledge work. So humans still need to be in the loop to supervise, guide, and show what good looks like…

…Before steel, buildings in the 19th century had a limit of six or seven floors. Iron was strong but brittle and heavy; add more floors, and the structure collapsed under its own weight. Steel changed everything. It’s strong yet malleable. Frames could be lighter, walls thinner, and suddenly buildings could rise dozens of stories. New kinds of buildings became possible.

AI is steel for organizations. It has the potential to maintain context across workflows and surface decisions when needed without the noise. Human communication no longer has to be the load-bearing wall. The weekly two-hour alignment meeting becomes a five-minute async review. The executive decision that required three levels of approval might soon happen in minutes. Companies can scale, truly scale, without the degradation we’ve accepted as inevitable…

… At the beginning of the Industrial Revolution, early textile factories sat next to rivers and streams and were powered by waterwheels. When the steam engine arrived, factory owners initially swapped waterwheels for steam engines and kept everything else the same. Productivity gains were modest.

The real breakthrough came when factory owners realized they could decouple from water entirely. They built larger mills closer to workers, ports, and raw materials. And they redesigned their factories around steam engines (Later, when electricity came online, owners further decentralized away from a central power shaft and placed smaller engines around the factory for different machines.) Productivity exploded, and the Second Industrial Revolution really took off.

We’re still in the “swap out the waterwheel” phase. AI chatbots bolted onto existing tools. We haven’t reimagined what organizations look like when the old constraints dissolve and your company can run on infinite minds that work while you sleep.

3. Our Approach to the Future – Hirotaka Shimizu

Venture companies seeking to go public typically expand by increasing sales through their hard-earned business models. Once sales exceed the break-even point, they begin to generate profits. During this process, they develop the organizational structures, governance frameworks, and compliance systems required of listed companies, steadily advancing toward an IPO. Only a limited number of these companies, under favorable conditions, ultimately succeed in going public.

Yet many of those that do achieve an IPO, often after significant struggles and setbacks, find their growth peak around the time of listing. According to the Ministry of Economy, Trade and Industry’s March 2024 report, “Research on How Startups Can Continue to Grow after Listing,” market capitalization growth typically peaks in the first year after listing and then declines uniformly from the second year onward. In fact, although the Tokyo Stock Exchange (TSE) Growth Market is intended to function as a gateway to higher-tier markets, only about one quarter of listed companies successfully make such a transition. Most are unable to achieve their anticipated growth trajectory and remain on the Growth Market. This is why IPOs are sometimes called jokingly by the public as “the final goal” of venture companies. To address this issue, the TSE reportedly plans to revise its continued listing criteria for the Growth Market by requiring companies listed for five years or more to have a market capitalization of at least ¥10 billion, thereby encouraging stronger post-listing growth.

Now then, why does growth come to a halt? There must be a reason. In my view, many venture owners concentrate too much of their attention and energy on their hard-earned business models. Yet all business models, even highly unique ones, have a shelf life. Every business inevitably moves from a growth phase to a maturity phase, and eventually to a decline phase. Companies that push aggressively during the growth phase and succeed in going public often discover that the differentiation they once created has diminished by the time they reach maturity. Their once-unique business models are imitated by competitors, or they unavoidably face intensified competition from companies with adjacent business models. As a result, they find themselves in a red ocean. In addition, the company growth cycle itself is shortening as information and technology continue to advance. This trend is particularly evident among venture companies in B2B marketing and technology domains. Once they face such situations, developing a new business model becomes increasingly difficult. Furthermore, listed companies are required to disclose financial information on a quarterly basis. In my view, this requirement can also discourage new investment, given the potential impact on share prices. I suspect that the current framework functions as a kind of “trap” into which many companies that manage to go public eventually fall.

To avoid this outcome, companies must continually conceive and pursue new business models while their existing models are still in a growth phase. However, most business managers fail to direct their attention to this imperative. In my view, this is because they lack long-term, ambitious goals. If managers were to set long-term goals, they would recognize that such goals cannot be achieved through a single business model and would therefore feel a natural imperative to develop the next one. Companies should, in my opinion, pursue growth driven by long-term goals, such as missions, visions, principles, aspirations, and ambitions, rather than relying on business models. I believe that the continued pursuit of these goals ultimately enables sustained corporate growth.

4. Peace and prosperity in Venezuela will come from democracy, not oil – Ricardo Hausmann

But then, concern: just hours after the raid President Donald Trump declared that he would now “run” Venezuela. He talked much about oil but not at all about democracy other than to dismiss María Corina Machado, Nobel peace laureate and leader of the democratic opposition…

…Instead, Mr Trump made clear, America will work with the dictator’s own vice-president. He spoke as if he owned the country and its assets. Venezuelans will be recipients of his benevolence, not agents of their destiny.

Removing a dictator—especially if leaving his henchmen and -women in charge—is not the same as rebuilding a country. And there is much to rebuild. When Mr Maduro came to power in 2013, Venezuelans were four times richer than they are today. A disaster followed: the largest economic contraction ever recorded in peacetime, triggering the departure of 8m Venezuelans. Brutality, repression and corruption accompanied the catastrophe.

At its heart was a systematic dismantling of rights: property rights, independent courts and free elections. Speaking out became a crime. As rights vanished, so did security, investment, trust and the power to imagine. People stopped planning for the future because the future no longer belonged to them.

The lesson is simple: prosperity does not come from oil, decrees or even benevolent rulers, but from rights. Rights create private property and security. They allow people to invest, innovate and dream. Restore rights, and society can recover.

Venezuelans now need neither revenge nor Trumpian improvisation, but a return to freedom and peace. The technology for that has already been invented: democracy, which is not just about voting but is a system for aggregating preferences while protecting liberties. Democracy aligns political authority with social consent and is the formula for sustained prosperity. Venezuela enjoyed it for much of the latter part of the 20th century.

5. Trump’s Enormous C-Length Win over China – Collapse Intelligence Agency

When we talk about “Oil,” we are using a lazy bucket term. In reality, a barrel of oil is a soup of thousands of different molecules. Each geographic barrel is a unique fingerprint.

“C-Length” refers to the number of Carbon atoms chained together in a single molecule.

This is the fundamental biophysics of the economy. The length of the carbon chain determines State of Matter (Gas vs. Liquid vs. Solid) and Energy Density (how much work it can do).

Short Chains (C1–C4): Gases. They float away.

Medium Chains (C5–C12): Thin Liquids (Gasoline). They evaporate quickly.

Long Chains (C13–C20): Oily Liquids (Diesel/Jet). The “Goldilocks” zone for heavy work.

Very Long Chains (C50+): Solids (Asphalt).

The US/Venezuela/China trade war is essentially a fight over C20+ chains…

…To run a modern economy, you need a specific ratio of products: roughly 40% Gasoline, 30% Diesel, 10% Jet, 20% Industrial/Asphalt. This matches the general demand pattern of the economy.

But nature never gives you that exact ratio in the ground.

Scenario A: Refining Light Oil (US Shale – Mostly C5-C10)

You have too much Gasoline/Naphtha.

To make Diesel (C16), you have to mathematically glue molecules together.

Biophysics: It is energetically difficult and expensive to “Oligomerize” (fuse) small chains into big ones. You cannot efficiently run an industrial economy on shale oil alone because you can’t make enough Diesel/Jet fuel without massive waste.

Scenario B: Refining Heavy Oil (Venezuelan Orinoco – Rich in C20-C100)

You have huge long chains.

The Coker: You heat them up and “Chop” them. A C50 chain can be snapped into three C16 chains (Diesel).

Biophysics: It is thermodynamically efficient to “Crack” (break) a long chain into specific smaller pieces. This is why US Coking Refineries are the “Golden Key.” They take the cheapest feedstock (C50+ sludge) and turn it into the most valuable product (C16 Diesel)…

…The US possesses the “Holy Grail” of refining: Single-Site Deep Conversion.

US Advantage: A barrel of Orinoco sludge enters a Texas refinery and leaves as 80% High-Value Diesel/Jet and 20% solid Petcoke. It is processed in one location, efficiently.

Russian Flaw – The Mazut Glut: Russia cannot fully refine its own heavy barrels. Its refineries lack the depth of US “Coking” capacity.

Russia is forced to export massive volumes of Mazut (M-100)—a cheap, low-value heavy fuel oil—because they can’t crack it into diesel domestically. They have to ship this half-refined trash to buyers who can finish the job.

China: “Teapot” refineries in Shandong have effectively become the “Trash Cans” for the Eastern Bloc. They import Russian Mazut and Venezuelan Bitumen blend to crack it into diesel and asphalt.

The Eastern Bloc relies on shipping half-refined residue between countries to achieve what Texas does inside a single fence line. That creates a massive Thermodynamic Friction (shipping fuel oil is heavy and dirty) that the US avoids…

…Iranian Oil (Soroosh/Nowruz) and Russian Mazut: Heavy, but optimized for fuels (Energy).

Venezuelan Oil (Merey 16) and Canadian Tar Sands: The global gold standard for high-yield Bitumen (Asphalt).

China consumes massive amounts of asphalt for its ceaseless road/infrastructure construction. Losing Venezuelan supply implies a structural shortage of road-paving material.

With Venezuela (Orinoco) gone to the US, and Canada (Tar Sands) logically aligned with the US (despite mercantile friction), China has only one source left for heavy, complex oil: Iran.

The Bottleneck: This forces China into a single-point dependency. If the US/Israel acts against Iranian export terminals (Kharg Island), the Eastern Bloc has minimal access to the heavy oil required for their specific refinery configurations.

Russia can’t help: Russia produces “Urals” (Medium Sour), it’s true heavy oils are limited in production and export.

Canada via the TMX pipeline supplies 200 000 bpd. This is the bpd spoken for CHINA crude. TMX total is 800 – 900 thousand bpd. And this pipeline is MAXED out. China can’t get any more. TMX schedules are spoken for. Other consumers have contractual claim.

You can’t pave a road with Iranian Soroosh/Russian Heavy efficiently; you get less asphalt and more waste…

…By seizing Venezuelan Orinoco heavy oil, the US also effectively secures the highest-value feedstock for its specialized machine, forcing China to run its “Teapot” refineries on inferior or politically volatile alternatives. This heavy oil sludge can be more easily cracked into lower forms as needed for desired usage.

Heavy oils give US optionality in refining. It is more efficient to “chop” that it is to “glue.”

The US will very likely install governance and corporate structure that is supplicating to its national needs. It can begin to squeeze the Eastern Bloc slowly by reducing exports of Merey 16. Or it can simply increase prices. China was able to buy this sanctioned oil at discount.

Now the US controls this oil supply. It’s categorization is “Clean.” So China pays fair market prices for continuing their infrastructure construction.

The same way that China uses REE controls.

We can make an estimation that China currently relies upon Venezuelan bitumen for roughly 50% of its asphalt production needs.

Depending on the mood of the US administration, this is about to get very expensive or outright disappear from China’s procurement.

Whether by design or coincidence, the US now has a very real wartime advantage against China.

It’s likely the US does not recognize this fully. They just wanted China OUT.

Disclaimer: The Good Investors is the personal investing blog of two simple guys who are passionate about educating Singaporeans about stock market investing. By using this Site, you specifically agree that none of the information provided constitutes financial, investment, or other professional advice. It is only intended to provide education. Speak with a professional before making important decisions about your money, your professional life, or even your personal life. We currently have no vested interest in any companies mentioned. Holdings are subject to change at any time.

What We’re Reading (Week Ending 04 January 2026)

The best articles we’ve read in recent times on a wide range of topics, including investing, business, and the world in general.

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!

Here are the articles for the week ending 04 January 2026:

1. Authenticity after abundance – Adam Mosseri

Everything that made creators matter—the ability to be real, to connect, to have a voice that couldn’t be faked—is now suddenly accessible to anyone with the right tools. Deepfakes are getting better and better. AI is generating photographs and videos indistinguishable from captured media. The feeds are starting to fill up with synthetic everything…

…We are now seeing an abundance of AI generated content, and there will be much more content created by AI than captured by traditional means in a few years time. We like to talk about “AI slop,” but there is a lot of amazing AI content that thankfully lacks the disturbing properties of twisted limbs and absent physics. Even the quality AI content has a look though: it tends to feel fabricated somehow. The imagery today is too slick, people’s skin is too smooth. That will change; we are going to start to see more and more realistic AI content.

Authenticity is fast becoming a scarce resource, which will in turn drive more demand for creator content, not less. The creators who succeed will be those who figure out how to maintain their authenticity whether or not they adopt new technologies. That’s harder now—not easier—because everyone can simulate authenticity. The bar is going to shift from “can you create?” to “can you make something that only you could create?” That’s the new gate…

…But flattering imagery is cheap to produce and boring to consume. People want content that feels real. We are going to see a significant acceleration of a more raw aesthetic over the next few years. Savvy creators are going to lean into explicitly unproduced and unflattering images of themselves…

…Social media platforms are going to come under increasing pressure to identify and label AI-generated content as such. All the major platforms will do good work identifying AI content, but they will get worse at it over time as AI gets better at imitating reality. There is already a growing number of people who believe, as I do, that it will be more practical to fingerprint real media than fake media. Camera manufacturers could cryptographically sign images at capture, creating a chain of custody…

…In a world of infinite abundance and infinite doubt, the creators who can maintain trust and signal authenticity—by being real, transparent, and consistent—will stand out.

As for Instagram, we’re going to have to evolve in a number of ways, and fast. We need to build the best creative tools, AI-driven and traditional, for creators so that they can compete with content fully created by AI. We need to label AI-generated content clearly, and work with manufacturers to verify authenticity at capture—fingerprinting real media, not just chasing fake. We need to surface credibility signals about who’s posting so people can decide who to trust. And we’re going to need to continue to improve ranking for originality.

2. 2025’s biggest investing lesson – slow down – Chin Hui Leong

HERE Is the uncomfortable truth about 2025: The year’s biggest wealth destroyer was not tariffs, AI disruption, or interest rate uncertainty.

It was speed…

…At the start of 2025, traders reacted swiftly to every hint about interest rate movements.

A strong jobs report? Sell immediately – fewer rate cuts ahead.

A weak inflation print? Buy before everyone else does.

This behaviour assumed that being first to interpret the data would translate into superior returns.

Let us test that theory with 2024’s track record. Goldman Sachs predicted five rate cuts. We got three.

Traders priced in a 73 per cent chance of a March 2025 cut. The first cut came in September, six months later. The market expected 1.5 percentage points of cuts. We got one.

In other words, the number of cuts was wrong, the timing was off, and the size of the cuts were lower than expected.

Yet despite these spectacular misses, the S&P 500 rose more than 23 per cent in 2024.

The lesson: You can be completely wrong about interest rates and still do well in the market – if you stay invested.

The investors who traded every data point, trying to front-run the US Federal Reserve, generated fees and anxiety.

The investors who ignored the noise generated returns…

… In his book Your Money and Your Brain, author Jason Zweig explains that our minds recognise patterns even when none exist.

But this is the kicker: We cannot switch this mechanism off at will…

…Consider this: Every major decline in 2025 was accompanied by an avalanche of negative headlines – detailed articles on what went wrong, podcasts dissecting the damage, and social media hot takes piling on.

Amid that onslaught, any good news was buried.

The investors who reacted to the noise sold at lows. The investors who waited for the noise to clear bought those shares from them.

Speed did not protect portfolios. Patience did.

3. Running Out of Runway – Poe Zhao

Last week’s dual IPO filings from Zhipu AI and MiniMax reveal a paradox at the heart of China’s AI model market. Both companies have proven they can build competitive technology. Both have validated their business models at the unit economics level. Both are running out of time…

…Zhipu grew from ¥57 million in 2022 to ¥312 million in 2024, a 130% compound annual growth rate. MiniMax achieved even more dramatic expansion, with revenue surging 782% to $30.5 million in 2024. In the first nine months of 2025, MiniMax generated $53.4 million, already exceeding its full-year 2024 results.

But losses grew faster. Zhipu’s adjusted net loss exploded from ¥97 million in 2022 to ¥2.47 billion in 2024. That’s 20x growth. MiniMax went from $7.37 million in losses in 2022 to $465 million in 2024.

The cash burn is brutal. Zhipu: ¥300 million monthly. MiniMax: ¥2 billion monthly. Zhipu’s mid-2025 reserves stood at ¥2.55 billion. Do the math. Six months later, both companies rushed to file IPOs. The December timing was necessity, not choice…

…Research and development consumed ¥2.2 billion of Zhipu’s budget in 2024. That’s a 26x increase from the ¥84 million spent in 2022. Within that R&D figure, ¥1.55 billion went directly to compute services. Computing infrastructure alone ate 70% of the entire R&D budget.

MiniMax shows better cost discipline but faces the same fundamental pressure. Training-related cloud computing costs reached $142 million in the first nine months of 2025. The company has managed to improve efficiency. The ratio of training costs to revenue dropped from 1,365% in 2023 to 266% in the first three quarters of 2025. But even at 266%, you’re spending nearly $3 on training for every $1 of revenue.

This creates the first paradox. At the transaction level, these businesses are profitable. Sell an API call or a subscription, you make money. Scale that up, you should make more money. But scaling requires maintaining competitive model quality. Competitive model quality requires constant compute investment. The compute investment grows faster than revenue. The more you sell, the more you lose…

…China’s entire large language model market totaled ¥5.3 billion in 2024, according to Zhipu’s prospectus. Enterprise customers contributed ¥4.7 billion of that. Individual consumers accounted for just ¥600 million.

Do the math. Zhipu burns ¥300 million monthly. MiniMax burns ¥2 billion monthly. Combined, that’s ¥2.3 billion per month. Annualize it and you get ¥27.6 billion. The two companies alone are burning through more than five times the entire current market size annually. And they’re not alone. Multiple other companies compete in the same space…

…Zhipu bet on scale. The company invested heavily in frontier model development. R&D spending jumped from ¥529 million in 2023 to ¥2.2 billion in 2024. Compute infrastructure dominated that budget. The strategy assumes that leading-edge capabilities justify the burn rate. Stay at the frontier, win the highest-value customers, eventually reach economies of scale.

MiniMax took the efficiency route. The company’s prospectus explicitly positions itself as capital-efficient. Cumulative spending from founding through September 2025 totaled approximately $500 million. The prospectus contrasts this with OpenAI’s estimated $40–55 billion in cumulative investment. That’s a 100x cost difference for comparable multimodal capabilities…

…This reveals what makes the situation structural rather than cyclical. Your strategy becomes irrelevant when competitive dynamics dictate behavior. Zhipu chose scale. MiniMax chose efficiency. DeepSeek’s emergence forced both to spend more regardless of their chosen path. In a true market, companies can differentiate on cost, quality, or features. In this market, everyone must match the pace of iteration or become obsolete. The pace keeps accelerating. The costs keep compounding.

4.Why We Worry – Part I – Fawkes Capital

This year alone, Google will spend roughly $60 billion more in annualised capex than it did before ChatGPT launched. Since late 2022, the company has deployed an additional $85 billion in cumulative capex on AI-related development. With similar spending levels expected next year, Google’s capex now exceeds its net profit – a sharp departure from pre-AI years, when capex represented only about 25% of profit…

…What is Google receiving in return for this extraordinary level of investment? At present, Google processes roughly 1.4 quadrillion AI tokens per month. If we make a simplifying assumption and apply Google’s API input pricing across all of those tokens, the result is an additional $21 billion of annualised revenue.

For context, this is not an especially compelling trade-off: $85 billion of incremental capex for $21 billion of low-margin revenue. In effect, Google is deploying vast sums of capital for what amounts to a modest 5% uplift in annual revenue, and materially lower returns than its core search and advertising franchise generates. A major outlay for just a 5% uplift in annual revenues doesn’t sound like a great use of capital to us.

And if this is the underlying economic reality for the industry leader, it is difficult to see how outcomes will be more favourable for its competitors. Over time, we doubt that the return on capital employed (ROCE) from datacentres will meaningfully improve from today’s levels…

…If Big Tech and data centre operators collectively spend around $400 billion on AI infrastructure in 2026, then, by our estimate, at least $80 billion in annual net income would need to be generated to justify that investment. The hurdle is high because processors, which make up the bulk of capex, have a useful life of only about five years. Back-solving this requirement implies that something like 333 million paying users of ChatGPT – roughly the entire US population – would be needed to support such economics.

Today, the numbers fall drastically short. Only around 5% of users (about 20 million people) pay for ChatGPT, and both paid and non-paid user growth has begun to stall in recent months. OpenAI’s attempt to introduce advertising as a revenue stream has met fierce consumer resistance. And unlike Google, directing users to websites does not generate economic value for OpenAI. This raises the critical question: how will OpenAI, or any non-advertising-based AI provider, monetise its service at the scale needed?…

…. A similar pattern emerged during the late-1990s dot-com bubble. Telecom operators, despite enormous capital outlays, found their services rapidly commoditised. Usage growth slowed, pricing power collapsed, and the industry could not extract the household revenues required to justify the capex binge. High returns initially attracted more competition, which eventually eroded margins for even the leading “pick-and-shovel” equipment suppliers. Investor belief in unassailable competitive advantages proved illusory. Once reality set in, the bubble burst and triggered a shallow recession…

…SemiAnalysis notes that Google’s TPU infrastructure now rivals NVIDIA’s latest commercially available GPUs – at significantly lower cost. Sensing the threat, NVIDIA has resorted to taking equity stakes in companies that depend on its support (OpenAI among them), effectively subsidising its customer base to stave off competition and preserve margins. This is not a sustainable strategy. Amazon’s upcoming Trainium 3 chip has also narrowed the performance gap and is likely to be cost-competitive upon release.

With credible alternatives emerging, NVIDIA’s 75% gross margins – the foundation of its current valuation – will not hold indefinitely. When investors fully appreciate this, and when that realisation intersects with the economic unsustainability of OpenAI’s model, the conditions for a sharp correction may be in place.

5. AI will kill all the lawyers – Sean Thomas

‘Last week we did an experiment, a kind of simulation. We took a real, recent and important case – a complex civil court appeal which I wrote, and it took me a day and a half. We redacted all identifying details, for anonymity and confidentiality, and we fed the same case to Grok Heavy AI. And then we asked it to do what I did. After some prompting, the end result was…’ He shakes his head. ‘Spectacular. Actually staggering. It did it in 30 seconds, and it was much better than mine. And remember, I am very good at this.’

He sits back, wry yet resigned. ‘It was at the level of a truly great KC. The best possible legal document. And all done in seconds for pennies. How can any of us compete? We can’t.’…

…James believes AI will work its way up the legal hierarchy. First the gruntwork, then the drafting, the citation, the argumentation. Eventually the majority of legal jobs will be replaced. ‘Process lawyers are obviously doomed. AI will handle the most complex probate and conveyancing cases in seconds. The most complicated human skill will be,’ he chuckles, sadly, ‘to scan and digitise paper documents. Barristers will make arguments in courtrooms that are drafted by AI, and then people will wonder why they are paying human barristers £200,000, and they too will disappear.’…

…I ask what he thinks this will do to his colleagues – psychologically, economically, emotionally. ‘At first, they will fight, like radicals. A losing battle. There will be attempts to outlaw the use of AI in various legal areas. But it won’t work, the economics will see to that. So lots of people who make a lot of money will, suddenly, not make that money. God knows what that might do to property prices, to politics, to all of us. Because it won’t just be the law.’

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), Mastercard, Meta Platforms (parent of Instagram), and Visa. Holdings are subject to change at any time.

What We’re Reading (Week Ending 14 December 2025)

The best articles we’ve read in recent times on a wide range of topics, including investing, business, and the world in general.

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!

Here are the articles for the week ending 14 December 2025:

1. When Mountains Become Cages: Lessons from the Sichuan Basin – Eugene Ng

The Sichuan Basin (四川盆地) is surrounded by mountains on all sides and is drained by the upper Yangtze River and its tributaries. The basin is anchored by Chengdu, the capital of Sichuan province, in the west, with the Chengdu Plain and Chongqing in the east…

…The Tibetan Plateau contains the headwaters of most of the streams and rivers in its surrounding regions. This includes the three longest rivers in Asia (the Yellow River, the Yangtze River, and the Mekong River).

The upper tributaries of the Yangtze River (长江 or 扬子江) flow through the Sichuan Basin, providing water for irrigation to grow crops, and for civilisation…

…Because of its relative flatness and fertile soils, the Sichuan Basin can support a high population density, providing staples such as rice, wheat, and barley…

…The Sichuan Basin was the strategic fortress that shaped the Three Kingdoms era (220-280 AD), following the collapse of the Han Dynasty. Wei (in the north) was led by Cao Cao, his son Cao Pi, and strategist Sima Yi. Shu Han (in the southwest) was led by Liu Bei, with strategist Zhuge Liang, and warriors Guan Yu, Zhang Fei, and Zhao Yun. Wu (in the southeast) was led by Sun Quan, strategist, Zhou Yu, and Sun Ce.

Surrounded by mountains and accessed through treacherous gorges, Sichuan was nature’s citadel. Easy to defend, nearly impossible to invade. Emperor Liu Bei built his entire kingdom in Sichuan. When he lost the battle for central China, Sichuan became his refuge and his power base.

However, the Sichuan Basin was both a blessing and a curse. It kept Shu Han alive for decades against stronger rivals, but the same isolation made it nearly impossible to project power outward after decades of failed northern campaigns.

The same mountains that kept enemies out also kept Shu Han’s armies in. Zhuge Liang launched five major northern expeditions against Wei, and all sputtered out for the same core reasons:

Geography was brutal. To attack Wei, Shu had to march through mountain passes and supply armies across hostile terrain. Wei just had to defend chokepoints. Offense is always harder; offense uphill through mountains is nearly impossible.
Economics didn’t add up. Shu was the smallest, poorest kingdom—one province against Wei’s nine. Every campaign drained resources Shu couldn’t replenish. Wei could lose battles and recover; Shu couldn’t afford to lose anything.
Talent ran thin. Zhuge Liang was brilliant, but he couldn’t be everywhere. When he died in 234 AD, Shu’s brain died with him. Wei had depth; Shu had dependence.
Strategic logic was flawed. The campaigns weren’t really about conquering Wei—they were about survival through offense, keeping Wei preoccupied so they wouldn’t invade Shu. Defense disguised as attack. It bought time but burned treasure…

…That is why Shu Han, despite having brilliant strategists like Zhuge Liang, could never quite break through to challenge Wei’s dominance in the heartland of the North China plains (华北平原). They were trying to play offense from the strongest defensive position in China…

…Shu Han’s mountains kept enemies out but armies in. Companies build defensive moats: loyal customers, proprietary technology, high switching costs, and then discover that those same moats prevent them from expanding into new markets. The thing that protects them eventually confines them. Ask BlackBerry how their keyboard moat worked out. Ask Intel if their x86 architecture saved them from irrelevance. Defense becomes offense becomes history…

…The North China Plain birthed Chinese civilization because the flat land, water, and soil aligned. In investing, today’s geography is market size, secular tailwinds, and competitive position. Invest in businesses riding massive currents, the Yangtze Rivers of commerce, not isolated mountain kingdoms. Find the disruptors and top dogs commanding vast plains of opportunity (i.e., large total addressable markets), where continued expansion is possible, and resources flow abundantly. The best investments are not defensive fortresses. They are empires with still abundant room to build and grow…

…The Yangtze River still flows through Sichuan. The mountains still stand. But Shu Han is gone. Geography endures. Dynasties do not. Companies do not last forever. Similarly, management does not, as they have to pass the torch on.

Niche businesses prosper, then calcify, then fade. Without access to vast markets, even genius becomes a footnote. The question is not whether you are smart. It is whether your terrain allows for growth or just survival.

2. Horses – Andy Jones

Engines, steam engines, were invented in 1700.

And what followed was 200 years of steady improvement, with engines getting 20% better a decade.

For the first 120 years of that steady improvement, horses didn’t notice at all.

Then, between 1930 and 1950, 90% of the horses in the US disappeared…

…I was one of the first researchers hired at Anthropic.

This pink line, back in 2024, was a large part of my job. Answer technical questions for new hires.

Back then, me and other old-timers were answering about 4,000 new-hire questions a month.

Then in December, Claude finally got good enough to answer some of those questions for us.

In December, it was some of those questions. Six months later, 80% of the questions I’d been being asked had disappeared.

Claude, meanwhile, was now answering 30,000 questions a month; eight times as many questions as me & mine ever did…

…But while it took horses decades to be overcome, and chess masters years, it took me all of six months to be surpassed.

Surpassed by a system that costs one thousand times less than I do.

A system that costs less, per word thought or written, than it’d cost to hire the cheapest human labor on the face of the planet.

And so I find myself thinking a lot about horses, nowadays.

3. Energy Predictions 2025 – Casey Handmer

In 2025, headlines scream that datacenters are pushing prices up and consuming all the power. I think datacenters are exposing the rot in a moribund power generation and delivery industry which has proven unable to meet demand in recent years. But it is a moot point.

Datacenters are already building their own captive power plants. As AI demand outstrips production of gas turbines, hyperscalers will turn to offgrid solar+battery power systems, which are already competitive with pure gas or gas+solar in the sunnier parts of Earth.

Depending on location, 10x overbuild of solar and batteries are sufficient to hit >99.5% uptime for the GPUs…

…On the flip side, these captive solar power plants will be curtailing approximately 75% of their generated power and will be able to provide net power on all but a few days per year. That is, 99% of the time, which is substantially higher utilization than any conventional thermal power plant.

Within the next five years, market power between utilities and datacenters will flip, with DCs becoming the preferred load growth power generation partner.

To spell out the implications, this means that consumers will get access to extremely competitive (cheap) power most of the time, and some combination of utility-owned and privately owned batteries will be needed to smooth out the gaps, as they would be anyway…

…If SpaceX or a competitor can ship inference compute to a 560 km unshaded sun-synchronous orbit which is 80% 1 kg/m^2 solar arrays by mass and 80% compute by cost, then it should be possible to make money. Otherwise, we can expect to see compute being developed on the ground…

…At Terraform Industries, we’re pioneering the technology to convert cheap solar power, air, and water into synthetic natural gas and other hydrocarbons. Within the next five years, solar cost reductions will drive our process to be cost-preferred in all hydrocarbon import markets, and geological sources of oil and gas will never again be able to compete. Our grandchildren will be swimming in copious cheap energy and wondering what all that drilling was for.

We believe that the path forward is lime-calcite captured CO2 + electrolyzed H2 to make CH4 and CH3OH (methanol). Methanol can be upgraded via a wide variety of existing petrochemical processes to make DME, ethylene, propane, gasoline, kerosene, and almost anything else you can imagine…

…In 2025, most gas is used for electricity generation, while most oil is used for cars, trucks, ships, and aircraft.

Solar is going to continue to displace all other primary electricity generators. And electric cars and trucks will continue to dominate growth in ground transportation.

By 2045, natural gas will be used as LNG primarily for high performance supersonic aviation, shipping, and industrial heat.

Methanol will be used as the universal industrial chemical precursor for plastics, paints, fertilizers, adhesives, as well as specialty fuels. Kerosene will service the legacy aviation fleet. Internal combustion piston engines will ultimately go the way of the piston steam engine…

…They don’t want you to know this, but rocks are made of metal oxides, and infinitely abundant commonly occurring rocks such as basalt contain basically every metal you could ever want.

With sufficiently cheap power, we no longer need to travel to the ends of the Earth to build mines. Instead, build a solar powered rock refinery at your local gravel pit…

…But much of the coast of Australia, Chile, Peru, Namibia, South Africa, Mexico, Saudi Arabia and other gulf states have essentially infinite quantities of cheap land, free solar power, and sea water. Democratized solar desalination technology can turn any and all these areas into arbitrarily lush paradises with <1% of the available land under solar arrays.

4. Why AGI Will Not Happen – Tim Dettmers

One of the most common misconceptions I see is that people assume hardware keeps improving and improving. This is an important misconception that explains a lot of the poor thinking around AI progress. The efficiency of GPUs has driven almost all innovation in AI. AlexNet was only possible by developing one of the first CUDA implementations that could compute convolutions over networked GPUs. Further innovation was mostly possible through improved GPUs and using more GPUs. Almost everybody sees this pattern — GPUs improve, AI performance improves — and it is easy to think that GPUs will improve further and will continue to improve AI outcomes. Every generation of GPUs has been better, and it would seem foolish to think that it will stop. But actually, it is foolish to think that GPUs will continue to improve. In fact, GPUs will no longer improve meaningfully. We have essentially seen the last generation of significant GPU improvements. GPUs maxed out in performance per cost around 2018 — after that, we added one-off features that exhaust quickly.

The first of these one-off features was 16-bit precision, then Tensor Cores, or the equivalent, then high-bandwidth memory (HBM),then the TMA or equivalent, then 8-bit precision, then 4-bit precision. And now we are at the end, both in the physical and the idea space. I have shown in my paper about k-bit inference scaling laws what data types with particular block sizes and computational arrangements are optimal. This has already been adopted by hardware manufacturers. Any further improvement will lead not to straightforward improvements but to trade-offs: either better memory footprint at lower computational efficiency or higher computational throughput at higher memory footprint. Even if you can innovate – linear improvements, need exponential resources – further improvements will be trivial and will not add any meaningful advancement.

While GPUs can no longer improve meaningfully, rack-level optimizations are still critically important. Efficient shuttling of key-value caches is one of the most important problems in AI infrastructure. The current solution to this problem, however, is also relatively straightforward. Companies like OpenAI boast about their AI infrastructure, but it is relatively simple to design because there is essentially only one optimal way to design it. And while it is complex to implement, it just needs clear thinking and mostly hard, time-intensive engineering. But the overall system design is not particularly novel. OpenAI – or other frontier labs – have no fundamental advantage in their inference and infrastructure stacks. The only way to gain an advantage is by having slightly better rack-level hardware optimizations or data-center-level hardware optimizations. But these will also run out quickly – maybe 2026, maybe 2027…

…I believe in scaling laws and I believe scaling will improve performance, and models like Gemini are clearly good models. The problem with scaling is this: for linear improvements, we previously had exponential growth as GPUs which canceled out the exponential resource requirements of scaling. This is no longer true. In other words, previously we invested roughly linear costs to get linear payoff, but now it has turned to exponential costs. That would not be a problem on its own, but it sets a clear physical limit on scaling that is rapidly approaching. We have maybe one, maybe two more years of scaling left because further improvements become physically infeasible. The scaling improvements in 2025 were not impressive. Scaling in 2026 and 2027 better work out better.

Despite these exponential costs, the current infrastructure build-out is reasonable, particularly with the growth of inference use, but it still creates a very precarious balance. The biggest problem is this: if scaling does not provide much larger improvements than research/software innovations, then hardware becomes a liability and not an asset…

…The key value of AI is that it is useful and increases productivity. That makes it beneficial. It is clear that, similarly to computers or the internet, AI will be used everywhere. The problem is that if AI were just used for coding and engineering, it would have a very limited impact. While a lot of economic activity is supported by digital programs, these also have diminishing returns, and producing more software will not improve outcomes significantly if existing software is already good enough (just look at the SAAS failure in China). This makes wide-spread economic integration absolutely vital for AI effectiveness.

So in order to provide real value, AI needs to be used in ways that provide new benefits, not just improvements to what already exists. This is a difficult problem, but the right answer is to integrate AI into everything to squeeze out non-linear improvements, see what works and what does not, then keep what is working. China is taking this approach by subsidizing applications that use AI to encourage adoption. The Chinese population is very receptive to innovation, which facilitates this process. It is nothing unusual in China to see an 80-year-old grandma use AI to help her with their daily life. The US, on the other hand, bets on ideas like AGI and superintelligence, which I believe are fundamentally flawed concepts that have little relevance to future AI progress. This becomes clear when you think carefully about what these terms actually mean in physical reality…

…The concept of superintelligence is built on a flawed premise. The idea is that once you have an intelligence that is as good or better than humans — in other words, AGI — then that intelligence can improve itself, leading to a runaway effect. This idea comes from Oxford-based philosophers who brought these concepts to the Bay Area. It is a deeply flawed idea that is harmful for the field. The main flaw is that this idea treats intelligence as purely abstract and not grounded in physical reality. To improve any system, you need resources. And even if a superintelligence uses these resources more effectively than humans to improve itself, it is still bound by the scaling of improvements I mentioned before — linear improvements need exponential resources. Diminishing returns can be avoided by switching to more independent problems – like adding one-off features to GPUs – but these quickly hit their own diminishing returns. So, superintelligence can be thought of as filling gaps in capability, not extending the frontier. Filling gaps can be useful, but it does not lead to runaway effects — it leads to incremental improvements.

5.The cure for FOMO is…time – Josh Brown

Strategy, formerly known as Microstrategy. This is a publicly traded company that once sold software but now serves as the largest publicly traded “digital asset trust” or DAT. It created and defines the category. For those who haven’t been paying close attention, the idea behind these stocks is that the company sets out to accumulate as much of a crypto asset as it can (in the case of Strategy they’re buying Bitcoin) and the shareholders benefit as the underlying asset (BTC) appreciates. Why not just buy the asset itself or a spot price ETF? Because the digital asset treasury is accumulating the asset at a faster pace using the money it raises via taking on debt or secondary stock sales or preferred stock sales or all three at once.

MicroStrategy currently holds roughly 649,870 bitcoin, acquired at a total purchase cost of about $48.37 billion, which works out to an average price of approximately $74,433 per BTC. Based on the fixed 21 million-coin bitcoin supply, the company controls about 3.0%–3.1% of all bitcoin that will ever exist. Saylor’s going to continue to dilute his shareholders in his quest to accumulate even more of it so, the thinking goes, if you are bullish on the Bitcoin asset itself, you buy his stock and take the ride to even faster gains than you would otherwise get with the ETFs. In this way, he has convinced the faithful that dilution is actually good, not bad. It’s helping the cause.

I never could wrap my head around it. I get the theory, I think, but it hasn’t clicked in terms of why it would work. Maybe this is because I don’t have a mental price target of $1 million per Bitcoin or something like that. I don’t know. I sold all my Bitcoin and bought the BlackRock ETF IBIT a while back to replace it and that’s pretty much the extent of my involvement in the asset class. The appeal of Microstrategy as an investment is mystifying to me still.

But, I must confess, for a long while I was wondering what was wrong with me. Was I missing something? Was there some aspect to this I wasn’t getting? My uncertainty stemmed from the performance of the stock, which was stratospheric…

…Between August 10th, 2020 and last Thanksgiving, MSTR returned 3,050%. An investment of $10,000 would have become worth over $300,000. No other publicly traded company I can find did anything even close to that in the same timeframe. Nvidia, for example, merely 10x’d in the period.

On Wall Street, price is validation, even if price is only temporary. Saylor was validated for the time being. He knew what he was talking about. After all, millions of investors had agreed with him and those who did not had been rendered wrong by what Jeffrey Gundlach often refers to as “the bloodless verdict of the market.” I was dumbfounded…

…And then a funny thing happened. Time went by. Things changed. We got a dozen ETFs listed that could serve the same purpose MSTR had served for the stock market investor – a way to own Bitcoin exposure in a traditional brokerage account. Additionally, Fidelity and Schwab, Robinhood and Public, all became legitimate venues in which to buy, sell and hold the underlying asset. This was a tremendous unlock. Where once MSTR was the only game in town, now there were many options, none of which required people to pay a premium or remember a seed phrase or transact with Coinbase or get involved with cold storage wallets and the like. Bitcoin became as accessible as running water, everywhere and to everyone. Even in an IRA. That was the beginning of the reckoning for investors in MSTR. One year later and we see the result…

…Warren Buffett once famously said the stock market is not a game where the guy with the 160 IQ beats the guy with the 130 IQ every time. He says temperament is much more important than intelligence. Temperament keeps you from acting on impulse. It’s an innate sense that things might look different in the future than they do today. The cure for FOMO doesn’t come in a can or a bottle or a box. Sometimes it pays to just stick around awhile and watch.

The cure is time.

What We’re Reading (Week Ending 07 December 2025)

The best articles we’ve read in recent times on a wide range of topics, including investing, business, and the world in general.

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!

Here are the articles for the week ending 07 December 2025:

1. Understanding ROIC on Low Growth Businesses – John Huber

A 20% FCF yield that is durable is just as good as a reinvestment moat that grows at 20% (in fact, I’d take the former over the latter in many cases because growth rates of 20% tend not to last past a few years). Of course, many 20% FCF yields are also fleeting, but there are enough examples of durable companies (some examples below)…

…People are placing too much emphasis on the stated ROIC of low growth mature companies that earn high FCF and don’t need to retain much of their earnings. It’s important to remember that the capital on a business’s balance sheet is the money that someone else invested (i.e. shareholders in past years).

If there is no place to reinvest capital going forward, then what matters going forward isn’t the ROIC (which is based on a historical balance sheet figure that is no longer relevant). What matters in this case is the FCF that we can collect going forward and the price we have to pay to acquire that FCF (i.e. the FCF yield)…

…Imagine a real estate developer invests $5 million to build a new apartment building that produces $200,000 of annual cash flow. This is a 4% FCF yield, or in the parlance of real estate, a 4% cap rate (technically the cap rate uses a pretax number based on what RE investors call net operating income, but we’ll ignore taxes for simplicity)…

…Viewing this building as a “business” suggests this is a mediocre one at best: a 4% return on capital is not creating value because the investor could have likely earned better returns investing in some other real estate investment, other stocks, or some other asset class altogether…

…So we have a 4% ROIC business that isn’t creating value. Let’s assume the market goes south, the developer’s business is overleveraged and on the rocks, and he decides to bring on a partner to help inject much needed cash. He offers you a 50% share of this building at a valuation of just $1.5 million…

…Let’s look at your result: you invest $750k and now have a $100k of cash flow (50% share of the building’s overall annual cash flow).

This means that your return on the capital you invest is not 4% but rather 13.3% ($100k / $750k).

The same building had an original cost basis of $5 million. That was the initial capital that went into funding its development. This same asset that traded at a 4% yield now trades at a 13.3% yield. However, if you viewed the financials and crunched the ROIC for this building using GAAP financials, it would still show an ROIC of 4% because that is the capital that the original developer invested into the building.

Would this stop you from investing at a 13.3% yield (assuming you like the long-term prospects for the building)? Of course not. You would view this as a great deal.

2. Blue Owl’s teachable moment for investors and asset managers chasing yield and ‘hot money’ – Isla Binnie

Blue Owl’s (OBDC.N), opens new tab turnabout decisions in the last two weeks – to merge, and then not merge, and then maybe merge two of its private credit funds at a later time – offer a cautionary lesson for retail investors in search of higher yields and the asset managers chasing the billions in “hot money” wealthy individuals bring.

The New York-based asset manager withdrew a proposal last month to merge a $1.7 billion non-public fund for retail investors with a $17 billion publicly traded fund for institutional and retail clients after news of the deal helped send Blue Owl’s shares down more than 10% in less than two weeks. The retail investors, who had to vote on the plan, were spooked by two things: it could have forced them to take a 20% loss at current prices and Blue Owl paused redemptions until early next year…

…”The reason that private credit can advertise more yield is because they’re providing you more credit risk … it’s more concentrated investments in riskier companies. Now that doesn’t sound like a trade that should be in a liquid fund,” said Robert Cohen, director of global developed credit at DoubleLine, a bond-focused investment firm managing $90 billion in assets, referring to private credit in general.

Blue Owl’s proposal touched a nerve in credit markets already rattled in recent months due to a few high-profile bankruptcies that have undermined confidence in private credit. Also, some in the market fret that expectations of interest rate cuts by the Federal Reserve could reduce the appeal of private credit investments, one of whose main selling points is their juicy yields.

3.Want This Hearing Aid? Well, Who Do You Know? – Steven Levy

Fortell is a hearing aid, one that claims to use AI to provide a dramatically superior aural experience. The chosen few included in its beta test claim that it seems to top the performance of high-end devices they’d been unhappily using.

These testers have made pilgrimages to Fortell’s headquarters on the fifth floor of a WeWork facility in New York City’s trendy SoHo neighborhood, where they were fitted for the hearing aids—which from the outside look pretty much like standard, over-the-ear, teardrop-shaped devices. But the big moment comes when a Fortell staffer takes them down to street level. There, among street clatter, honking cabs, and delivery trucks backing up to luxury stores, they are asked to conduct a conversation with a Fortell worker. Two other employees stand behind them, adding their own loud discourse to the urban cacophony.

Despite the din, the testers clearly make out what the person in front of them is saying…

… “A lot of people regard AI as something you’ll use to make businesses more efficient,” he says. “But people haven’t really internalized that you could use AI to make products exponentially better.”

De Jonge and Morris eventually dubbed the new company Chromatic, a name they later ditched, settling instead on Fortell. They realized that there would be two critical components in an improved approach to a hearing aid. The first would exploit the recent advances in AI for a better algorithm to selectively augment conversation. And the second would be a custom chip to process that algorithm in real time.

The first requirement became the province of Igor Lovchinsky, who had been Butterfly’s AI wizard. He’d come to the field late in life; up until his mid-twenties he’d been a Juilliard-trained concert pianist but left the field when he became enamored with science. Lovchinsky felt that the AI claims made by some other hearing aid companies were overblown; they were simply tweaking the amplification, he says, or aiming the microphones in a different direction.

“What became clear is that what was needed is source separation,” he says. “Take an audio wave that contains both things you want to hear and things you don’t want to hear, and separate them into just speech and just noise.” Even in 2021, it wasn’t clear that this was possible. “We all have this incredible neural network in our heads honed by billions of years of evolution to recognize speech,” he says. “If you do the source separation with the slightest deviation from full naturalness, your brain will immediately hear it.”…

…Having the right algorithms wouldn’t be worth much if you didn’t have a properly engineered chip to run them. To lead its silicon team, Fortell tapped as CTO Andrew Casper, another Butterfly alum who was a lead engineer on a Google team making AI chips. Casper also wasn’t sure that his task could be accomplished. “Your ear is very sensitive to latency,” he says, noting that if the altered sounds weren’t processed in 10 milliseconds—a hundredth of a second—it would throw users into a hellish uncanny valley. “We didn’t know if it could be done in that amount of time with a high enough fidelity so you aren’t going to notice distortions.” Only then, he says, could the company move to the final challenge: “Can we even put this thing into your ear?”

It was going to take years before the startup got those things right and could even begin to test on humans. Fortunately, the $9 million initial stake, the majority of which came from Kushner, provided a long runway. “For the first few years of the company there was no hearing aid in sight,” says de Jonge. “We needed to build for ourselves to see if the science problems could be solved.”

By 2023, Lovchinsky and Casper had made significant progress on their respective missions. Lovchinsky’s team realized that separating out the voices required creating a proprietary version of what is known in the industry as Spatial AI, involving a 3D understanding of the real world. (Confusingly, they also use the nonproprietary technology, spatial AI, in their product.) “It gleans perspectives from multiple microphones and can infer the same way that healthy people can, from both ears,” he says. His team also found a way to train their AI models with huge amounts of synthetic data that emulated all sorts of conditions. “It’s specifically useful in the most challenging environments,” he says…

…Now that the product is launched, Fortell will sell hearing aids in a single clinic on Manhattan’s Park Avenue. It’s decked out like a posh lounge, with the devices on display in a tasteful presentation that’s straight out of the Apple retail playbook. Hanging on the wall is a silicon wafer with the circuitry of the custom chips. In the early stages, his staff of four audiologists will serve only a couple of dozen customers a week, to make sure everything goes smoothly. In any case, while ramping up production, the supply will be limited.

This is great for Fortell, but it seems de Jonge’s initial impulse to usher everyone’s grandparents into the land of the hearing is in danger of being limited to the one percent, which doesn’t exactly qualify him for a Salk medal. When I ask de Jonge how his invention can scale to change life for the masses, his replies, whether due to secrecy on future plans or just not having a good answer, seem hand-wavy. In his defense, Fortell has resisted the temptation to jack up the traditional price of premium hearing aids—the $6,800 is actually a bit less than some other medically prescribed hearing aids. (As with other high-end hearing aids, the price is part of a package that includes fitting and support from professional audiologists.)…

…It’s hard to measure hearing quality, but Fortell has set out to prove scientifically that it has a better solution to hearing loss. It contracted researchers in NYU Langone’s audiology and neuroscience departments to consult on a blind experiment comparing Fortell with the leading AI-powered hearing aid competitor, a Swiss company called Phonak, whose devices retail for $4,000 and is considered the gold standard in AI hearing products. (In the study, Phonak isn’t mentioned by name and is identified only as the control hearing aid group.)

The test matched performance in environments where noise was coming at random intervals from three directions—kind of an emulation of the Cocktail Party Problem. “This is a configuration that’s particularly good to show the advantages of this aid, because what it does is actually extracting the various signals and getting rid of some of them,” says Mario Svirsky, the Noel L. Cohen Professor of Hearing Science at NYU School of Medicine, who consulted in the study (and was paid for his time).

Svirsky says the test and its goals were set out in advance. If it showed that Fortell notched a 4-decibel increase over its rival in boosting the desired signal, it would be a home run. But when they ran the study, the difference reported between the two devices was 9.2 dB in Fortell’s favor. “The results were overwhelming,” he says. “I’ve never seen such a categorical result in my career.” In one chart, the line representing the hearing improvement from Fortell virtually towered over the Phonak line. The study concluded, “In the most challenging multi-talker environment participants had 18.9X higher odds of understanding speech versus the top AI hearing aids on the market today.”

Naturally, I sought comment from Phonak about those results. Michael Preuss, the lead audiologist for Phonak’s AI platform, has been wearing hearing aids since he was 3 years old. Phonak, he says, has been in the business for 75 years and has been working with AI in its products for the last quarter century, and for the last seven years has pursued the idea of producing an AI chip—just like Fortell. Phonak, too, has spent years developing and testing its AI system, which rolled out last year to what the company describes as acclaim and adoption. When I tell Preuss about how some startup he never heard of trounced his product in a head-to-head test, he seems unruffled. “We have seen in the past that there is no industry standard in how you set up these studies and how you do these kinds of measurements,” he says. “You can design studies to enhance your own performance.” To be sure, Fortell did set up conditions that played to its strengths. But Svirsky says that those conditions were the ones that matter to hearing aid wearers. Also, unlike almost all studies performed by hearing aid companies, Fortell has submitted its work for publication in a peer-reviewed journal.

4. “Suspicion of Gross Fraud”: some notes from passing on Intellego Technologies – Andrew Walker

The company at the center of this story is a tiny little Swedish company named Intellego Technologies; when I was researching them over the summer, they had a ~$200m market cap (note: I used USD there, but Intellego reports in SEK; for ease going forward, I will use SEK through the rest of this article. 10 SEK roughly equals $1, so just divide by ten to get to a rough USD number)…

…Bears claimed the company was…. let’s say incredibly sketchy. The financials didn’t really make sense. Despite seemingly massive profits, operating cash flow was basically non-existent. Bulls said the bears were missing the forest for the trees and misconstruing normal small company growth pains with something more nefarious…

…Obviously, that bull / bear debate seems to have been settled now; the stock getting halted because the company’s cash was frozen / the CEO getting arrested for “suspicion of gross fraud” has a way of settling debates…

…As I’ll detail, to say Intellego had a ton of red flags around it is an understatement.

But, even if you put those red flags to the side, there was a pretty easy reason not to invest: it was literally too good to be true…

…Here’s where the too-good-to-be-true part comes in: UVC dosimeters aren’t exactly an unknown technology; a quick amazon search reveals a heck of a lot of options for dosimeters. Sure, maybe a hospital grade disinfecting system needs something better than a color changing chameleon sticker, but this technology isn’t some wild revolutionary breakthrough. Intellego was guiding to more than 700m SEK in revenue and 400m SEK in EBIT for 2025. In USD, that’s ~$70m in revenue and $40m in profits, making Intellego a very large and profitable business…. and an extraordinarily fast growing one; revenue was ~260m SEK in 2024, and the company was suggesting >10B SEK (~$1B in USD) in sales in five years.

I could never find a single person who could explain to me why Intellego had a right to make such enormous margins and insane growth on a technology that seemed so simple / commoditized. I’d hear bulls wave their hands and say “probably some type of patent?”, but I’d never really hear a good answer why this was a defensible market that should yield such high profits / growth…

…As mentioned, in August Intellego guided to over 700m in revenue and 400m in EBIT for all of 2025. Intellego’s initial full year guidance came in February 2025 had been for over 500m in revenue and 160m in EBIT for all of 2025 (which in itself represented insane growth from 2024’s ~260m in revenue). If you believed those numbers, the business was going parabolic. But look at those numbers: from February to August Intellego increased their sales guidance by ~200m and their EBIT guidance by over 240m, which implies that the business was experiencing negative incremental costs. How?…

… I did want to share one last tidbit from my Intellego research: my call with their (again, I assume soon to be former) CEO. I had a call with him in early August to talk about the company. It was a really weird call (my first notes from the call were “weird call”) for a bunch of reasons, including that he showed up ~ten minutes late. I won’t get into all of the details of the call, but there is one specific thing that I’ve been thinking about a lot with the benefit of hindsight that might be interesting.

I spent most of the call pressing on my key question: how could a product that seemed so simple / commoditized generate such high margins / insane growth? The CEO was pretty dismissive of those concerns (at least in my opinion), and on the heels of the call I would have a lot of mental debate with myself: was he dismissive because he was crazy, or was he dismissive because there was something so good about the product that he knew he had the right to be dismissive (was Steve Jobs crazy to be dismissive of the Zune?). The interesting thing is that he was quite cavalier on all of my questions about competition…. but he was completely honed in when I asked him questions about the company’s accounts receivable. Multiple times he told me “our one weakness is accounts receivable” or “we know that the receivables are our big weakness.”

I’ve heard CEOs mention receivable as an opportunity to improve (i.e. bring receivables from 60 days to 50 days and ROIC improves markedly!), but I’ve never heard a CEO say they were a weakness, let alone the company’s sole weakness! It just seemed like a really weird focus / Achilles heel for a company whose products were so in demand that revenue was set to ~triple, and it seemed like a strange thing for a CEO to be so singularly focused on.

5. The Untold Story of Charlie Munger’s Final Years – By Gregory Zuckerman

In the year before his death, Munger made over $50 million from a bet on an out-of-favor industry he had shunned for 60 years. He revved up his real-estate activities, working with a young neighbor to place big, long-term wagers, unusual for a nonagenarian. He faced down health challenges and wrestled with the future.

“Even a week or two before passing away, he was asking questions such as, ‘Does Moore’s Law apply in the age of AI?’” recalls his friend Jamie Montgomery, referring to whether artificial intelligence would see exponential gains like those experienced in computational power…

…Munger made his own investments, too. Sitting in a recliner in his library, he’d grab green Value Line binders from a nearby desk and pore through data on publicly traded companies.

For decades, he barely looked at coal stocks, friends say, but in 2023, these companies grabbed his attention. Coal usage was in a long-term decline, and investors saw a bleak future for the industry. Yet many producers remained profitable, trading at inexpensive levels. Coal will remain necessary as global energy demand grows, Munger argued to friends and others.

“He read an article that said coal was down the chute,” Borthwick recalls. “He said, ‘Horse feathers.’ ”

In May 2023, Munger purchased shares of coal miner Consol Energy. Later in the year, he bought shares of Alpha Metallurgical Resources, which produces coal for steel production. By the time of Munger’s death, Consol had doubled in value. Alpha had also surged. Together he scored paper gains of more than $50 million, friends say…

…Back in 1978, a surgeon had bungled cataract surgery, leaving him blind in his left eye. He learned to compensate, installing bright lights around the house. Around 2014, though, Munger experienced a problem in the optic nerve of his right eye. He faced the possibility of going blind—yet he took the setback in stride, says Li Lu, a regular visitor. Munger decided to adjust his life, asking others to read to him and contemplating other steps.

“I’ll have to learn Braille,” he told one friend. He had studied it after his botched cataract surgery but never mastered it. He was ready to try again.

That turned out not to be necessary. His right eye slowly improved, but Munger’s movement became constricted…

…Munger was counting down to a 100th birthday party on Jan. 1, 2024. Friends and longtime business associates including Jim Sinegal, Costco’s co-founder, planned to fly to Los Angeles for the festivities.

Munger’s health was faltering, though. He sensed the end was near. When a friend asked how he was feeling, he replied: “There’s a lot wrong with me.”

When he discussed his legacy, he said he was comfortable with his accomplishments and optimistic about Berkshire’s future.

“Once it’s built, you don’t need to be Warren and Charlie,” he told a friend. “What we have is a framework for looking at investments.”

Near the end of life, Munger leaned on humor for strength. He told family members that Diet Coke was responsible for his longevity, lightening the mood.

And he shared a wish with a visitor.

“Oh, to be 86 again,” he said.

Late on Thanksgiving evening two years ago, days before his death, Munger was admitted to a hospital near Montecito. He asked family members to leave the room so he could call Buffett one last time.

They shared a last farewell.

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

What We’re Reading (Week Ending 23 November 2025)

The best articles we’ve read in recent times on a wide range of topics, including investing, business, and the world in general.

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!

Here are the articles for the week ending 23 November 2025:

1. Blue Owl private credit fund merger leaves some investors facing 20% hit – Antoine Gara

Earlier this month, Blue Owl told its shareholders that it planned to merge its Blue Owl Capital Corporation II fund, which has $1bn in assets and was one of the first private debt funds targeting wealthy individual investors, with its OBDC fund, which has $17bn in assets.

Blue Owl Capital Corporation II investors are being asked to exchange their shares in the private fund for shares in OBDC at the stated net asset value of both funds. However, OBDC trades on public markets at a discount of about 20 per cent to the stated value of its assets. Blue Owl Capital Corporation II, meanwhile, is not publicly traded and instead offers investors the ability to redeem cash every quarter at the fund’s stated value.

If the mooted deal were to be approved by shareholders and completed at current prices, Blue Owl Capital Corporation II shareholders would see the value of their investments fall by about 20 per cent.

Blue Owl Capital Corporation II investors will be restricted from pulling money from the fund until the merger with OBDC closes in early 2026, at which time they will permanently lose the ability to redeem cash at the fund’s NAV…

…Jonathan Lamm, chief financial officer of OBDC, conceded in an interview with the Financial Times that at current prices, the investors in Blue Owl Capital Corporation II could take a potential haircut on their investments. But he said the merger came with significant benefits, such as the ability to own more liquid shares in OBDC, which trade on the New York Stock Exchange.

2. Blue Owl’s clever private-to-public deal makes investors see red – Sujeet Indap

Blue Owl, a US-based private capital firm, just took a bruising in such a skirmish. On Wednesday it cancelled a planned merger between two affiliates that lend to middle-market companies. One of these “business development companies” is publicly traded; the other is private, so its investors have more limited opportunities to sell their holdings.

While now dead, the merger deserves study. Here is how it worked: investors in the unlisted company would have received shares in the listed one. Measured in terms of fund assets, the swap was a wash: an owner of $1 of what sits in the unlisted bucket would still hold a claim on $1 of stuff in the enlarged, listed counterpart.

The catch was that the listed company’s shares were trading in the market at a 20 per cent discount to their net asset value. So in return for getting access to an investment they could sell whenever they liked, Blue Owl’s clients were taking a pretty sharp haircut if they wanted to sell immediately. Predictably, they cried foul.

While that’s the simplified version of events, the deal actually came with some pretty complex engineering. Had the acquiring publicly traded BDC been trading at a premium to its net assets, the exchange would be calibrated based on its share price, not the — lower — net asset value. In return for their $1 of assets they would get paper they could sell into the market also for $1, but representing a claim on stuff worth less than that.

Confusing? Welcome to private markets.

3. Going All-In on MSTR – Ben Carlson

A reader asks:

Let’s say I have a brother. Let’s say he was on a lucky hot streak this year YOLO’ing into the most speculative plays in the market (quantum, crypto, meme stocks, etc) and was up 100% YTD. Pressing his luck, he thought it was a good idea to put nearly all of his portfolio into MSTR (using margin for more leverage) when it was trading in the 300’s and he is now down 50%. I told him to never touch MSTR with a 10-foot pole and if he was bullish Bitcoin, just buy Bitcoin. I also told him many times to never use margin, especially on high risk stocks. He is at risk of a significant % of his net worth (>50%) going away forever with a home purchase on the horizon as well that’s in jeopardy. Now he suddenly wants my advice on how to get out of this mess. I told him I don’t know and I honestly don’t. It’s a darned if you do, darned if you don’t lesser of two evils situation. How do you deal with clients that consistently ignore your advice and now want your help getting out of a mess?…

..This is the problem with the bull market brain you get from making big gains in the markets. It’s difficult to know if you’ve morphed into a degenerate gambler when you’re making money. Investors who have taken on excessive levels of risk the past few years have been compensated for it.

Once you get a couple of big wins under your belt it’s easy to let things get out of control.

Strategy (formerly Microstrategy) was in the $300s when the brother got into the stock. Now it’s well below $200 and falling fast…

…Here’s the thing — you could try to offer sensible advice. Sell now before it gets worse and you get a huge margin call. Invest in something far more reasonable and diversified.

I’m not sure it will matter.

When I first started my blog I had this dream that I could somehow save people from making illogical financial decisions. After creating financial content for more than a decade now I’ve come to realize this but some people cannot be saved.

They are doomed to make money mistake after money mistake and there’s nothing you can do about it.

Then there are others who need to make a huge mistake before having an ah-ha moment of realization that they need to change their behavior. Some people do change their stripes but it’s not easy.

4. A Century-Old Classic Buffett Would Love – John Garrett

Every so often you stumble across a book so old, so unassuming, that it shouldn’t have any relevance to modern investing… and yet it reads as if it were written yesterday.

That was my experience with R.W. McNeel’s 1927 gem, Beating the Market. Nearly a century old, it feels startlingly contemporary…

…Although it was published three years before Warren Buffett was born, the lessons in this little volume closely mirror his own philosophy: buy below intrinsic value, bet on America, stay unemotional, seek value, avoid new issues, ignore brokers, be patient, resist the crowd, and focus on businesses with quality management — to name just a few.

You’ll find the similarities striking…

…“Before one starts in to speculate, therefore, he should paste this old creed in his hat: ‘I believe in my country – The United States of America. I believe in the American people, their genius, their brains, and their brawn. I believe in their honesty, and their integrity and dependability. I believe that nothing can stand in the way of their commercial advancement and prosperity.’” R.W. McNeel…

…“Charlie and I have always considered a ‘bet’ on ever-rising U.S prosperity to be very close to a sure thing. Indeed, who has ever benefitted during the past 237 years by betting against America? If you compare our country’s present condition to that existing in 1776, you have to rub your eyes in wonder. And the dynamism embedded in our market economy will continue to work it’s magic. America’s best days lie ahead.” Warren Buffett…

…“Hold firm the principles underlying all successful speculation, that earning power makes values, and values make prices in the long run, and, having in mind the value based on earning power of any particular stock.“ R.W. McNeel

“Put together a portfolio of companies whose aggregate earnings march upward over the years, and so also will the portfolio’s market value.“ Warren Buffett…

…“One chief reason many fail to buy stocks when they are low is because of fear. Periodically prices of stocks representing ownership in the great productive industries of the United States and her great railroad systems fall so far that ownership in them is selling for 25 to 50 cents on the dollar of the value of the bricks and mortar and working capital which the stocks represent. But the majority of people will not buy them then because they are afraid. If they would analyze the cause of their fear they would discover it to be due to doubt as to the very stability of American institutions, for nothing less fearsome would justify certificates of ownership in the great industries of the nation selling at such ridiculous prices.” R.W. McNeel…

…While Buffett ultimately built a far broader and more sophisticated investing framework than McNeel could ever have imagined, the foundations McNeel laid in 1927 remain remarkably solid. Strip away the technology, the speed, the data, and the noise, and you find the same timeless principles: discipline, patience, rationality, independent thought, and a focus on value anchored in real businesses run by real people.

That is why this nearly century-old book still feels so alive. Markets evolve, but human nature does not. The behaviours that drove booms and busts in McNeel’s era are the same forces we wrestle with today — fear, greed, impatience, imitation, overconfidence, and the lure of the crowd.

Or, as Buffett put it most succinctly:

“Humans behave the way humans behave, and they’re going to continue to behave that way in the next 50 years.”

McNeel understood that in 1927.

5. Robotaxis and Suburbia – Ben Thompson

Another classic of the Uber bear genre was this 2014 post by NYU finance professor Aswath Damodaran attempting to determine Uber’s true value; the startup had just raised $1.2 billion at a $17 billion valuation, and according to Damodaran’s calculations, “it is difficult to justify a price greater than $10 billion” (his actual valuation was $5.9 billion). Investor Bill Gurley — before his dramatic powerplay that led to the ouster of founder Travis Kalanick — explained what Damodaran got wrong in How to Miss By a Mile: An Alternative Look at Uber’s Potential Market Size:

The funny thing about “hard numbers” is that they can give a false sense of security. Young math students are warned about the critical difference between precision and accuracy. Financial models, especially valuation models, are interesting in that they can be particularly precise. A discounted cash flow model can lead to a result with two numbers right of the decimal for price-per-share. But what is the true accuracy of most of these financial models? While it may seem like a tough question to answer, I would argue that most practitioners of valuation analysis would state “not very high.” It is simply not an accurate science (the way physics is), and seemingly innocuous assumptions can have a major impact on the output. As a result, most models are used as a rough guide to see if you are “in the ball park,” or to see if a particular stock is either wildly under-valued or over-valued…

Damodaran uses two primary assumptions that drive the core of his analysis. The first is TAM, and the second is Uber’s market share within that market. For the market size, he states, “For my base case valuation, I’m going to assume that the primary market Uber is targeting is the global taxi and car-service market.” He then goes on to calculate a global estimate for the historical taxi and limousine market. The number he uses for this TAM estimate is $100 billion. He then guesses at a market share limit for Uber – basically a maximum in terms of market share the company could potentially achieve. For this he settles on 10%. The rest of his model is rather straightforward and typical. In my view, there is a critical error in both of these two core assumptions.

Gurley argued — correctly in retrospect, given that Uber’s gross bookings over the last 12 months were $93 billion in rides and $86 billion in deliveries — that Damodaran failed to consider how a radically better experience could dramatically expand the addressable market, and completely missed the potential for network effects leading to an outsized share of that expanded market…

…That last sentence was about Uber’s diminished bargaining vis-à-vis a centralized robotaxi operator versus individual drivers, and it’s an important one in terms of Uber’s long-term valuation. However, as robotaxis continue to expand — Waymo is now in five cities (three via their own service, two via Uber), Tesla (with human supervisors in the car) in two, and Amazon’s Zoox in one — I do wonder if I am making a similar mistake to Horan and Damodaran.

First, like Horan, am I too caught up in the current economics of robotaxis? As an apostle of zero marginal costs I am intrinsically allergic to the depreciation inherent in the cars themselves, along with the significant marginal costs in terms of energy and insurance; Uber side-stepped this by offloading those costs to the drivers. Can scale solve this? At some point — Cybercab already points to this future — vehicles will be purpose-built at scale to be robotaxis, and my experience with Full Self-Driving (Supervised) has me convinced that insurance costs will be manageable, not just because of scale, but because there will be fewer accidents.

Second, like Damodaran, am I limiting my thinking by focusing on the current market — even if that market is already massively larger than the taxi & limo market ever was? The experience of a Waymo is certainly magical; it’s also peaceful, and by removing the human from the equation, provides a sense of safety and security that Uber has always struggled with. This last point could address a major suburban point point, which is kids: the lockdown in kids’ freedom corresponded with a dramatic rise in organized activities, the sheer volume of which leaves lots of parents feeling like unpaid Uber drivers themselves. Some may rely on Uber to solve this problem; it seems likely to me far more would be willing to entrust their children to a Waymo.

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 Waymo), Amazon, and Tesla. Holdings are subject to change at any time.