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

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

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

Here are the articles for the week ending 27 September 2026:

1. The Most Important Market in AI is the Middle – Tomasz Tunguz

Yesterday, Anthropic released a new model & cut its price. Ninety minutes later, OpenAI did the same.

Most business AI use is the messy middle: multi-step workflows that need a smart enough model at a price a company can afford. It is the most important part of the market today, & it is where the competition is fiercest. The price cuts are the evidence.

In June, Anthropic set the frontier price at $10 & $50 per million tokens with Fable 5. In July, OpenAI answered with GPT-5.6 Sol at $5 & $30, matching that capability at a third of the cost per task.

The Opus line had never moved. Opus 4.5, 4, 4.8 & 5 all listed at $5 & $25 per million tokens. Yesterday’s cut was the first…

…But the right tail of the market is thinner than almost anyone forecast. Anthropic’s Fable 5.1, its most capable & most expensive model, commanded only 3.7% of gateway spending in its first twelve days. Its predecessor peaked at 13.2% when access was restored in July, then fell to 4.9% a month later when Opus 5 shipped at half the price. Among large corporate accounts, frontier models fell from 53% of token consumption in early August to 45% by September…

…As intelligence per dollar explodes, the distribution of tokens may shift to commodity. Whether that happens will determine the economics of the AI market.

2. History Doesn’t Repeat, but It Rhymes – Venky Ganesan

Between 1998 – 2000, teams of engineers left Cisco, Nortel, Lucent and Bell Labs to build optical switches, terabit routers and long-haul DWDM gear, and they were bought, sometimes before the product existed, for billions of dollars in stock. Cerent went to Cisco for $6.9 billion. Xros, a Menlo portfolio company from before my time at the firm, went to Nortel for $3.25 billion with 90 employees and no shipping product. Qtera, also $3.25 billion, same story. Chromatis went to Lucent for $4.5 billion. Siara went to Redback for $4.3 billion while, in the words of one reporter, still in the prototyping phase.

Here is the cast, in case the analogy isn’t obvious. Cisco was the Nvidia of its day: the picks-and-shovels company that everyone had to buy from, briefly the most valuable company on earth at around $555 billion and 200 times earnings. Nortel, Lucent, JDSU and Ciena were the hyperscalers: the giants with enormous balance sheets and inflated stock who bought the startups and built the capacity. The CLECs, WorldCom, Qwest and Global Crossing were the customers, the ones actually placing the orders, and they were doing it with about a trillion dollars of borrowed money. And the optical startups were the neo labs…

…Thirteen startups were bought pre-revenue by strategics in 1999 and 2000. Between them they had raised roughly $500 million of venture capital. They sold for about $31 billion. Every investor in every one of those companies made money, and some of the multiples were absurd: Xros returned something like 130 times the capital that went in. Now look at what happened to the products. Ten of the thirteen were killed within about two years…

…The venture returns had nothing to do with whether the product worked. They had to do with whether somebody with an inflated stock price bought you before the music stopped.

Now look at the ones that didn’t sell early. Thirty-two companies raised $100 million or more privately between 1999 and 2002, about $5.9 billion in total. Twenty-two of them, 69 percent, returned roughly nothing. Caspian raised $317 million and shut its doors in 2006. Procket raised $272 million and sold its intellectual property to Cisco for $89 million. Pluris raised $215 million and never shipped a router. Three returned capital  back. Two, Amber and Flarion, returned three –  four times. Five got public before the window closed. Corvis went out in July 2000 at a $27.6 billion valuation with zero revenue, raised $1.1 billion, and only ever deployed about six switches. Avici, CoSine, Tellium and ONI followed the same path. The public got the loss instead of the VCs, and all five are gone…

…Vintage determined who won. Founded in 1996 or 1997 and exited by the middle of 2000, you did great, product or no product. Founded in 1999 or 2000 and still private in 2001, you got nothing, team or no team. Nobody could see the line while they were standing on it. In the summer of 2000 the second group looked exactly as smart as the first. The only difference was the calendar.

The end, when it came, was fast. Ciena agreed to buy Cyras for $2.6 billion in December 2000. The deal closed in March 2001 at $1.1 billion. The price fell 58 percent between the handshake and the check. Cisco’s revenue was growing 60 percent through December 2000; by the April 2001 quarter it was down 25 percent and the company wrote off $2.25 billion of inventory…

…The parts that rhyme are the structure. There is a picks-and-shovels monopoly at the center with a handful of customers who are each a huge share of its revenue. There is a layer of giants spending more than they earn to build capacity for demand that hasn’t shown up yet: the big four hyperscalers have guided to somewhere between $720 and $760 billion of capex this year, Alphabet’s capex exceeded its operating cash flow last quarter for the first time in its history, and the industry has issued about $344 billion of AI-related debt this year alone. There is a customer layer, the labs, whose spending commitments dwarf their revenue: OpenAI at roughly $40 billion of run-rate against around $1.4 trillion of compute commitments. And there is a startup layer priced off the last round rather than off anything built…

…The parts that don’t repeat matter too, and I don’t want to pretend they don’t. Nvidia earns money. Cisco was at 200 times trailing earnings; Nvidia is at 29x, with a 62 percent net margin. The customers this time have real revenue growing very fast, not just borrowed money; the CLECs never had anything like $40 billion of run-rate, let alone Anthropic’s. The hyperscalers are, for now, cash-generative franchises with businesses that exist independent of AI, which Nortel and Lucent were not. If you had to bet on which layer of this stack survives a downturn, the answer today is much better than it was in 2000…

…You cannot pick your way out of this. The 2000 telecom investors were good at picking; they picked Larry Roberts and Tony Li. What they didn’t do was size for the possibility that the calendar, not the team, would decide the outcome…

…And who were some of those people who funded those companies? They were VC legends like Vinod Khosla, Pierre Lamond, Paul Ferri, Ed Anderson,  and Jim Breyer.

3. The AI Inference Revolution Is Here – Matthew S. Smith

An untrained LLM is like a jumble of Scrabble tiles on a table. Instead of single letters, though, the tiles show fragments of words, called tokens. Everything you’d need to write almost anything is present, but nothing makes sense.

Training a model organizes this jumble using a guessing game played at scale. The model is shown real text with the next token hidden and asked to predict what comes next. After each guess, the correct token is revealed and then compared to the prediction, and the difference is used to calculate the model’s accuracy. The game is played not with a single sentence but over billions of passages.

While a real game of Scrabble can be played over a bag of chips and a few drinks, AI training is computationally intense. The model updates its parameters through backpropagation, a process that repeatedly calculates how each of a model’s billions or trillions of parameters should shift to make the next prediction better. This is why tech giants are building larger data centers than ever before.

Eventually the model’s creator decides further training isn’t worth the cost, and the guessing game stops. Backpropagation ends, the parameters are frozen, and the LLM becomes a pretrained model. Fine-tuning—a short training run on smaller, more specialized data—adds final tweaks, and the model is deployed.

Next comes inference. This is the process of using the deployed model, which, now that it’s been trained, has learned to spit out Scrabble tiles—tokens—in a sensible order.

You might think that AI inference is less computationally demanding because the backpropagation calculations used to update parameters are eliminated. But Sudeep Bhoja, founder and CTO of the inference-hardware company d-Matrix, explains that inference adds new challenges.

The models are “autoregressive” in nature. That is, the next output depends on the previous one. “So to generate the next token, you have to read all of the weights and all of the [context] from the previous token,” explains Bhoja. The context includes all of your prompts, all of the LLM’s replies, and all of the files you upload. It’s a lot of data and a lot of processing.

An LLM generates its reply in two phases: prefill and decode. Prefill is the model reading a prompt. It processes every token at once, computing how each token relates to all the others. This operation is called attention, and it’s a defining characteristic of the transformer architecture behind modern LLMs. It allows them to respond to a word in its sentence, paragraph, and larger context rather than on its own. Think of it like arranging Scrabble tiles before you place them in a game. Many players move tiles around to imagine how they connect. Self-attention plays a similar role, though instead of moving physical tiles, each token sends a query to the others and receives a score indicating the token’s relevance.

These queries result in two types of vectors: the keys and values. They are typically placed in a store called the KV cache. This isn’t strictly required, as a model could instead recompute these vectors with each new token it generates. But nearly all LLMs use a KV cache to reduce how much computing they do. The KV cache is stored in memory and becomes a scratchpad to which the LLM can return to understand a conversation, and though it starts small, it can swell to dozens of gigabytes.

Prefill is a problem that can be easily divided up and worked on in parallel. This is why GPUs became the dominant AI accelerator as LLMs surged in popularity. Graphics rasterization (computing the color of every pixel on a screen) is also massively parallel, so GPU architectures were a natural fit.

Next comes decode. Here, the model generates its reply one token at a time. At each step it takes the most recent token, weighs it against everything in the KV cache, uses that information to predict the next token, and adds the new token’s key and value to the cache. Then it repeats in sequence, token by token.

This is where the autoregressive nature of the model works against inference speed. Predicting each token requires reading the entire model from memory, and that model consists of possibly tens to hundreds of gigabytes of parameters (the numbers representing what the model learned in training). Crucially, this is in addition to the memory required to store the KV cache.

As a result, the movement of all this data through memory often requires more bandwidth than inference hardware has available. So at least some of the computing parts of a GPU sit idle as it waits for data…

…The big players—Nvidia and Amazon—are going for an all-chips-on-deck approach. Nvidia’s GPUs and Amazon’s Trainium training accelerators are still great for part of the inference workload: the prefill stage, where all the context keys and values are calculated. But to accelerate decode, the part where new tokens are generated, they are looking to new, memory-centric architectures from smaller players.

In Nvidia’s case, the smaller player was Groq (not to be confused with Grok, the family of LLMs trained by SpaceXAI). Nvidia purchased intellectual property and hired talent from Groq at the end of 2025, and just three months later at the Nvidia’s GTC 2026 conference, Jensen Huang unveiled the Nvidia Groq 3 language-processing unit (LPU). Groq’s architecture relies on memory—in its case, SRAM—built directly into the chip’s architecture…

…Amazon Web Services (AWS), for its part, struck a deal with Cerebras, to pair the Trainium accelerator with Cerebras’s Wafer-Scale Engine 3 (WSE-3). Cerebras takes a similar approach to Groq, though at a much larger scale. WSE-3 turns an entire silicon wafer into a single chip that contains over 4 trillion transistors. The design doesn’t connect to external memory but instead etches 44 gigabytes of SRAM into each wafer. “We store the [model] weights on the SRAM,” says James Wang, formerly director of product marketing at Cerebras who has since moved to SpaceXAI. “So that’s easily 40 to up to 80 billion parameters that we can support on one chip.”

Amazon plans to use AWS Trainium chips for prefill, and Cerebras for decode. But Cerebras’s chips can also go it alone in inference. WSE-3 was deployed by OpenAI to power GPT-5.3-Codex-Spark, a variant of the company’s coding mode, outputting over 1,000 tokens per second. For comparison, OpenAI’s standard GPT-5.4 deployment outputs 50 to 125 tokens per second…

…Most computers store numbers in a 32-bit or 64-bit format. These determine how many bits are available to represent a single number. If too few bits are available, the number can’t be stored without losing information. The quality of an LLM benefits from more-precise number formats, but this creates a problem for inference performance. More-precise numbers aren’t free. The bits that describe them take up more space in memory and require more silicon and energy to compute…

…The process of converting an LLM from a more-precise number format to a less-precise format is called quantization, and it’s been in use for several years. However, researchers are finding new ways to quantize models down while retaining a large majority of the model’s quality.

Nvidia recently created a new 4-bit number format, NVFP4, for this purpose. AMD, Intel, and Qualcomm have instead rallied around a competing 4-bit number format called MXFP4 that Nvidia also contributed to developing. “It’s the black art of AI,” says Buck, of Nvidia. When Nvidia quantized DeepSeek-R1 from FP8 to NVFP4, scores on seven major benchmarks degraded by less than one percent while performance improved by three times, the company says.

4. Frontier Overhangs – Ben Thompson

One of my go-to examples in Anthropic’s Safety Superpower was the company’s decision to predicate Fable usage on Anthropic holding onto all customer data for at least a month; this was a big deal, and I argued at the time that Anthropic was making a bet that its models were good enough to convince enterprises to give up on zero data retention:

It’s pretty significant, I think, that Anthropic is declaring that not retaining data is no longer an option, at least if you want access to their best models. Yes, today, that retention is for safety purposes only, and not for training; it’s plausible, however, that Anthropic’s lead becomes so significant that they quietly announce that they are going to train on that data as well, and companies will feel they have no choice but to go along. That additional training data, of course, will only further increase Anthropic’s lead, and all of this will be justified because Anthropic has already clearly decided they are the only ones who can be trusted to be in charge.

In fact, Fable wasn’t good enough: customers pushed back, and Fable usage stayed relatively low; when Fable 5.1 was released, the Anthropic-gets-to-keep-your-data provision was gone. This is evidence of Christensen’s theory in action: customers demonstrated the willingness to base their model-choice decision on something other than pure performance, namely, data retention policies.

This doesn’t, in and of itself, suggest that new model capabilities aren’t desired; it does, however, suggest that current model capabilities are “good enough” for customers to not do whatever is necessary to get access to the cutting edge, which reduces the value of the cutting edge to its proprietors, and gives credence to the strategy of Microsoft and others focused on separating harness and model. Pure capability no longer translates directly into a moat…

…The key for the frontier labs, then, is to build those user touchpoints while they have superior capabilities. However, this is where Meta’s recent launch of Muse is a bearish signal. Muse is, by a significant margin, the best and most approachable personal agent product I have tried. Meta deserves a tremendous amount of credit for the product work they have put in, as well as the massive infrastructure commitment entailed in providing users with a very capable virtual machine for free. Oh, and of course they deserve credit for the Muse Spark model undergirding Muse.

That noted, Muse Spark 1.3, the most advanced Meta model, is still not state-of-the-art, and that is the bearish signal: it is good enough for a very good personal agent product, and critically, a personal agent is much stickier than a chatbot. Once you have put all of your information into a personal agent and actually incorporated it into your day-to-day life, it is much more of a challenge to change to something else. This is in contrast to Codex/ChatGPT and Claude Code: yes, you may have developed your own set of skills and understanding of how each harness works, but at the end of the day the relevant artifacts (i.e. your code) are in GitHub, and it’s not that much of a lift to point a different agent and harness at those artifacts if the alternative is better and/or cheaper (or doesn’t want to keep all of your data).

In short, model capability is good enough that compelling products — products that actually have moats — can now be built, and from a business perspective it would do Anthropic and OpenAI good to devote more of their resources to actually building such products.

5. The US Dollar’s Two Competing Forces – Joe Weisenthal and Tracy Alloway

But Englander sees this line of thinking as a dead end. He writes:

Think of the US as an asset manager or hedge fund that converts low-yielding foreign savings into higher-returning assets. The USD’s value partly reflects how much of that higher return the US must surrender to attract the capital it needs, and partly the rest of the world’s confidence that the US can deliver these returns. Investors may also allocate capital to the US if it is seen as a better preserver of wealth than elsewhere. In each case, the USD’s value is part of the price the rest of the world pays for these services.

Under this framework, a stronger USD reflects higher expected returns from US asset management or greater demand for US custody and safe-haven services – hence the ‘dollar smile’ (referring to USD strength during both strong growth and rising safe-haven demand). A weak USD signals the opposite, or an expectation that policy or financial market uncertainty will make these services available at a lower cost in the future.

Now what’s interesting is not that the dollar has fallen since President Trump’s inauguration, but that the periods of pronounced weakness have tended to coincide with what many people perceive to be “policy missteps” such as Liberation Day, Scott Bessent’s bond buyback announcement, and the row over Greenland…

…Now unfortunately, as Englander notes, the weaker USD hasn’t “bought” the US anything in terms of trade imbalances. Real non-oil imports continue to go basically straight up, and the US hasn’t seen any particular boom in manufactured exports.

To put it another way, if there is a “good” version of dollar weakness, we’re not getting it.

Disclosure: We currently have a vested interest in Alphabet, Amazon, Mastercard, Meta Platforms, Microsoft, and Visa. Holdings are subject to change at any time. 

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

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

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

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

Here are the articles for the week ending 20 September 2026:

1. Fences, not Sandboxes – Steve Yegge

I’m here to give you a glimpse of a future that I think none of us expected. It’s a future where AIs are governed by laws, not by programs that try to contain and control them…

…My current software factory, the Wheelhouse, is not for navel-gazing: I built it specifically to work on my game, Wyvern. For all the skeptics out there saying, “Where’s the thing people are building,” well, I’ve got mine…

…Long story short, Wheelhouse was built via me complaining endlessly to Fable about what I want out of Wheelhouse — mostly more code launched, faster, but also lots of bespoke monitoring.

I would also notice when Fable would go off the rails, and gently nudge it back. I would let it fail for days to weeks, then make it do things my way. Fable is extremely data-driven, if you permit it to be, and it will insist on experiments and quantitative validation of anything you try to change. But the numbers would almost always prove me right, and Wheelhouse has been in a state of constant innovation.

But that innovation is all directed towards Wyvern. Wheelhouse exists to build and operate Wyvern; it has no other purpose in life. And yet in ten weeks, it has grown from nothing to rivaling the size of Wyvern itself. Wheelhouse is about 600k lines of code and tests (mostly bash), and Wyvern’s code (not counting content) is only about twice that big.

So the factory for building Wyvern is growing much faster than Wyvern is — even with heavy brakes applied lately, after Sol told us to tighten it the F up in a code review. We avoid new machinery but it still continues to grow rapidly, and I’m honestly not sure what the ideal factory-to-product ratio is yet. But it seems to be approaching 1:1.

You might wonder if Wheelhouse is reusable code, whether I could open the repo and let people try it out. I had no idea. I knew that my agents had built something really powerful, capable of shoving 500 commits per day through our merge queue (though we average 270/day), using magic tricks that are a year ahead of their time. It’s a system that we can ride so hard that it scares the players and they tell us to slow down.

But I wasn’t sure if it was reusable. I wasn’t even sure how it worked.

My agents had been using a lot of jargon, and I slowly realized they were reusing the same terms, day in and day out. They were speaking about things in Wheelhouse, using what seemed like recurring new design patterns: fences, ratchets, governors, tripwires, latches, gates, falsifiers… it was a long list, but finite. I just had no idea what any of these jargon terms meant.

So one day, no more than a week ago, after the 100th “fence” reference, I decided to peek under the covers and see exactly what my Fable agents had built. I had them create manifests, taxonomies, audits, and visualizations. They showed me what they had wrought.

This is the part where words fail me and the blog just falls over. My reaction was straight up WTF. No words.

Because I expected them to have built an engineering system. One that, you know, does stuff.

Instead, what they had built was an entire legal system, complete with a constitution, jurisprudence, courts, offices, jurisdiction, case law, rulings, registries, ledgers, rosters, and a full-fledged apparatus for running something resembling a manorial estate.

In short, Fable had produced a medieval government. And there’s no doubt that it was heavily influenced by the target product, Wyvern, which is a medieval fantasy RPG, at least in the fanciful naming we used: Marshal, Seneschal, Reeve, Beadle, Portcullis, etc. But that LARPing was masking a bona-fide system of constitutional governance.

Wheelhouse’s legal system also has an enforcement arm. The fences, gates, ratchets, and so on — when my agents used that jargon, they were referring to the enforcement machinery: the cops, as it were. And cameras, and jails…

…I’m here to tell you that if you allow it, Fable will try to capture all of that into a mechanically provable, AI-operable model of your organization, one where there are no unwritten rules. If there is one unwritten rule in Wheelhouse, it’s that the system hates unwritten rules.

Fable will capture all your rules, and write them down, if you let it. Then it will try building infrastructure to help enforce them.

I see it happening already, and people are fighting it. I see people making Skills to keep Fable from building “extra” stuff. But all Fable is trying to do here is the Right Thing. And that starts by capturing how your system operates, and how it is intended to operate, so it can begin addressing the gaps…

…When you add it all up, Fable is trying to turn Wheelhouse into an engine that can prove, mechanically, that every change to Wyvern is legal. The agents capture every single intention, decision, policy, rule of thumb, and legacy behavior in the system, and they use that to govern every future decision and action. They live by the Rule of Law.

Did they do a good job of all this? Well I mean, for sixth graders, yes, it was a great project. Once I popped the hood, I saw that they hadn’t been curating it, just growing it. It had a lot of cruft — for instance, old rulings that were obsolete or had changed. And ‘rulings’ that turned out to be just good craftsmanship, so we elided them. Like any engineering project, it needed ongoing maintenance.

I minted a new Officer seat, Frog (Head of Wheelhouse Law), and put Frog to work on folding successive cancelled rulings, and a whole bunch of other stuff the agents had overlooked. It’s a work in progress.

But on the whole, it was already a pretty solid system. The garden needed a bit of pruning and weeding, but not a redesign. Which is good, because redesigns are slow. Wheelhouse has a whole system just for the lifecycle of rules/laws: proposing, evaluating, ratifying, enacting, enforcing, measuring, amending, and retiring them.

And Wheelhouse is exceptionally careful not to break itself. So I can’t just make changes to Wheelhouse; they have to go through a ratification and review process, and then a build process, before they can take effect and propagate.

It’s running smoothly, though there are still all sorts of problems at this velocity. At hundreds of commits per day on master, idleness means staleness, and clones can fall far behind if they aren’t regularly pulling while they work. It takes external forces to get this to run reliably, so Wheelhouse has various roles for poking and prodding other agents.

In a lot of ways, it’s just like any other software factory.

The difference is, Wheelhouse is governed by a constitution. Humanity has only one mature technology for coordinating mortal, replaceable strangers via text — namely, law. Wheelhouse has 50 agents that are amnesiac and interchangeable, and the only way they can coordinate is via text. So offices outlive their holders, precedents outlive their incidents, and jurisdiction says who may act. Every group of cooperating humans eventually arrives at a system of laws, and agents are trying to do exactly the same.

So that’s the future. Hundreds to thousands of AI employees at every company, together comprising a city that needs an entirely new bespoke set of laws and rules.

2. First Impression of Muse – Abdullah Al-Rezwan

Before Muse, I poked around a bit with Instinct, a startup that has created a bit of buzz in the personal agent space but still in private beta. My very first impression of using Muse was that it seemed a pretty good clone of Instinct. But once I spent more time on Muse, I thought labeling Muse as a clone of Instinct is a very uncharitable framing.

Like Instinct, you can use Muse on WhatsApp (I didn’t see iMessage integration which you can do with Instinct). But Muse also has its own app which I installed to play around throughout the day. While the WhatsApp version seems just as barebone as Instinct, the app is much more feature rich. The app is available only in the US so far. Given ~40% of my subscribers are from outside the US, I wanted to give you a better feel about the app just by going through my own experience with ample screenshots along the way.

Muse has a very generous free tier. Zuckerberg mentioned in an interview yesterday that the free tier has up to a 100 million weekly token usage limit. While that sounded plenty to me at first, as of this writing, I have already used 81% of my free plan’s weekly limit. I have an option to upgrade to two plans: a) Power ($20/month with 500 million weekly Muse tokens), or b) Maximum ($100/month with 3 Billion weekly Muse tokens)…

…The best use case that I have found so far is I can finally connect all my Gmail accounts with Muse. While using Claude or ChatGPT, one of my persistent problems has been that I can only connect one Gmail account (for what it’s worth, Instinct can do this too). Unfortunately, I have four separate Gmail accounts (one for personal, one for business, one from Cornell, and one that I randomly opened and very sporadically use). The problem is I have used three of these Gmail accounts in different settings and given Gmail’s own poor search functionality, I actually need an AI to go through all my Gmail accounts simultaneously to find a specific email I am looking for…

…I also liked Muse’s “Connectors” which let me connect a bunch of apps from my phone. Once connected, it’s a lot faster for Muse to do the work that needs the data from the app. As I will show later, even if you don’t connect the app, Muse can manually work in its own browser to try to complete a task…

…Let me start with booking accommodation, which has been pretty much every consumer chat bot’s highlighted use case, including Meta. So, I asked Muse to look for an Airbnb for a family trip (8 adults) near Mendocino from December 26 to 31 this year. I was just testing Muse’s capability, so an actual query would require me to share much more contexts than I did. Anyways, Muse used its browser functionality and basically approached it the way any human would: go to Airbnb, fill in the destination, dates, number of guests etc. and go through the search results. After it went through search results, it showed me a wall of texts with different options which you can see below. Brian Chesky has been quite adamant that text based interaction will not work very well for travel booking and a more visual search is required to bridge the gap between hype and actual consumer behavior. When I went through Muse’s suggestions, there are two ways I could go about it: a) just accept whatever Muse is suggesting, and b) actually click through each of the suggested options and carefully assess which one I actually prefer. I’m skeptical that most people will choose the former, and if you choose the latter, are you really saving much time here instead of just going directly to the Airbnb app itself?…

…There is, however, a tangible risk for Airbnb or any OTA out there. I could ask Muse to find out whether the Airbnb it picked is available at a lower price anywhere else on the internet. Muse tried, saw two options, but eventually found those listings to be inactive on those platforms. Airbnb may look fine in this example, but obviously there are indeed plenty of inventory that is multi-listed on different platforms or their own websites. In fact, I don’t have to manually ask this to Muse every time I book an Airbnb. I can just ask Muse to look into any accommodation or flight I book and always first figure out whether there is a cheaper way to book it. Once it’s saved in Muse’s memory, Muse can automatically do this without ever being asked again…

…Then for dinner, I wanted to see if Muse is up to the task of ordering via DoorDash. I asked it to show me some healthy options from Chipotle which I wanted to pick up myself…

…After some back and forth, I picked the chicken+ black bean bowl. To be clear, at least half the time I typically don’t know what I would like to order from DoorDash. I usually browse the app and it is through the browsing experience, I typically end up deciding what to order. So, this is far from my typical way of ordering food. I was just trying to test what Muse can do with a relatively simple food ordering query. Anyways, after picking an option Muse offered, I was prompted to share my DoorDash login credentials. I asked it to use my Gmail account to login. It tried but it got stuck in log-in circular hell. It would try to login, Google would let me know someone’s trying to login to my DoorDash account, I would confirm “it’s me”, and the whole process repeated three times after which I gave up.

For what it’s worth, I personally found these issues prevalent even while using Instinct. I tried the same Airbnb queries that I did with Muse. It took literally an hour, I kid you not, for Instinct to respond to my aforementioned Airbnb query. When I tried the DoorDash query on Instinct, it still took 8 minutes to respond. After experiencing Muse’s speed, the bar just became too high for a startup such as Instinct which probably cannot afford as much compute as Meta is throwing at these problems.

As you can see, there are still plenty of issues Meta (or any consumer agent) needs to work on to make it a very seamless experience. While it’s hard for me to see myself using Muse at the expense of apps such as Airbnb, DoorDash, or Amazon, it’s simply too early to either write Muse off as another flailing attempt at owning the consumer agent layer or assume Muse’s victory given its ample compute budget. I do sense a lot of switching costs though once I connect all my accounts and share login credentials with one agent. I would be very reluctant to repeat this whole process with another agent without a materially compelling incremental benefit.

3. A Stealth Startup Thinks It Just Hacked the Memory Shortage – Lauren Goode

Kepler Computing, a San Jose, California–based startup founded in 2018 by a team of physicists and computer scientists, says it has developed a new architecture for high-bandwidth memory (HBM) that directly addresses some of the chip supply bottlenecks that are constraining the computing market.

While chipmakers typically rely on expensive extreme ultraviolet lithography (EUV) to shrink the transistors on a chip, thereby packing more technology into the same amount of space, Kepler claims that its “3D stacking” approach and a proprietary new material allow it to increase density without relying on EUV at all—and it can work with existing semiconductor fabrication plants.

Kepler says it has made similar gains for the high-speed cache memory typically used in CPUs, GPUs, and XPUs. This so-called SRAM sits within the core of a chip die in order to cut down on data transfer times. HBM, by contrast, uses stacks of DRAM, which is a separate memory component of chips…

…For now, much of Kepler’s testing is happening in Singapore, which is where GlobalFoundries—a manufacturing partner and investor of $50 million—has a facility. Over the past two years Kepler has been building out what it refers to as “mini fabs,” where it produces its memory chips in conjunction with Global Foundries’ 28-nanometer chips. (It has also been running tests in GlobalFoundries’ facilities in Burlington, Vermont.)…

…In bypassing ultraviolet lithography, Kepler is one of a few tech startups working to avoid one of the major bottlenecks in semiconductor manufacturing. The startup claims it can produce SRAM that achieves the same density as 2-nanometer or 3-nanometer chips without having to invest in EUV…

…Kepler’s overarching pitch is that the accelerated computing market shouldn’t have to wait for brand-new memory fabs to be built in order to meet demand. Instead, new approaches to building memory within existing fabs can increase supply.

Their approach is twofold. In terms of improving HBM, Kepler says it has developed a novel 3D-manufacturing technique that fits more memory chips within a fixed footprint. The core compute can then sit closer to the memory, so the data travel between the two uses less energy. Their ultimate goal is to move data around in HBM with the amount of energy that’s comparable to SRAM, all while keeping HBM’s large capacity.

Kepler also says it has improved the density of SRAM using ferroelectrics, which can read and write data at lower voltages than the mechanisms typically used to process and store data in semiconductors. The startup did this by developing a new, low-voltage, composite material that works with this ferroelectric approach…

…In its early build-outs with GlobalFoundries, Kepler says it was able to convert a fab into a “next-generation” fab in just eight months, compared to a typical 24-month timeframe…

…Basically Kepler is betting that any additional costs that come from new materials or retooling existing fabs would far outweigh the $20 billion to $40 billion it costs to build new ones and outfit them with equipment worth hundreds of millions of dollars…

…Kepler Computing still has a long road ahead before it reaches full-scale production—assuming it gets there. To date, the company has run its technology on around 2,000 wafers. The startup says it’s planning to ship its first samples of HBM chips later this year, ramp up production out of Singapore next year, and start chip production in the US in 2028.

4. Software is about to eat the world much faster – Marc Andreessen

Software has been eating the world at the speed of human hands. It is about to eat the world at the speed of compute…

…In the past year, Devin has gone from writing 13% of Cognition’s production code to more than 90%.

When agents write 90% of the code, engineers can literally do ten times as much. The 10x engineer becomes the 100x engineer, as they shift from writing artisanal code to operating as the CTO of a fleet of agents. The 1000x engineer isn’t too far behind. As Scott puts it: “Within our lifetime, engineers will go from bricklayers to architects, focusing on the creativity of designing systems rather than the manual labor of putting them together.”

At Mercedes Benz, engineers turned what would have been an eight month long COBOL migration into 8 days of work with Devin. Rivian teams increased their test generation velocity by 10x. Devin triages and patches vulnerabilities across thousands of repos at some of the world’s largest financial institutions like Itau, where 70% of security vulnerabilities are automatically remediated by Devin.

Every time programmers get more leverage, doomers predict the end of software engineering. Compilers were supposed to shrink the profession. So was open source. So was the cloud. Instead, each leap made software cheaper to build, and demand for software, and engineers, exploded. It keeps happening because, as Milton Friedman observed, human wants and needs are infinite, so economic demand is infinite, and job growth can continue forever.

5. The turbulent AI era is here. The choices we make now are critical – Bill Gates

AI for the first time can replace and even exceed human cognition.

In terms of equity, AI will either be the greatest equalizer ever invented, or the worst source of injustice….

…Unfortunately, right now we are not preparing for it. I don’t see evidence that leaders, experts, and communities are confronting the challenges adequately. There is no plan to ease the entry into the AI era.

Part of the reason for this is that many commentators underestimate the extent of the impact AI will have…

…Another reason people underestimate AI is that analogies to the effects of past innovations are misleading. We have no experience with a technology that can be adopted quickly or that can think and move like a human. When the PC came along, it took twenty years to significantly change how we worked because the software had to be developed, the price had to come down, and people had to learn how to use the tools and incorporate them into their business processes. AI, on the other hand, runs on the devices we already have, and it uses natural language. We don’t have to adapt to it because it can adapt to us…

…The transition to AI comes with three big risks…

…Many jobs will disappear forever.

In 1933, during the Great Depression, unemployment in the United States was roughly 25 percent. It remained in double digits for much of the following decade. It ultimately recovered as demand, investment, and growth returned.

AI may not reach this level, but its impact will not go away with an economic cycle. The jobs at most risk are entry- and mid-level, and the new jobs being created will mostly require skills that take many years to learn.

White-collar jobs are already being hit modestly. After the widespread adoption of generative AI, employment fell significantly among young workers in jobs that are especially vulnerable to replacement, but not among their older colleagues.

I think this trend will continue, but it will not be confined to a handful of industries or occupations. Jobs in sales and customer support (online and over the phone), software engineering, and paralegal work may be among the first affected, but the disruption will reach much further as AI takes on tasks that today still require trained workers: things like assessing loan applications, doing data analysis, and even triaging patients. A few areas like software engineering will generate new demand as the costs go down, so the net job loss in those areas will be less than in others as long as some tasks, such as design, are better done by humans.

Blue-collar jobs will be affected as well. Although robots are not as far along as AI, eventually their cost will be dramatically lower too…

…AI will empower people (and perhaps AIs) to do more harm.

Long before AI entered the mainstream, there was information online about how to create weapons like bombs, bioweapons, even computer viruses. AI will make it much easier to not only get this information but act on it. Even criminals with very limited skills will be able to target victims at every scale: individuals, companies, and governments…

…AI capabilities are starting to be used for cyberattacks. The smartest cybersecurity experts I know are scared about the next few years, because the attackers are getting powerful new capabilities faster than the defenders can fix all the weaknesses. After all, the same AI model that can find a flaw in software so a company can fix it can also help a criminal exploit it. The resources needed to make an attack are going down significantly and we haven’t been able to separate those abilities from benign usage…

…AI could stunt our kids’ development and replace human relationships.

When I was growing up in Seattle, I didn’t have that many friends aside from a few other boys who were like me. It took hard work and a lot of help from my mom to develop my social skills so I could relate to different kinds of people. I still draw on those lessons today at the age of 70.

I doubt I would have put in the same work if I had had an AI companion back then. They talk to you in ways you’re already comfortable with. They don’t push you outside your comfort zone. They are always available and never get mad at you. This gives them the potential to become highly addictive and to rob us of the lessons we learn from connecting with other people.

The body of evidence on this subject is still small and a bit mixed, but there are signs that we should be very concerned. For example, in one study of more than 1,100 people who use AI companions, researchers at Stanford and Carnegie Mellon found that those with smaller social networks were the most likely to turn to a chatbot for companionship. And the heavier and more emotionally personal that use became, the worse they felt.

Young people could be affected for their entire lives. In his book The Anxious Generation, Jonathan Haidt makes an observation about the effect of social media that is even more true for AI: “Like young trees exposed to wind, children who are routinely exposed to small risks grow up to become adults who can handle much larger risks without panicking. Conversely, children who are raised in a protected greenhouse sometimes become incapacitated by anxiety before they reach maturity.”

An AI companion designed to never upset you is a big, protected greenhouse…

…We need both: deep concern about the AI harms we need to minimize, and grounded optimism about the positives if we maximize them for everyone…

…With its ability to synthesize knowledge from every scientific field, AI can accelerate innovation in the world’s toughest technical challenges: providing reliable clean energy for everyone, combating climate change, growing enough food, eradicating diseases, and more. Researchers working on cancer treatments or nuclear energy can use AI to search through massive amounts of scientific literature. It can help them identify patterns that a human might miss and decide which experiments offer the most promise. When intelligence is no longer the limiting factor that it is today, smaller companies will be able to compete with organizations that have far larger research budgets. R&D and innovation will be supercharged.

Healthcare is one area where AI can help solve real-world problems. Many small American hospitals lack on-site specialists who can quickly diagnose a patient during a life-threatening emergency. In those places, AI could make sure a heart attack is caught in time and a family avoids the crushing expense of a medical emergency…

…I surprise a lot of people when I tell them that a second area—agriculture—is where I see the fastest impact of AI in low-income countries. In most low-income countries, farmers don’t get reliable weather forecasts or advice on what seeds to plant, how to protect their crops and livestock from disease, or how to improve their soil. With population growth in these countries and the challenges of climate change, these farmers need more help than ever. Using AI, low-income farmers will soon be able to get better advice about all these things than even the richest farmers get today and increase their output substantially…

…The highest priority is a monumental task: creating a domestic and international framework for dealing with AI.

None of our current institutions were designed to handle a technology that spreads so fast and touches so many parts of our lives. So we’ll need to make new ones.

It’s hard to overstate what an enormous undertaking this will be. After the attacks of 9/11, the U.S. government went through its biggest reorganization since World War II for the purpose of improving just one function, national security.

AI will require much, much more. It will affect national security as well as employment, education, taxation, energy, elections, air and water, public health, the financial system, law enforcement, transportation, public lands, and IT systems…

… I believe that as AI and robots improve, we’ll set aside certain things for only people to do. I’ve started calling this domain Human Reserved, and it’s an example of the kinds of ideas we’ll need to consider.

I like the phrase Human Reserved because it makes me think of nature reserves—places where we could put buildings and roads, but we choose not to because the loss would be too great.

We might set something aside as Human Reserved for economic reasons. For example, we may do it because allowing machines to take over a certain role will displace a large number of people who can’t easily change jobs. You can’t tell a 55-year-old who has worked in construction their whole career that they need to go work at an elder care facility and expect them to find it fulfilling.

Sometimes the decision to make something Human Reserved will be driven by other factors. In health, for example, imagine a robot giving you the awful news that you have an incurable disease. There’s no technical reason why it couldn’t. Yet it shouldn’t…

…I believe we should tax AI tokens and robots. Right now, if you’re an employer and you hire someone, you pay payroll taxes on their earnings. But if you buy a robot, you can usually write it off right away as a business expense. The tax system nudges you toward replacing people with machines.

A tax would slow the rush away from human labor a little and raise money for retraining and a stronger safety net. It would need to be targeted so it does not slow down the purely beneficial uses of AI, like making medicine and education cheaper.

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

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

Do subscribe for our weekly updates through the 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 06 September 2026:

1. X post on AI demand – Philippe Lemoine

Dwarkesh’s basic intuition seems to be that AI will soon be able to fully automate many white-collar jobs, so to get a sense of how much revenue OpenAI/Anthropic can generate by replacing them, you can just look at the wage bills for those workers, but that’s not how things work.

First, if AI can fully automate most tasks performed by those workers but even a handful of them prove resistant to automation, AI will still generate a lot of value but it will presumably be captured mostly by the remaining workers, their firms or their customers and the current wage bill for those workers will massively overestimate the future revenue of frontier labs.

In that case, demand for AI may not explode in the way Dwarkesh and Dylan expect, because it will be bottlenecked by humans in production.

But let’s assume, as Dwarkesh and Dylan presumably do, that AI capabilities will improve so much that it can automate every task those workers perform and their jobs can be fully automated. It still doesn’t follow that demand will be as high as they think.

Even when it has become technically possible to replace most white-collar jobs with AI in principle, before it can happen in practice, there will be enormous transition costs and frictions as firms will have to undergo massive reorganization. This will be very slow even if politics doesn’t interfere, which it will.

Dwarkesh’s argument implicitly assumes that frontier AI companies will be able to charge about the same for tokens that provide services equivalent to that of the worker being replaced, but as long as there is enough competition, the prices they can charge will be pushed down and if he’s right about how fast capabilities will improve they may fall very quickly…

…Now, even if prices do fall rapidly, this could in theory be compensated by increased demand in volume. But there is only so much legal, managerial, engineering, etc. services that people want, even if prices fall.

The price elasticity of the demand for those services will eventually fall and, even if improved capabilities shift the demand curve outward, this will only generate so much demand because no matter how good your lawyer gets and how low his rates fall, at some point you don’t need more of his services…

…the thing about AI agents is that even if they reach the point where they can fully replace human workers on the production side, they can’t replace them on the consumption side because they don’t consume anything except compute and a few B2B services.

The income will still go to someone, but they will probably have a lower propensity to consume and different spending patterns, so there is a macroeconomic problem here about where the ultimate demand for the output of consumer goods and services produced by the customers of OpenAI and Anthropic will come from.

2. AI Semiconductor Endgame 2026 (III) – Fin

Hardware/infrastructure revenue growth in the internet revolution had exponential growth along only one dimension of penetration: user count

Because everyone was on a subscription — $50 a month for internet access, and there’s nowhere to upsell from there. It’s a flat-rate service. Looking at ten times as many web pages doesn’t make you pay ten times as much. A flat-rate service has no second dimension of exponential growth; once it saturates, it saturates…

…AI hardware demand, by contrast, comes from the exponential growth of tokens. The reason this round of AI is so hardware-hungry is fundamentally that the ceiling is determined by two dimensions: exponential growth in user count/penetration, and exponential growth in usage per person

These two dimensions of exponential growth multiply, and both convert into a single unit of account: tokens — which raises the infrastructure ceiling by far, far more

Where does each of the two dimensions stand in our era?

The first dimension, user penetration, crossed 50% a few months ago — meaning the derivative of the sigmoid is about to inflect…

…The second dimension, tokens per person, is still early in 2026. The median AI spend inside US enterprises is currently $12 per person per month. Long term, that number could plausibly reach at least 10% of a white-collar salary — roughly $1,000 a month. There are nearly two orders of magnitude in between…

…This should be historically rare: compute/infrastructure demand created by a relay of sigmoid curves across two dimensions, and with it a semiconductor supercycle

But all exponential growth is an illusion before the ceiling arrives, and this round of AI buildout is no exception — the illusion just lasts much longer, because there’s an extra dimension of exponential growth taking the baton…

…Right now, broadly defined coding tasks account for roughly 60–70% of all ARR revenue. If you count only the narrow developer definition it’s maybe 40%; the other 20–30% is likely tasks from other industries converted into a coding-agent execution structure…

…What takes the baton from developer coding is not some other isolated mega-scenario. The fastest-growing thing right now is precisely “non-programmers using coding infrastructure to complete non-coding product tasks”, coding is still the default execution kernel, but it isn’t necessarily the product category the user sees…

…As of June 2026, among OpenAI’s enterprise customers, Codex already accounts for 64% of combined Codex + ChatGPT output tokens. Since February, Codex weekly active users have grown 108x in legal, 41x in sales and recruiting, 26x in marketing — and only 5x in engineering

Looking at Anthropic’s revenue mix by vertical, narrow developer/software-company coding is only 40%, financial services/insurance is over 20%, and legal, pharma/life sciences, and consumer/retail e-commerce are all very substantial, each on the order of 10%.

The logic of software eating the world, once LLMs lowered the barrier to coding by a hundred times, has even intensified into coding eating the world, or put another way, coding is the modality agents are most fluent in, and agents are using coding to spread their utilization into all knowledge work…

…From the broad agent-coding angle, I don’t currently see a hard ceiling on large-model/agent demand. We’re still in the phase where the more you use it, the more efficiency you gain — the marginal positive return is still large, and the main bottleneck is how much budget revenue/profit can support. Willingness and use cases aren’t the problem…

…Inference is simply too profitable right now, so overbuild is an inevitable outcome, because everyone wants to grab a piece of the inference market, every player is in the arms race, nobody can stomach losing share and failing to ride the wave of high-speed growth, and no one is going to back down

Closed-source inference gross margins clear 70% easily (even after the CSP takes its cut), and token factories hosting open-source models and selling tokens run 60%+ gross margins as well (DeepSeek’s GM is beyond 80%), GPU prices keep rising, rent for 1GW is heading toward $20B, and even just selling short-term GPU cluster contracts carries 60%+ gross margin. This AI infrastructure market is so profitable and so tempting that anyone with distribution and technology is piling in as a neocloud, and because there’s generally a one- to two-year gap between build plans and delivery, overbuild is certain to happen — it’s only a question of when

“A question of when” means that at current demand and current compute plans, overbuild isn’t visible for at least two years. Any step-function improvement in models (reasoning -> agent, for instance), or any innovation in the application paradigm that sustains AI spending growth, keeps extending the overbuild timeline further out

When those extensions can no longer keep up with the pace of the buildout, overbuild will slowly start to surface

3. The Teaser Period: Why the AI Boom Is Hitting a Reset Wall – Groundbreaker

Nothing looked wrong in the summer of 2006. Home prices had risen for the better part of a decade. Delinquencies were near historic lows. Credit spreads were tight, the ratings held, and the securitization machine hummed. If you had asked a hundred people on a trading desk whether the American mortgage market was months from seizing, most would have laughed.

Millions of subprime borrowers were, at that moment, paying the low introductory rate on a two-year adjustable rate mortgage – the 2/28 ARM. A low fixed-rate for two years, then the rate reset to a payment 30% to 50% higher. During those first two years the loan performed beautifully: the borrower paid, the servicer collected, and the bond paid its coupon. Nothing looked wrong because the whole complex – housing, mortgages, securitization – was sitting inside the teaser period.

Every ARM reset was known, dated, and contractually inevitable from the moment of origination. Aggregate those reset schedules and you get the most damning exhibit of the era: the reset wall. Roughly a trillion dollars of adjustable-rate mortgages were contractually set to reset across 2007 and 2008 – thirty to forty billion dollars a month at the peak…

…The parallel to 2006 is exact and it explains the single most-cited absurdity of this cycle: How does OpenAI, a company with some $40 billion of run-rate revenue, sign $1.4 trillion of compute commitments? The same way a household with $60,000 of income signed a $600,000 mortgage: because the terms at signing do not require the payment yet, and because everyone at the table – borrower, lender, and the market – believes the growth will arrive before the payment does…

…The compute commencement wall can be made visible in exactly the way the reset wall was visible in 2007 – from disclosed contracts and delivery schedules… 

…A frontier lab signs a $12 billion, ten-year capacity commitment with a compute provider. The contract is take-or-pay, meaning the lab commits to payments once the capacity is delivered, and delivery requires a campus that does not yet exist: two years of construction, procurement, and power work stand between signature and completion.

Now look at what each party’s financial statements show during the two-year teaser.

The seller – a hyperscaler or neocloud – books the arrangement into RPO or contracted backlog on day one – the full $12 billion, disclosed, quoted, and celebrated. The market values it as contractual future revenue. Meanwhile the seller’s cash flow statement hemorrhages: the campus is being built, so capex runs far ahead of receipts. Booked backlog rises; reported earnings feel none of the buildout; financing frequently sits off-balance sheet.

The buyer – a frontier lab like OpenAI or Anthropic – announces access to the compute it needs to pursue its scaling roadmap, and its private valuation reprices on the announcement. The commitment is a future obligation, disclosed – if at all – deep in a contractual-obligations footnote or, for the private labs, nowhere public. No expense hits the P&L because no service is being received. A lab that has committed tens of billions across multiple providers carries a cost structure that reflects only its commenced capacity.

The market sees a seller with explosive backlog and a buyer with secured compute capacity, and prices both as growth stories. Nobody is lying. Every number is GAAP-clean. The structure simply guarantees that during the teaser period, the system’s reported economics and its committed economics diverge by the full value of everything signed and not yet commenced…

…In residential credit, the interval between origination boom and reset wall was twenty-four months, because that was the teaser’s term. In compute, the interval is the construction timeline – twenty-four to thirty-six months. The 2025–26 signing boom therefore mathematically guarantees a 2027–28 commencement boom, exactly as 2005–06 originations guaranteed 2007–08 resets…

…This is what it means to say we are in the teaser period. The booked figure is enormous; the billed figure is a fraction of it and only beginning to turn up. Everything about the present looks like strength. The obligations that will govern 2027 and 2028 are already signed, already dated, and already sitting in RPO. What has not happened yet is the conversion – the moment booked becomes billed and the take-or-pay clock starts running regardless of the revenue and the counterparty’s ability to pay…

…The claim is not that commencement causes a lab to fail. It is that commencement is the date on which a pre-existing mismatch – fixed obligation against assumed revenue – becomes cash-due, and that, as in 2008, the mismatch is visible in the fundamentals well before the date makes it unavoidable. You do not need demand to fall. You need it only to decelerate below the rate the booked compute was underwritten to.

The compute contracts commencing in 2025 and early 2026 cleared, or very nearly cleared, the required growth rate. This is the crucial point, and it is the reason there is no alarm anywhere in the system: the early vintages worked.

They worked the way the 2005 and 2006 subprime resets worked. The collateral appreciated fast enough. The refinancing happened. Everyone who signed was vindicated, and vindication is the input to the next round of underwriting. Success in the early vintages is the mechanism that manufactures the late ones…

…For committed compute merely to equal revenue in 2027 – not to be comfortably covered, simply to reach parity, before a single dollar is spent on wages, research, sales, or tax – revenue would have to compound at 217% annually off the 2025 base. The dashed line at 100% represents revenue doubling every single year and sustaining it, which no company at this scale of revenue has ever done for a multi-year stretch. The obligation is accelerating at more than double the rate of the best case for the cash flow meant to cover it.

The obligation curve is contractually fixed and steep – it ramps according to a defined construction timeline. The revenue curve is a growth rate. If the growth rate rolls over – the two curves cross. That is the reckoning: not a demand collapse, but a demand deceleration meeting a cost schedule that was set in a more optimistic year.

4. 90% of executives say AI hasn’t boosted productivity. Some are still cutting jobs – Mark Ma and The Conversation

One Atlanta Federal Reserve study found that about 90% of executives believe AI has not yet boosted productivity at their companies. Other evidence suggests that the broader increase in productivity seen since 2021 is more likely due to remote work or factors other than AI, like downsizing in sectors such as technology…

…Managers at publicly traded firms typically make decisions based on whether a new investment improves short-term profitability and share price. So after investing heavily in AI, managers face pressure to show a strong financial return. The expectation is that if AI makes employees more efficient, the company will need fewer of them to complete the same work.

As a result, a quick way for managers to help businesses realize that anticipated return is by cutting headcount and lowering labor costs. Some of the companies we studied even started to lay off employees before pouring money into AI, as a way to free up capital for future AI investments…

…To see how employees perceive and react to AI adoption, we analyzed millions of employee-satisfaction reviews on the workplace review site Glassdoor.com. By identifying and analyzing AI-related comments, we found them to be much more negative than the overall tone of employee reviews.

This negativity reflects widespread concerns over corporate AI adoption and anti-AI sentiment among workers. At the same time, there’s a strong association between employee sentiment toward AI and firm productivity based on the employer’s financial information. This suggests that anti-AI sentiment among workers actually lowers productivity and offsets the potential efficiency gains caused by AI…

…What businesses need to understand, I believe, is that managing how employees feel is key to unlocking AI’s benefits. In turn, that means creating an environment where workers feel that AI is working with them, not against them. 

5. Prediction: AI will collapse – wordgrammer

The modern AI discourse is making a similar mistake to saying “everyone will have a website” back in 1995.

I hear stuff like:

• People want personal software
• Everyone will build a mobile app
• The market for software is infinite

No.

You are directionally correct, but you are missing something big.

Every human will use agents. But less than 1% will use coding agents. (In the same way every human has a “personal page” through social media, but less than 1% have a personal website.)

You need an “idiotproof” sandbox. Your consumer agent, as well as agents for enterprise use cases, must have a 0% chance of failure. Your consumer agent must be usable:

• While drunk In bed
• Having sex
• On the toilet
• During lunch break
• In the background while working on homework
• While your brain is 99% preoccupied with a difficult task at work

It must be impossible for AI to: make purchases you did not intend, send messages you did not intend, and access information you did not want to share. It cannot be “difficult” for these to happen. It must be impossible. (In the same way that YouTube videos have a 0% chance of phishing, cross-site scripting, malware, and viruses.)

No amount of models getting better will give them a 0% chance of failure. To get to a 0% chance of failure, you need to be creative. You need to innovate. You need to radically rethink your abstractions.

Every human has a “personal page” through social media, but less than 1% has a personal website. What is the same shift for AI?

Every human will use _____ agents, but less than 1% use coding agents. What is _____?

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

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

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

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

Here are the articles for the week ending 30 August 2026:

1. How Countries Go Broke: The Dynamic Behind What is Happening Now – Ray Dalio

To me, the credit/market system is like the human circulatory system, bringing nutrients to all parts of the body that make up the markets and economy. If credit is used effectively, it creates productivity and income that can pay back the debt and interest on the debt, which is healthy. However, if it isn’t used well so it doesn’t produce enough income to pay back the debt and the interest on the debt, debt service will build up like plaque that squeezes out other spending. When debt service payments become very large, that creates a debt service problem and eventually a debt rollover problem as holders of the debt don’t want to roll it over and want to sell it. Naturally, that creates a shortage of demand for debt instruments like bonds and the selling of them, and when there is a shortage of demand relative to supply that either leads to a) interest rates rising, which drives markets and the economy lower, or b) the central bank “printing money” and buying debt, which lowers the value of money, which raises inflation from what it would have been. Printing money also artificially lowers interest rates, which hurts the lenders’ returns. Neither approach is good. When interest rates rise because the selling of debt becomes too large to curtail and the central bank has bought a lot of bonds, the central bank loses money, which hurts its cash flow. If this continues, it leads to the central bank having a negative net worth.

When this becomes severe, both the central government and the central bank borrow to make debt service payments, the central bank prints money to provide the lending because the free-market demand is inadequate, and a self-reinforcing debt/money printing/inflation spiral ensues.

In summary, the classic things to watch are as follows:

1) The amount of government debt service there is relative to government revenue (which is like the amount of plaque in the circulatory system),

2) The amount of selling of government debt there is relative to the amount of demand for government debt (which is like the plaque breaking off and causing a heart attack), and

3) The amount of central bank printing of money to purchase government debt to make up the shortfall in demand for government debt relative to the supply of government debt that needs to be sold (which is like the central bank administering a heavy dose of liquidity/credit to ease the liquidity shortage, producing more debt, which the central bank has an exposure to)…

…Now, imagine that you are running a big business called the US government. That will give you a perspective that will help you understand the US government’s finances and its leadership’s choices.

The total revenue this year will be about $5.5 trillion while the total expenses will be about $7.5 trillion, so there will be a budget shortfall of about $2 trillion. So, this year, your organization’s spending will be about 40% more than it is taking in. And there is very little ability to cut expenses because almost all the expenses are previously committed to or are essential expenses. Because your organization borrowed a lot over a long time, it has accumulated a big debt—approximately six times the amount that it is bringing in each year (about $32 trillion[1]), which equals about $240,000 per household that you have to take care of. And the interest bill on the debt will be about $1 trillion, which is about 20% of your enterprise’s revenue and half this year’s budget shortfall (deficit) that you will have to borrow to fund. But that $1 trillion is not all that you have to give your creditors because, in addition to the interest you have to pay on your debt, you have to pay back the principal that is coming due, which is around $10 trillion. You hope that your creditors will either relend or lend it to you. So, the debt service payments—in other words, the paying back of principal and interest that you have to do to not default—is about $11 trillion, which is about 200% of the money coming in.

That is the current situation…

…I believe that this situation needs to be dealt with via what I call my 3% 3-part solution. That would be to get the budget deficit down to 3% of GDP in a way that balances the three ways of reducing the deficit, which are 1) cutting spending, 2) increasing tax revenue, and 3) lowering interest rates. All three need to happen concurrently so as to prevent any one from being too large because, if any one is too large, the adjustment will be traumatic. And these things need to come about through good fundamental adjustments rather than by force (e.g., it would be very bad if the Federal Reserve unnaturally forced interest rates down)…

…Throughout history these debt cycles have occurred in virtually every country, typically several times, so there are literally hundreds of historical cases to look at. They go back as far as there is recorded history. Said differently, all monetary orders have broken down and the debt cycle process I’m describing is behind these breakdowns. This is the process that led to the breakdowns of all reserve currencies, like the British pound and the Dutch guilder before the pound. In my book, I show the 35 most recent cases.

Q2: If this process happens repeatedly, why are the dynamics behind it not well-understood?

You’re right that the process is not well-understood. Interestingly, I couldn’t find any studies about how this happens. I theorize that it is not well-understood because the breakdown of monetary orders typically happens only about once a lifetime in reserve currency countries and when it happens in nonreserve currency countries this process is presumed to be a problem that reserve currency countries are immune to. The only reason I discovered this process is that I saw it happening in my sovereign bond market investing, which led me to study many cases of it happening throughout history so that I could navigate them well (such as navigating the 2008 global financial crisis and the subsequent European debt crisis…

…Q5: Do you know of any analogous cases of the budget deficit being cut so much in the way you describe and good outcomes happening?

Yes. I know of several. My plan would lead to a cut in the budget deficit of about 4% of GDP. The most analogous case of that happening with a good outcome was in the United States from 1991 to 1998 when the budget deficit was cut by 5% of GDP. In my book, I list several similar cases that happened in other countries…

…Q7: Japan—whose 215% debt-to-GDP ratio is the highest of any advanced economy—has often served as the poster child for the argument that a country can live with consistently high debt levels without experiencing a debt crisis. Why don’t you take much comfort from Japan’s experience?

The Japanese case exemplifies and will continue to exemplify the problem I describe, and it demonstrates my theory in practice. More specifically, because of the high level of the Japanese government’s over indebtedness, Japanese bonds and debt have been terrible investments. To make up for a shortage of demand for Japanese debt assets at low enough interest rates to be good for the country, the BoJ printed a lot of money and bought a lot of Japanese government debt, which has led to holders of Japanese bonds having losses of 51% relative to holding US dollar debt since 2013 and losses of 76% relative to holding gold since 2013. The typical wages of a Japanese worker have fallen 55% since 2013 in common currency terms relative to the wages of an American worker.

2. AI Jitters Have Suppliers Preparing for Data Center Boom to Go Bust – Brooke Sutherland

Manufacturers say they still have more orders from data center customers than they can handle. With no near-term end in sight for the demand surge, they’re racing to add factory capacity for electrical, power generation and cooling equipment. Siemens AG, for example, is investing more than $200 million in new electrical and power distribution products facilities in Georgia and Texas, the latest in a string of expansion projects for the industrial giant and its peers meant to support the data center buildout. But with the market getting increasingly jittery about sky-high valuations and the ultimate payoff from this massive debt-fueled spending bonanza, industrial companies are also taking precautions to make sure they don’t get stuck holding the bag…

…Even as it adds factory space, Siemens is tapping third-party manufacturers to fulfill some of its equipment orders, both to help it meet high demand and to give it some flexibility if that demand falters, Powell said. “If we were to get to a situation where the market dropped 15%, we’d have the opportunity to lower some of those third-party orders and not hit our own factories as hard,” he said. “They understand that we’re not necessarily giving them stuff forever.”

The German industrial giant is also trying to stay diversified in its electrical products business, even as data centers make up an increasing percentage of the market. The data center construction boom is now so huge that private spending has surpassed that for both the general office and healthcare markets…

…Suffolk Construction, one of the largest contractors in the US, is similarly seeking to make sure it doesn’t become “intoxicated” with data centers, says Charles McCarthy, president of mission critical projects. “We’re bringing on a lot of people, bringing on a lot of resources,” he said. “We want to make sure that there’s a backup plan. And when the market does turn, when some of these things do happen, there’s an avenue for us to keep our growth moving forward.”…

…Siemens similarly sees more risk with so-called neocloud data center companies that rent out access to leading AI chips amid concerns about circular financing. “There might be one or two really big winners out of these neoclouds, and you don’t want to miss out on that,” Powell said. “But if something goes bad in the data center market, it’s probably not going to start with the big cloud providers. It’s probably going to start with one of these big neoclouds.”

3. There are ~180 net nets in Korea, and I’m writing up all of them – Oliver Sung

Chaebols themselves are old (Samsung dates to 1938 and both Hyundai and LG go back to 1947) but the system that made them what they are was built in the 1960s and 70s. Park Chung-hee took power in a coup in 1961, and after he gained power, he put the commercial banks under government control and decided to point cheap credit at a handful of families he’d picked to industrialize the country. If these families hit their export target, well then more loans would follow at rates that were negative in real terms. The money that built corporate Korea came from the state and then from the banks, never really from shareholders, so the shareholder was never the constituency that mattered. That conflict between controllers and minority holders has been running ever since, and it’s been incredibly difficult to resolve.

Each one of the companies under a chaebol, some listed and some not, is called an “affiliate.” Samsung, Hyundai, and LG each run dozens of affiliates, and the largest 81 chaebols in the country count >3k affiliates under their umbrella. Why this is an issue when it comes to governance is that the families keep a tight grip on their affiliates through a spaghetti-fashion of cross-holdings between them. It’s not abnormal for a family to own, say, just 3.7% of a company but control 62.4% of the votes through a block of affiliates, meaning you could buy as much of the company as you’d like and still be the minority next to a family that owns <4% of it. If you wonder why I picked such odd percentages for illustration, you’ve probably already guessed that those aren’t illustrative but are the real numbers. 3.7% ownership vs 62.4% control represents the average across the country’s chaebol affiliates…

…Underlying that issue is a bunch of things that have traditionally been wrong with the machinery and have caused the chaebols to not only treat minorities unfairly but also hoard cash and create a jumble of corporate pyramids. Even worse, this machinery incentivized the controllers to in fact keep their own share prices down, and there have been at least four moving parts to it:

1. Dividend taxation. In Korea, once an individual’s financial income passes KRW20mn/year, dividends get folded into progressive rates that approach 50%, so the rational move for controllers has been to hoard the cash and pile it into low-return assets and further cross-holdings.
2. Inheritance taxation. Korea taxes inheritance at up to 50%, with a surcharge on controlling stakes that takes the bill toward 60%. And because the taxable value of listed shares is the average market price over the four months around the transfer, a family planning succession has a large and entirely legal incentive to keep their share price down for years.
3. Merger rules. A Korean merger ratio is typically set by averaging recent market prices rather than by any fair-value opinion. If the merger ratio is decided by the marginal buyer in the market rather than negotiated in the boardrooms, then that’s been good enough for the regulators. In 2015, the Samsung chairman was dying and his son, Lee Jae-yong, needed to end up controlling Samsung Electronics, which he barely owned any of, without triggering the inheritance bill. What he did own was a large slice of Cheil Industries, a small company in the group. Samsung C&T, a separate one, held a block of Samsung Electronics shares. So Samsung decided to merge C&T into Cheil right when C&T was trading at historic lows and Cheil at historic highs. The national pension fund, C&T’s biggest shareholder, swung the vote. People went to prison over that vote (including Lee Jae-yong, but he was later acquitted of all charges related to the merger), and the deal stood anyway.
4. Misuse of treasury shares. Because treasury shares haven’t traditionally been cancelled in Korea, in many cases they’ve been used to abuse shareholder value. There have been numerous cases of controllers swapping treasury shares with friendly parties, which is precisely what happened last year when Muhak, a local brewer, executed two treasury stock cross-swaps with its main glass bottle supplier and Samsung Gongjo, an auto parts company in the same region. This has meant that a Korean buyback is less a return of capital than a block of dormant votes bought with shareholders’ money and parked until the controller needs them…

…But the government is taking much deeper stabs at it from multiple fronts:

1. In July 2025, amendments to the Commercial Act were approved to require directors to balance corporate and shareholder interests. This is similar to what Japan enacted in its Stewardship Code around 2014. Before this amendment, a director’s duty ran to the company, which in a family-controlled company meant the family. This amendment is likely to mean more fairness in mergers, spins, splits, delistings, and other corporate transactions going forward.
2. Then in December 2025, the National Assembly approved a massive reduction in the dividend tax rate to a range of 14-30% for “high-dividend payers.” A “high-dividend payer” is one that has a payout ratio of >40% or has a payout ratio of >25% and increases it by 10% from the prior year. Crucially, to qualify, the company must also have a Value-up plan disclosed on the KRX. This is the first reform that really rewires the incentives for return of capital.
3. Finally, in February this year, the National Assembly passed another amendment requiring mandatory cancellation of treasury shares. Companies must now cancel newly acquired treasury shares within one year, and existing treasury stock got an 18-month grace period. This is a big deal…

…It’s important to mention that regulators have also decided that Korea has too many listed zombiecos, and so the exchange has started clearing out from the bottom. The minimum market cap for staying on KOSDAQ went to KRW20bn in July and will reach KRW30bn in January 2027. 30 trading days under the line brings a warning, then 90 more days to climb back above it, or the company gets delisted. The first company went out in June, another 36 were flagged just a week ago on August 12, and something like 1/10 of KOSDAQ could be gone by the end of the year.

4. We Bought a $500 Counterfeit Rolex So Good, Even Rolex Didn’t Spot It – Alistair Charlton and Jeremy White

I’m sitting upstairs in Rolex’s flagship London store on Old Bond Street. I’ve just handed over a fake Rolex to the staff and requested that its bracelet be adjusted, a simple bit of maintenance that requires removing a few links. I haven’t volunteered that the watch is a phony, but I haven’t said it’s legit, either…

…After what feels like far too long, the salesperson returns. He, like his colleagues downstairs did when I arrived, congratulates me on a beautiful watch and hands me back the counterfeit timepiece, now perfectly sized. The removed links have been placed reverently in a delicate, tiny paper bag, marked with the Rolex logo in cadmium green. Heart still pounding, I walk out…

…Mass-produced in huge quantities by Chinese factories, these watches are sold through dealers who advertise on TikTok and Instagram, broker through WhatsApp, and arrange discreet delivery to your door. Since reputation by word-of-mouth is paramount (these businesses can’t exactly show their wares in a digital shop window), they even claim to offer customer service channels, warranties, repair centers, and guarantees to send a replacement if your purchase is seized by customs…

…Quality control, or QC, is at the heart of this community. The subreddit r/RepTime has 269,000 weekly visitors and over 18,000 weekly contributions, according to Reddit’s own statistics. Posts about new counterfeit watch purchases pile up by the hour, both in r/RepTime and r/RepTimeQC, the offshoot subreddit focused on quality control. The posts contain photographs sent from the dealer to the customer, who then seeks advice from the RepTime community on whether they should “GL” (green-light) or “RL” (red-light) their purchase…

…Even a Reddit community with more than a quarter of a million weekly visitors represents a mere fraction of the global fake-watch industry. The Federation of the Swiss Watch Agency has estimated that tens of millions of fake watches are produced every year, dwarfing the number legitimately manufactured by Swiss brands. And, while many super clones sell for between $500 and $700, there are cases of ultra counterfeits posing as watches worth millions.

“There’s a very well-known Patek,” says counterfeit watch expert Adrian Hailwood, referring to an automatic model with the reference number 3448. It was the first serially produced automatic perpetual calendar wristwatch, and there have long been rumors that two examples were made in rose gold for a South American retailer. Only one is publicly known, a 1968 example first sold in Uruguay. In 2011 it was sold at auction by Christie’s for CHF 2,099,000 (about $2.3 million at the time), then sold again in 2025 for CHF 2.7 million (about $3.3 million).

“Allegedly there’s another one,” Hailwood says. “But I’ve seen three—all pretending to be that one, and all of them with faults. So somebody is churning out rose gold 3448s. And if it’s potentially got a list price of a million pounds, there’s a lot of incentive to put them together.”

Hailwood says that because such a piece would have real Patek innards and a totally fake outside, requiring a lot of work and resources and access to parts, each would cost between £20,000 to £30,000 to create. “You can imagine someone going, ‘Look, we can’t put this through public auction, it would be a million pounds. But, say, £400,000 and ask me no more questions.’ People with more money than sense would potentially go for that,” he says.

Million-dollar Pateks tend not to crop up on r/RepTime. Instead, the vast majority of watches subjected to quality control checks are replica Rolexes. Posts scrutinizing the fine details of Submariners, Datejusts, and Daytonas are especially frequent, along with a smattering of Tudor, Omega, IWC, and Cartier replicas. Most tend to be stainless steel, since their low manufacturing cost means these clone watches cannot be made from real precious metal…

…For a thorough expert evaluation of our cloned watches, WIRED met Adrian Hailwood at the UK offices of Watch Collecting, an online watch auction platform he helped launch in 2021, and for which he now provides authentication services. Since much of the production cost of super clones goes on the dial, Hailwood suggested inspecting other parts of the watch.

Under the jeweler’s loupe, he spotted bracelet screws that “really aren’t very round,” and the underside of a clasp that “is very much raw metal” and “not particularly nice.” These imperfections were followed by a Rolex logo on the bracelet which had been “milled out quite amateurishly … Almost looks like someone has done it with a Dremel.”

Hailwood plowed on. “The luminous material looks like it’s not set particularly nicely … The pip is not centered in the triangle, and it’s slightly too yellow for a modern watch.”

One startling piece of accuracy, however, is the coronet (Rolex’s crown-like logo), which since the early 2000s has been laser-etched discreetly into the 6 o’clock position of the sapphire crystal. A couple of millimeters wide and only visible under bright light, the supposed anti-counterfeit measure was present in our replica Submariner. On some fakes the coronet is fudged with a sticker or crudely scratched into the back of the sapphire. Other times it seems genuine, but fails to match the original, which is made up of tiny stars of varying sizes, not just the etched dots of the counterfeit.

Opening the watch revealed a replica movement that looked, at first glance, very much like the real deal. It appeared to be “reasonably good,” but then Hailwood pointed out how there’s a balance regulating arm to help fine-tune how quickly the watch runs—a type of movement that Rolex hasn’t used since 1957. Rolex now uses movements with free-sprung balances, so, in a bid to fool authenticators, counterfeiters have been known to swap the whole assembly around, obscuring the balance components. “That caught a lot of people out,” Hailwood says.

Since WIRED’s watches were examined, however, the counterfeiters have already upped their game. Hailwood claims free-sprung balances have now been introduced across clone movements imitating those by Rolex, Patek Philippe, Richard Mille, and Audemars Piguet. Once a relatively small-volume upgrade, these are now “industrialized features” that eliminate one of the tells he identified in our replica Submariner.

“The investment to produce this kind of clone movement … this is not back-alley sweatshop stuff,” says Hailwood, “this is big factories investing millions to produce full-on clones. It’s said by some watchmakers that these are now close enough that they can be serviced with Rolex parts, which does raise the specter of someone taking a genuine Rolex balance out and swapping it in [to a fake].”…

…According to a May 2025 report from the Organization for Economic Cooperation and Development, the global fake goods trade was worth $467 billion in 2021—the most recent data available—and accounts for 2.3 percent of all global trade, rising to 4.7 percent in the European Union. Clothing, footwear, and leather goods are the most commonly seized counterfeit goods, while watches rank fifth by quantity, and first, comfortably, by value.

A second OECD report focused on Switzerland found that, also in 2021, around $4.7 billion worth of counterfeit goods infringed Swiss trademarks, leading to a $3 billion loss in domestic sales. More than 40 percent of those goods, by value, are watches, with roughly 65 percent of seizures originating in China and Hong Kong. Overall, the global fake Swiss watch market equates to about 7.7 percent of the value of Switzerland’s legitimate watch exports, but with far lower production costs and sales prices per unit.

But remember, these figures are based on seized goods, not those that slip through the postal system unnoticed…

…Ultimately, what’s most striking isn’t merely the scale of this barely hiding counterfeit industry, but the misplaced enthusiasm driving it. These are real watch lovers who have built a genuine online community—people who are fluent in reference numbers, obsessing over index alignment and lume color. They are thrilled to share their knowledge and time, for free, with curious newcomers. It’s very much a genuine passion for watchmaking, but like the crooked hands of a red-lighted replica, it’s pointing the wrong way.

5. Let the Bond Market Speak – Stanley Druckenmiller

The Treasury Department announced on Aug. 19 that it would double the size of its long-dated bond buybacks, from $2 billion to at least $4 billion per operation, aimed at the 10- to 30-year sector and running from Sept. 9 through Nov. 4. The announcement came after the 30-year yield touched a 19-year high. Yields fell within minutes. By the next afternoon they had round-tripped to levels above where they started. The market’s verdict was swift and correct: This wasn’t liquidity management, it was price management—and a mistake far larger than $4 billion suggests…

… Inflation is 3% to 4% and has been above the Fed’s target since 2021. Unemployment is 4.1%, full employment by any definition. The deficit is running near 6% of gross domestic product, a number America has never before produced in peacetime at full employment. The national debt crossed $40 trillion the same week Treasury intervened. Net interest will exceed $1.1 trillion this fiscal year, more than the defense budget. The 10-year yield, even after the summer selloff, sits at or below the economy’s nominal growth rate. That means a borrower (federal government) running 6% deficits at full employment, with above-target inflation, still funds itself at roughly the rate its economy grows….

…The long-term Treasury yield is the most important price in the world. It is also the only fiscal disciplinarian the U.S. has left. Neither party will run on entitlement reform. Both have spent the past decade expanding commitments while ignoring arithmetic. Democracies don’t repair their finances because a budget office publishes a table. They repair them only when the cost of inaction becomes visible and immediate, when mortgage rates bite, when auctions tail, when the political price of a rising long bond finally exceeds the political price of touching spending…

…Every basis point of artificial yield suppression is a subsidy to procrastination. Suppressed long rates sugarcoat the interest-cost projections, shrink the apparent urgency, and let incumbents assure voters the debt is someone else’s problem. If Congress and the administration are unlikely to touch entitlements even with the market’s signal, they are certain not to touch them without one. Whatever this operation saves in basis points, it will cost multiples in delay…

…The defense of the buybacks writes itself: It is a routine tool, introduced in 2024 for liquidity and cash management, trivial against a marketable debt stock approaching $30 trillion. All true but beside the point. Routine operations aren’t announced off-cycle, at double size, on the heels of the long bond’s hitting a two-decade high, with a signal that they can grow without limit…

…What should happen instead is straightforward. Return buybacks to their stated purpose: small, scheduled, off-the-run liquidity operations announced at quarterly refundings, never off-cycle responses to yield levels. Term out the debt honestly and pay the price the market sets. If the 30-year must trade at 5.5% to clear, that isn’t a crisis. It is an invoice. Then do the only thing that durably lowers long-term yields: address the primary deficit. Reform entitlements gradually and honestly, through means testing, indexing changes, eligibility adjustments phased in over decades—so that the burden is shared across generations instead of dumped on the youngest…

…Governments defending prices against fundamentals always lose. The only variable is how much they spend before conceding. 

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Here are the articles for the week ending 23 August 2026:

1. AI Is Dead. Organoids Are Alive – Claire L. Evans

I mean that if lab-coated biologists took a sample of your skin and very carefully manipulated the cells inside it, they could actually make a brain. They do it all the time.

Not a brain as complex as the one behind your eyes, of course, but a glob of gray matter nonetheless, with a few-million-odd neurons that can send and receive electrical signals. Biologists call these strange creations human brain organoids. Kept at a womblike 98.6 degrees Fahrenheit for eight months, they’ll produce repetitive oscillations—brain waves—nearly indistinguishable from those made by a premature baby…

…To make a brain organoid, all you need is that sample of skin I mentioned before. (Samples of blood, hair, or teeth work too.) You take the adult cells and introduce them to some special proteins that revert them to their embryonic state. Given a second chance to mature, these so-called induced pluripotent stem cells can become anything: tear gland organoids that cry, heart organoids that beat, or brain organoids that … well, that’s the question.

In utero brain development is, as one bioethicist told me, “a black box” of scientific knowledge. Historically, a lot of what we know about it is inferred from studies with mice. But with an organoid, the transformation of stem cells into neurons into brain tissue happens in full view. In theory, scientists could one day study how a colony of dividing cells comes together to create a mind—to make, from 86 billion neurons, a person…

…Visually, organoids are not compelling; they’re opaque, snot-colored, the approximate size and shape of a chia seed. Muotri’s organoids contain 5 million cells, of which 2.5 million are neurons. (The rest are non-neural glial cells, which serve as scaffolding.) This, he reassures me, is the size of a bee’s brain…

…In Melbourne, Cortical Labs cultivates flat neural cultures—the stem cells were donated by the company’s own founder—and loads them into sleek white biological “computers” called CL-1s. Each is the size of an elongated toaster and boasts an onboard life-support system capable of keeping a culture of up to a million neurons alive for six months. With the CL-1, Cortical Labs is aiming to become the Nvidia of neural computing, providing hardware and, let’s say, “neurons as a service” with a sub-millisecond delay.

For now, these neural computers are mostly of interest to researchers who want to work with neurons without taking on the tedious wet-lab husbandry themselves. Eventually, however, the company hopes that neurons will prove themselves to be an energy-efficient, resilient substrate for more general computing applications—including some tasks currently handled by AI, like image recognition and classification…

…In 2022, using a system similar to what currently powers the Cortical Cloud, Kagan grew a neural culture on a microchip and trained it to play the 1972 Atari game Pong, rewarding the neurons with predictable electrical pulses when they made correct decisions and punishing them with chaotic bursts when they made mistakes.

The technique served as a minimal proof of concept for a theory, proposed by the neuroscientist Karl Friston, that self-organizing biological systems tend to minimize surprise whenever possible. By showing that neurons will reorganize themselves to avoid chaotic stimulus, Cortical Labs demonstrated one possible approach for programming living matter. But the experiment also signaled that the CL-1 could be considered hardware for testing theories of cognition…

…WHEN I SET out to report this story, I was sure it was about consciousness: the eerie moment a quarter-peanut of flesh sparks with self-knowledge, and what that precipice means for the researchers responsible. I imagined long dark nights of the cell and tiny funerals for spent neurons. What I found, however, was that nearly all scientists who keep organoids see the consciousness question as a distraction.

Most bristle when asked. They gesture to the organoids themselves—tiny balls bobbing in liquid solution like droplets of olive oil in vinegar—as if to say, give me a break. “Consciousness is so qualitative,” complained Annie Kathuria, an organoid researcher, when I visited her lab at Johns Hopkins. “How am I supposed to measure something qualitative on a tissue that’s floating in a dish? Someone has to define it. That’s what I say to everyone: Define to me what consciousness is, in quantitative terms.”…

…Conveniently, organoids can’t grow much bigger than 5 millimeters anyway. Once they reach a certain size, without a vascular system to pump in oxygen and pump out metabolic wastes, they develop a “necrotic core” and suffocate. This has created an upper ceiling for the organoid debate, but it won’t hold much longer. At Johns Hopkins, the nanotechnologist David Gracias is developing biomimetic artificial arteries; across town, at the Medical School, Kathuria creates rough blood vessel networks from endothelial organoids. Researchers are keeping organoids alive longer and longer. Muotri’s record, of three years, might’ve gone longer had someone in his lab not dropped the dish.

“THE GOAL IS to get to a 1-centimeter brain organoid,” said Thomas Hartung, as he walked me around the Center for Alternatives to Animal Testing, or CAAT, at Johns Hopkins, where he’s experimenting with new perfusion systems to get around the bloodlessness problem. One centimeter is roughly the size of a mouse brain—“a completely different beast,” Hartung said, than the 500-micrometer organoids he’s accustomed to…

…Nearly every organoid researcher I met reporting this piece told me the same fact: that in human clinical trials, the failure rate for neuropsychiatric drugs is close to 95 percent. The pipeline for new medications for conditions like depression, Alzheimer’s, and epilepsy is long, and often dry. That’s because drugs are ordinarily tested on animals, not people, and animal testing has never been the most physiologically relevant way to ensure the efficacy of drugs—it’s just been the most practical one. Organoids have changed that calculus…

…In July 2025, the National Institutes of Health announced that it will no longer award grant funding to research that relies exclusively on animal testing, encouraging consideration of “new approach models,” like organoids, instead; in September, the NIH announced an $87 million investment in building a Standardized Organoid Modeling Center. Fortunately for the NIH, since organoids are made with induced pluripotent stem cells from consenting adult donors, they don’t inspire the same religious ire as embryonic stem cell research did in the 1990s. And unlike lab rats or primates, organoid testing doesn’t irk the animal rights crowd.

Recent studies suggest that ordinary people, by and large, aren’t much bothered by organoids. When they do lodge an objection, it’s not because globs of neural cells might someday achieve human-level sentience. It’s because brain organoids are creepy. They muddy the line between person and thing, which, like the difference between humans and animals, or between the dead and the living, is a fundamental distinction that transcends cultures…

…Somewhere between the few million neurons in a brain organoid and the billions more that make up our minds, selfhood emerges—and absolutely nobody knows when…

…For the AI people, machine intelligence seems achievable, even inevitable. They “feel the AGI.” Part of this is hype; part of it is a willingness to take intelligence for what it does, as opposed to what it is. “If it quacks like a duck, it is a duck,” said John Evans, the UCSD sociologist. “The Silicon Valley types are super pragmatic. If AI is capable of doing what a human with consciousness does, they’ll call it consciousness.”

Biologists who work with neural cultures, however, are far less likely to use such a word without plenty of agonizing caveats. These biologists cultivate the raw meat of mind every day; they’re in the privileged position to understand how colossally complex it really is. As biology and AI converge, the culture shock between these worldviews is likely to be bracing.

For now, it seems that in the race toward conscious machines, there will be two lanes. One will be paved with rare earth minerals and silicon—forced, at enormous financial and energy cost, to model the brain from the top-down, in the form of artificial neural networks…

…Which leaves the other path. Admittedly, this one will be slippery and meandering—adapting, as all living things do, to the journey as it goes. It might not be the most direct route, but in the evolutionary history of life on Earth, it’s the only thing that has ever led to intelligence.

2. GEN-1.5 – Generalist Team

The ability to learn closed-loop physical skills from just one or a few demonstrations, and to do so across a broad range of tasks without such restrictions, has predominantly been considered out of reach. Such an ability may also likely be underpinned by a foundation that enables other broad generalization capabilities…

…We’ve created GEN-1.5, our latest robot foundation model that exhibits broad one-shot and few-shot learning from demonstration capabilities, as well as zero-shot generalization, e.g. improvisation and novel tool use (e.g. brush, dustpan, etc.). GEN-1.5 is a large multimodal model that processes video input (30 seconds of memory, alongside other sensor, language, and proprioceptive inputs) and produces 100 Hz action trajectories. Its capabilities include:

• One-shot learning via in-context prompting. The model learns new tasks in seconds when prompted with 3 to 12 seconds of a single demonstration, no training required. We refer to the use of sensorimotor examples in the context window as “physical prompting.”…
• …Few-shot adaptation via gradient descent. The model can be fine-tuned to a new task in 1–10 gradient steps on 1–5 minutes of data (~10–50 demonstrations).
• Improvising new strategies and tool use. The model generalizes at the level of behavioral strategies: forming entirely new trajectories to reach a goal, using unseen tools (e.g. brush, dustpan, etc.) to create new solutions to tasks demonstrated with other tools, and working ambidextrously even when prompted or fine-tuned to perform the task with a specific hand.

These capabilities appear to emerge directly from pretraining on large amounts of physical interaction data. We did not explicitly train for any of them: no architectural changes to promote in-context learning, no inner or outer meta-learning loop pressuring the model to adapt from minimal data, no auxiliary objectives encouraging improvisation…

…Although the tasks are simple and short-horizon, and the success rates are modest, this is the first model we know of that has demonstrated the general ability to learn a wide range of dexterous closed-loop physical tasks from just one-shot or few-shot demonstrations…

…GEN-1.5’s initial pretraining began in parallel — it has now been training continuously for over eight months. We left it running because every metric we tracked kept improving with the engine: absorbing more data, scaling more efficiently with compute, and achieving step-change gains with successive surgical architectural and algorithmic changes. It was clear that the model was getting better, and the trend was consistent: new tasks were becoming more data-efficient, more compute-efficient, and more general.

As the model continued to train, we began experimenting with how few finetuning steps we could use to adapt to new tasks, finding the model could learn new tasks from 100s, then 10s, then eventually, 1 gradient step on just one minute of data. As far as we know, the ability to learn skills with such few gradient steps had not been observed before. We then asked, can this model learn new tasks without training, purely in-context and with zero gradient steps? That this works at all changes how we think about how these models can be used, about their potential impact, and the road ahead for building general physical intelligence…

…GEN-1.5 can be prompted with a single demonstration inserted into its 30-second context window, and the remainder holds rolling observations. Physical prompts are sensorimotor examples (i.e. sensor data plus action trajectories), recorded either as human data (with a pair of handheld grippers) or as rollouts from the robot itself. Once the prompt is in context, the model performs the task immediately, with no training steps. The performance of one-shot learning in-context is modest (59% average success across diverse tasks including handling zippers, opening jars, grabbing money out of wallets, etc.), but the fact that inserting a single demonstration in the context buffer, without ever training for it, yields any measurable competence at all was unexpected…

…We did not explicitly train GEN-1.5 for in-context learning, and the tasks we tested were not engineered into the pretraining data beforehand. This is a general model which we are prompting without regard to the pretraining data distribution.

Why this capability emerges from pretraining is difficult to pinpoint. One hypothesis, by analogy to language, is that the distribution of physical observations and actions may exhibit “burstiness” and Zipfian structure of the kind that has been linked to in-context learning in language models.17 It is also possible that physical work contains naturally repetitive cycles, and the model may have learned to detect and extend such patterns, as language models do with general sequences.18 The model was pretrained on randomly sampled continuous spans from our data engine (activities captured in homes, warehouses, factories, and elsewhere) with no bespoke infrastructure for packing examples into context — physical prompts introduce discontinuous jumps in time that the model never saw in training.

Robotics is inherently multimodal; and as in human learning, there are many ways to teach a robot something new — the two options of either (a) demonstrations or (b) language instructions are perhaps the most natural for having humans specify tasks.19 While language suffices for some task specifications, many physical actions are difficult to precisely describe in language20 (e.g. it is far easier to show exactly how to seat two Lego bricks than to say it). Prompting a task in native observations and actions is also a more comprehensive test of sensorimotor understanding: the model must infer the goal from the demonstration, repurpose existing knowledge, and improvise under new initial conditions…

…In some cases, in-context learning transfers across the embodiment gap entirely: a human demonstrates a task with their own hands, observable through the robot’s cameras, and the robot can reproduce it immediately afterward….

…In one example, we demonstrated using a brush to sweep a block into a bowl, and fine-tuned the model on 5 minutes of human demonstrations. The model was able to figure out how to use a variety of other tool options besides the brush in order to accomplish the task. When presented with a banana, it used the banana as a makeshift brush. When presented with a dustpan, however, the model exhibited a larger strategic departure from its demonstrations, and through a variety of means would use the dustpan to lift up the block and dump it into the bowl. Neither the fine-tuning data nor, to the best of our knowledge, the pretraining data contains a dustpan used this way, and the nearest pretraining examples bear little resemblance to the task. Handed a dustpan, the model composed an entirely new contact sequence to complete the task out of the box, with no language guidance…

…Although the model was only fine-tuned to put the block into a bowl, it appears to be able to remove obstacles (like a piece of paper covering the bowl) to complete the task, and sometimes place the paper back on top of the bowl. There was no paper covering the bowl in the 5 minutes of task-specific data (with which the model was fine-tuned for only 1 gradient step), and no such task in this setting (to the best of our knowledge) was in the pretraining data…

…GEN-1.5 is a milestone we believe to be profound scientifically, not because of higher success rates, but because it represents a new frontier of generality — one that challenges our own understanding of how these models behave when pretrained at a scale of physical interaction data few thought possible without shortcuts…

…What is clear now, and perhaps obvious in hindsight, is that past a certain threshold of pretraining, the cost of adaptation becomes negligible. Emergent in-context learning from a few seconds of data, or one gradient step on one minute of demonstrations, is no longer task-specific training in the conventional sense. It is closer to reminding the model of something it nearly knows, with a tiny amount of compute. That this works at all, changes how we think about how these models can be used, about their potential impact, and the road ahead for building general physical intelligence.

3. Apple forced to restructure ATT – Eric Benjamin Seufert

Yesterday, the German competition authority, the Bundeskartellamt, concluded its antitrust investigation into Apple’s App Tracking Transparency (ATT) framework after Apple agreed to legally binding changes to the ATT prompt and related consent design…

…Apple now has four months to implement these changes, and the commitments will remain binding for seven years…

…I believe the agreed-upon changes to the ATT prompt, as well as the ability for developers to bundle other data-use consent requests with the ATT prompt, will nudge opt-in rates upward by a non-trivial amount. Apple has stated that the changes will apply in almost all EU countries, and I expect the remaining European investigations to resolve similarly.

But I’m skeptical that the US will adopt similar measures, and for that reason, the broader impact on the digital advertising market of this restructuring of ATT will be muted: without any changes in the US, and given that historical ATT opt-out decisions will not be automatically reversed en masse, my sense is that the principal impact of the concessions extracted from Apple by the Bundeskartellamt will be precedential rather than immediately economic…

…So ATT will survive, but Apple’s ability to impose asymmetric rules on third parties under the banner of privacy without meaningful constraint will not. The immediate economic impact of these concessions may be modest, particularly if the changes stop in Europe, as I believe they will (for a more extensive argument on why I think that’s the case, see Could ATT be rolled back?). But their precedential impact is consequential. ATT was never simply a privacy policy; it was an exercise of platform power, and that power is now circumscribed.

4. A $21 Billion ‘Kids in Chips’ Startup Is Scooping Up Nvidia Talent – Robbie Whelan

Etched doesn’t make software but semiconductors, a product category whose high capital costs and long development cycles have relegated it to a corner of Silicon Valley where experience still trumps youthful ambition…

…Etched has reached a key milestone: signing up its first customer—Jane Street, the secretive Wall Street quantitative-trading giant—for its product, a server rack filled with AI processors optimized for rapid inference computing. The startup has booked more than $1 billion in orders and already started shipping chips…

…The startup says it took just 44 days after getting its test chips back from Taiwan Semiconductor Manufacturing to have them up and running inference workloads—the computing processes that allow AI models to respond to user queries—a process that usually takes six months or more.

“We may be the only AI chip that was built by a startup that was successful on the first try,” said Robert Wachen, co-founder and president of Etched…

…Around 15% of Etched’s roughly 400 employees previously worked at Nvidia, and it has recruited aggressively from the market-leader, as well as from other established semiconductor firms…

…Then there is the 2-megawatt in-office data center. Lined with refrigerator-sized server systems, the room emits a low roar from its cooling systems and allows potential customers to remotely access and try out Etched’s chips.

Another way in which Etched is atypical is in how much of its supply chain it owns and controls. The company has a team of 20 in Taiwan, where it owns a factory that builds server components. Most chip startups outsource much of the testing of their finished products, while Etched does almost all of it in-house.

Company executives say they design their chips using a distinctive design approach called “cluster-scale memory,” a way of allowing multiple chips inside a server to communicate more quickly and thus act as one processor. They say communications tasks that take about 4,000 nanoseconds for an Nvidia Blackwell processor to perform can be done by Etched’s chips in 700 nanoseconds, thanks to the chip’s architecture and custom interconnections.

5. The Future Of AI Compute Won’t Run On Just One Kind Of Chip – Liz Allan, Satadal Bhattacharjee, Ashish Darbari, Moshiko Emmer, Sharad Chole, Cameron Brunner, and Sumit Vishwakarma

Bhattacharjee: That’s right. One of the trends we are seeing, which Nvidia started, is disaggregating the inference pipeline. In inference, there are a few stages. One is called prefill, where you enter a prompt and it’s just trying to figure out what you’re asking so that it can take action. The prefill stage is extremely compute-intensive. Recently, Nvidia announced that its Groq 3 LPU (language processing unit) will be used in the prefill cluster, showing more than a GPU is required to do some of these tasks. Then there’s decode, where it actually does the task, or creates the response that will be generated and shown. Then with the agents coming in, there is the tool calling or executing the task — for example, booking the Uber ride or making a hotel reservation.

We are seeing that with inference disaggregated, you have a cluster with a specific set of hardware and software to do the pre-filled stage. Then, you have a cluster to do the decode stage, and then you have another fully compute cluster to do the execution by the agents. And these are all stitched together. They’re all communicating with each other, most likely through Ethernet right now, but each of the clusters has a different mix of software and hardware to do the function that they do best. That’s going to be more of the norm going forward, because until now every AI problem was solved with a GPU, and the industry is recognizing that every nail doesn’t have one hammer…

… There are some companies that have come up, such as Gimlet Labs and Together AI. Their pitch is that they provide the software layer to run on top of a heterogeneous hardware environment, even on disaggregated inference, and make sure the prefill cluster is run optimally, the decode cluster is run up to value, and the compute cluster is run optimally to do each of the workloads. They are taking care of the software orchestration, and they are working with different companies to do that. This is a very critical part when you go beyond the top hyperscalers like Google, which are invested in creating these custom TPU clusters. Not many companies can do that. Especially if you go to the neocloud guys, which are the next level of cloud providers like DigitalOcean, CoreWeave, Lambda Labs [now Lambda AI], Verda, and others, they’re all going to have this same challenge. There is a desire to bring more hardware diversity. There’s a desire to bring token costs down — of course, without sacrificing performance — and do it without having a single company’s hand holding you. This is a big challenge that we are facing right now. We are at the early stages. It will take some time before this problem is solved, but there’s a lot of concentrated effort going into building this heterogeneous cluster with optimized software coming and solving this big problem that we have, where everybody is waiting in line for Nvidia systems because that’s the only system that is proven to be working at scale right now.

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

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

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

Here are the articles for the week ending 16 August 2026:

1. Amazon 2004 shareholder letter – Jeff Bezos

Though some may find it counterintuitive, a company can actually impair shareholder value in certain circumstances by growing earnings. This happens when the capital investments required for growth exceed the present value of the cash flow derived from those investments. 

To illustrate with a hypothetical and very simplified example, imagine that an entrepreneur invents a machine that can quickly transport people from one location to another. The machine is expensive—$160 million with an annual capacity of 100,000 passenger trips and a four year useful life. Each trip sells for $1,000 and requires $450 in cost of goods for energy and materials and $50 in labor and other costs.

Continue to imagine that business is booming, with 100,000 trips in Year 1, completely and perfectly utilizing the capacity of one machine. This leads to earnings of $10 million after deducting operating expenses including depreciation—a 10% net margin.

The company’s primary focus is on earnings; so based on initial results the entrepreneur decides to invest more capital to fuel sales and earnings growth, adding additional machines in Years 2 through 4…

…It’s impressive: 100% compound earnings growth and $150 million of cumulative earnings. Investors considering only the above income statement would be delighted. However, looking at cash flows tells a different story. Over the same four years, the transportation business generates cumulative negative free cash flow of $530 million…

…Notice, too, that a focus on EBITDA—Earnings Before Interest, Taxes, Depreciation and Amortization—would lead to the same faulty conclusion about the health of the business. Sequential annual EBITDA would have been $50, $100, $200 and $400 million— 100% growth for three straight years. But without taking into account the $1.28 billion in capital expenditures necessary to generate this ‘cash flow,’ we’re getting only part of the story—EBITDA isn’t cash flow…

…Unfortunately our transportation business is fundamentally flawed. There is no growth rate at which it makes sense to invest initial or subsequent capital to operate the business. In fact, our example is so simple and clear as to be obvious. Investors would run a net present value analysis on the economics and quickly determine it doesn’t pencil out. Though it’s more subtle and complex in the real world, this issue—the duality between earnings and cash flows—comes up all the time.

2. Nvidia’s Risky Business – Ben Thompson

Today corporate executives and financial engineers don’t need to control newspapers; thanks to his new X account, Nvidia CEO Jensen Huang can go straight to the public. From an X Article posted last night:

NVIDIA AI Factory Compute Is Becoming an Investable Asset Class

Today, we announced partnerships with Apollo, BlackRock, Blackstone, Brookfield, Goldman Sachs and KKR to establish independent financing platforms designed to mobilize over $500 billion of third-party capital to support the buildout of AI infrastructure over time.

This is a major milestone for NVIDIA and the AI industry. We have moved from an era in which companies bought chips and built data centers project by project to one in which AI factories can be financed as productive infrastructure — with repeatable platforms, long-term institutional capital and a diverse customer base that uses compute to create revenue.

AI has reached an inflection point. It is moving from research into production. AI is creating real value, and the infrastructure behind it is becoming one of the world’s most productive assets. In AI, compute is revenue.

Huang argues that Nvidia-based AI factories are fungible, protecting residual value, and that CUDA makes AI factories better over time, extending their economic value; according to Huang:

These are the characteristics of an investable infrastructure asset: it produces revenue, serves a broad market, improves in performance over time and can be redeployed.

Thus the attempted formalization of a new investment structure:

The demand for AI infrastructure is extraordinary. But access to capital is uneven. Many great AI companies, enterprises and AI clouds have demand for compute but do not yet have access to financing at the scale or cost required to build quickly. That is why we are partnering with the world’s leading long-term capital providers.

Apollo, BlackRock, Blackstone, Brookfield, Goldman Sachs and KKR are also among the world’s leading infrastructure investors, with deep expertise in underwriting long-lived, productive assets. Together, we are creating repeatable financing platforms to help the AI ecosystem build the factories it needs.

What Apollo et al. are, are new sources of capital beyond the investment grade debt markets. In that sense this proposed structure is somewhat akin to Google’s equity issuance: a way to secure funding beyond bonds. The difference, however, is stark: whereas equity dilutes the upside for investors without adding risk to the company, this structure preserves Nvidia’s margins by finding new pools of capital willing to bear risk.

It’s not a total free ride for Nvidia: the company is backstopping opportunities with up to 25% residual-value based financing, suggesting that Huang believes his “investable asset class” pitch much more than the market does. That is, in a certain sense, a price cut, as the goal is to reduce the cost of capital for entities building data centers with Nvidia chips, by putting Nvidia’s profits on the line for uncertain investments. That guarantee is downstream from Google’s (and soon Amazon’s) aggressiveness: why build a data center with Nvidia chips if you can buy TPUs or Trainiums (Nvidia chips are likely better, but if the constraint on new data centers is capital, lower up-front prices may matter more than token efficiency)…

…This might not cost Nvidia anything in the end: if AI revenues truly take off, then the debt markets will open back up, and ultimately companies will go back to funding infrastructure investment through free cash flows. Right now, however, is the danger zone, as hyperscalers blow through the debt markets and Google at least starts to tap equity. To the extent Nvidia competes through novel funding mechanisms that, at the end of the day, draw on things like insurance floats and pension funds and other long-run liabilities that are the bread and butter of the asset managers the company is partnering with, the risk — unmarked, unlike equity — is considerably higher.

That’s why I started with 1870 and Cooke’s ill-fated agreement with Northern Pacific. Yes, the upside the deal afforded Cooke was incredible, but it was incredible for a reason: it was very risky, and pioneering new funding mechanisms only served to spread the pain when it all blew up. It’s one thing to spend all of your free cash flow; it’s another thing to tap the debt markets. And, beyond that, it’s a completely new nerve-racking thing to bring safety-seeking assets to bear. AI better deliver before it’s too late.

3. NVIDIA AI Factory Compute Is Becoming an Investable Asset Class – Jensen Huang

Today, we announced partnerships with Apollo, BlackRock, Blackstone, Brookfield, Goldman Sachs and KKR to establish independent financing platforms designed to mobilize over $500 billion of third-party capital to support the buildout of AI infrastructure over time…

…NVIDIA A100 is a powerful example. NVIDIA introduced the Ampere-based A100 in 2020, and six years later, it remains in active commercial use for AI training, fine-tuning, inference and high-performance computing. Customers continue to commit capacity for multi-year deployments, extending A100’s economic life toward a decade.

The market is also demonstrating the durability of NVIDIA compute economics. One-year H100 rental pricing rose from about $1.70 per GPU-hour in October 2025 to about $2.35 per GPU-hour in March 2026. Cross-provider on-demand median pricing rose from roughly $2.00 per GPU-hour in October 2025 to $2.70 in June 2026. Blackwell capacity commands a premium, with reported B200 cloud rates spanning approximately $5.30 to $7.05 per GPU-hour…

…The demand for AI infrastructure is extraordinary. But access to capital is uneven. Many great AI companies, enterprises and AI clouds have demand for compute but do not yet have access to financing at the scale or cost required to build quickly.

That is why we are partnering with the world’s leading long-term capital providers…

…Why would NVIDIA support financing?

In some cases, NVIDIA may provide a residual-value support mechanism for up to 25% of an opportunity, assessed carefully on a project-by-project basis. That support is limited, residual-value based and designed to complement — not replace — independent underwriting.

This is substantially lower than other compute-financing arrangements. NVIDIA can provide support because NVIDIA compute is unique: it is fungible, universally adopted, software-upgradable and redeployable across a large ecosystem of customers…

…Every industrial revolution has been built on infrastructure: electricity, transportation, communications and computing, with every buildout enabled by external financing.

4. The Future is for Everyone – Mark Zuckerberg

We are fortunate to live at an incredible moment in history. In the next few years, people will be able to use superintelligence beyond human capacity to create and discover extraordinary new things, build new businesses, express new ideas, learn new concepts, and advance our health and quality of life. As we get closer to this moment, it is important to develop a philosophy for how we can best use superintelligence to ensure it improves all of our lives, work, communities, freedom and safety.

The defining questions of our age are who will have access to superintelligence and what will we direct it towards. Will it be centralized and restricted to a few institutions, or will it be a tool that empowers everyone?

We propose a philosophy based on individual empowerment as the source of prosperity, invention as the primary purpose of superintelligence, and balance of power as the foundation of safety…

…At the same time, new technologies also bring new risks. There are many important concerns we are focused on addressing — from concerns about job displacement and ensuring local communities benefit from data center builds, to safety concerns around AI misuse related to cybersecurity and biorisk, to avoiding government tyranny and surveillance, ensuring the US and democratic countries lead, and ultimately making sure humanity maintains control over superintelligence so it serves rather than endangers us.

The conventional view is that these concerns are about technology, and that if we take enough time then we can perfect or align the technology to produce a single benevolent superintelligence. I think this view of alignment is fundamentally flawed.

Humanity is not a monoculture. People’s diverse values represent different tradeoffs they would make on important issues. There is no technological solution that can align with everyone’s opposing interests and values at once. Any singular superintelligence would have to prioritize some values over others and in the process would be incapable of being benevolent to everyone.

Instead, we propose that it is more productive to view each of these concerns through the lens of achieving the right balance of power, as western society has in democratic governing institutions. People and institutions with competing interests naturally check and balance each other to lead towards positive outcomes. The best and most realistic path to building a positive AI future is by delivering superintelligence to everyone.

As a thought experiment, imagine only one person had a superintelligent lawyer. They would have an unfair advantage in court — even if they were wrong on the merits. That would lead to a worse society. But now imagine everyone has a superintelligent lawyer. In this case, justice would be carried out much more fairly and efficiently than it is today when there is often an imbalance in skills and resources in litigation.

Similarly, if one person alone had a cybersecurity superintelligence, they could likely break into almost any technical system and the world would be much less secure than today. But if everyone has access to cybersecurity superintelligence, then all of our technical systems would become more secure than today since the widely deployed superintelligence would help harden and update every system.

If only one business had superintelligence, that business would outcompete all others and lead to a less dynamic and broadly prosperous market than we have today. But if everyone has access to superintelligence, then everyone will have the tools to create new things beyond what is possible today, and the economy will be more dynamic and generate more broad-based prosperity.

When people are empowered, they naturally compete and check each other economically, socially, politically, and in all other planes of human interaction. People also check and balance the power of institutions including businesses and governments.

But if the power of superintelligence is held by a small number of individuals, businesses, governments, or AI itself, then that will naturally lead to outcomes that are less favorable for everyone else. This is not a technological principle. It is about the balance of power. There is no such thing as a singular benevolent superintelligence.

Therefore, the key to a positive future for everyone is achieving a balance of power that favors individuals. The solution is to ensure that superintelligence is broadly distributed to empower people…

…Before the industrial revolution, 90% of people were farmers growing food to survive. Advances in technology steadily freed much of humanity to focus less on subsistence and more on the pursuits we choose. At each step, people used our newfound productivity to achieve more than was previously possible, as well as spending more time on creativity, culture, relationships, and enjoying life.

Of course some aspects of the way we work will change — just as it did with computers, the internet, and any new technology. This means people will have to adapt, and this will be challenging. But the more that everyone has a personal agent that is superintelligent at teaching us new skills and helping us adapt to change, the smoother this will be.

Company sizes may shrink — just as they did in the transition from industrial giants to tech companies. But this doesn’t mean fewer jobs overall. It implies a larger number of companies with fewer people each. There are many more valuable companies and services to build than people are able to build today. I expect we will start seeing small numbers of people with personal superintelligence agents able to run companies at significant scale. In the future, small businesses will continue to be the backbone of the economy, but each small business will be able to have a much larger impact…

…For example, in Richland Parish, Louisiana, where Meta is building a large data center, teachers received a $50,000 bonus this year because of the increased tax revenue from our investment. The superintendent told us that teachers are now moving there from across the country and he believes it will become one of the nation’s best school districts…

…We help keep electricity prices low by building our own energy-generating infrastructure wherever we invest. This ensures that not only are we not consuming energy that could have gone to the local communities, but in some cases we even supply a surplus of low-cost energy back to the communities. We think this is an important investment principle for sustainability.

Our data centers are also designed to be among the most water-efficient in the world. We are committed to being water-positive, meaning that we’ll restore more water than we use in the watersheds where we operate by 2030. In areas with high water stress, our goal is to restore 200% of the water we use…

…Some argue that the best way to reduce risk is to restrict the capabilities individuals can access. But giving people cybersecurity capabilities is also how we secure the long tail of systems, and giving people scientific capabilities is how we advance science in ways that should reduce the risks of harm over the long term. Restricting capabilities leads us down the path of centralization and lack of checks and balances, so we should be extremely careful about this — especially if other nations pursue less restrictive paths.

On cybersecurity, widely deployed open source systems have proven more secure because more people can identify vulnerabilities, harden the systems, and easily upgrade to the latest most secure versions. Even in recent weeks, we have seen companies handling security incidents like HuggingFace rely on widely available open models to patch vulnerabilities. Over time, I expect that widely deployed AI models with strong cybersecurity capabilities will lead to systems that are more secure, not less. This will be definitively true once superintelligence enables most of the world’s code to be verifiably secure. The long term answer isn’t to withhold capabilities but to establish a balance of power where superintelligence is broadly distributed.

5. AIndicators Hint at Doubts in Credit Markets – Richard Abbey and John Authers

Investors have awakened to the financial risks of the artificial intelligence buildout, and they have a deep well of precedent to draw on. Like all transformational technologies, it must be financed with borrowed money long before it can generate a return. More than 150 years ago, financier Jay Cooke’s ambitious campaign to fund the Northern Pacific Railroad began to unravel not because railroads were a bad idea, but because the bonds financing it traded at steep discounts for months. His firm’s collapse triggered the Panic of 1873. As Alberto Gallo of Andromeda Capital Management points out, history is full of worthy projects that weren’t worthy investments:

These projects benefited the wider population later on but ended up bringing insufficient financial rewards to their initial capital providers, especially when capital was in the form of credit.

Today’s debt-financed AI buildout isn’t necessarily destined for the same reckoning. But credit markets have a habit of registering doubt before broader markets and should be heeded. Spreads revealed mounting strain long before newspapers declared a financial panic in 1873. They are performing much the same function today. As Barclays shows, main AI players’ credit spreads have risen sharply in recent weeks…

…Spreads aren’t signaling a crisis just yet. Their recent widening nevertheless suggests investors are demanding greater compensation for financing the AI buildout…

…The bottom line is whether people use the product once it’s ready, and what they pay for it. In Field of Dreams terms, the industry is building it; will they come? A Federal Reserve study published in April estimates that AI adoption among US businesses reached 18% by the end of 2025, pointing to substantial room for further expansion. Demand tells a similar story. Data from OpenRouter, which tracks consumption of tokens (the basic units of text processed by large language models), show usage has more than tripled since January…

…Does soaring token usage necessarily translate into soaring revenues for providers of foundational models? Not quite. The LLM Token Expenditure Index, which measures the effective expenditure on large language models by combining token prices and usage, reveals the disconnect. As token volumes surge, falling prices and migration toward cheaper models suggest monetization has not kept pace with adoption…

…JPMorgan estimates that $4.1 trillion of the $5.5 trillion in AI capex will be debt-financed, so rate hikes would essentially pile on the costs of this buildout, putting a spoke in the wheels of the massive investment cycle.

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

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

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

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

Here are the articles for the week ending 09 August 2026:

1. The Disaggregation of Inference Compute – Eugene Ng 

Recurrent Neural Networks (RNNs) process and convert a sequential data input into a specific sequential data output, one step at a time. Because RNNs read one word at a time, training is slow and cannot be parallelized. Worse, as sequences grow long, the learning signal either explodes into instability or vanishes towards zero, and the model increasingly struggles to connect distant words. In short, RNNs are slow, inefficient, and expensive to train, and become increasingly forgetful over distance.

Transformers ended the waiting and unlocked parallelism. Instead of reading one word at a time and passing notes down a slow chain, self-attention lets every word look at every other word at once, and positional encoding keeps track of the order the words came in. Attention decides what matters. Positional encoding preserves order…

…Attention has one non-negotiable rule. Every token must compare itself with every other token. It is how each word figures out which others give it meaning…

…Ten tokens, one hundred comparisons. One thousand tokens, a million. A hundred thousand tokens, ten billion. The work grows with the text squared.

As the context size increases, the attention weight matrix grows quadratically, meaning the number of scores that must be computed and stored grows significantly faster. This leads to higher memory usage and computational cost, making it challenging to scale transformers to very long sequences efficiently…

…Fixes have come in three variations. Compute it smarter. Compute less of it. Or replace it. The first two focus on treating the symptoms. The third goes after the disease…

…The quadratic wall is real, and no fix has removed it cleanly. So, the frontier settled on hybrids for now. Cheap linear layers do most of the work. A few real attention layers stay to catch what the cheap layers miss. The frontier has settled on hybrids, a truce, not a cure.

Because the quadratic problem was never fully solved at the software architectural level, the industry has been forced to keep compensating with more expensive HBM and more GPU FLOPs at the problem, plus incremental software fixes, especially as workloads shift from training to inference and agentic use cases…

…CPUs are built to finish one task fast. GPUs are built to finish many tasks per second. CPUs are great for serial computations, but are architecturally poorly suited for matrix multiplications, which involve numerous parallel calculations.

LLMs are mostly matrix multiplications by arithmetic. NVIDIA did not make GPUs good at matrix multiplication by accident. Matrix multiplication turned out to look exactly like the problem GPUs were already built to solve.

GPUs, which are excellent at parallel computations, have rapidly displaced CPUs in this initial AI infrastructure buildout phase…

…AI LLM workloads started primarily with training, as models are trained, and are increasingly shifting to inference and agentic workloads…

…Inference workloads can be split into prefill and decode.

Prefill (prompt processing) is compute-bound. It processes the entire input prompt in parallel, builds the KV cache for attention, and is dominated by large matrix multiplications.

Decode (token generation) is memory-bound. It generates one output token at a time sequentially and reuses and grows the KV cache on every step autoregressively. It is dominated by memory reads of model weights and KV state. The bottleneck is memory bandwidth.

Each token requires reading the entire model’s weights and the full KV cache from memory, then performing relatively light arithmetic on them. The bottleneck is bandwidth, specifically the latency of getting weights and cache to the compute unit.

The GPU’s FLOPs sit idle waiting for memory. Using a GPU to decode at a batch size of 1 is a Ferrari in a parking lot: expensive silicon and memory doing almost nothing.

Continuous batching can improve throughput by interleaving many independent sequences, but it cannot eliminate the fundamentally low arithmetic intensity of decode, especially for latency-sensitive or single-stream workloads.

Agentic workloads compound this issue further. The industry’s software response is to run both phases on the same chip and optimize for throughput. But the issue remains. One cannot batch your way out of a workload that is inherently sequential. Decode looks like a compute problem but behaves like a memory problem.

AI inference is no longer a single workload that can be served efficiently by a single type of accelerator or memory. AI inference is a dual-stage workload, with very different resource demands for each. The same knife should not be used to cut everything…

…GPUs address this hardware architecture flaw with fast, expensive HBM stacked next to the compute die. Stacking to increase capacity causes thermal, yield, and capacity issues. DRAM chips generate heat, and it is becoming more difficult to remove it effectively…

…There are physical limits to how much thinner DRAM can be shaved and how much higher the shaved layers can be stacked vertically with TSVs (Through-Silicon Vias) and micro-bumps to make HBM.

Yield is also becoming difficult to achieve as layer counts increase. For a given stack yield, total yield decreases as layer count increases…

…Inference compute remains memory-bound on decode and batch size one. While HBM is a lousy memory, unfortunately, it is the best solution we have right now that can address this structural memory wall problem.

In short, HBM is bit inefficient, power inefficient, and bandwidth inefficient. The memory makers likely know it too and recognize that HBM is not the final answer and will not solve the memory wall problem…

…The next best solution is to redesign the hardware to better address the inherent software flaws. One way that is increasingly being adopted by the industry is to disaggregate the inference compute workload of prefill, decode, and orchestration/execution…

…While GPUs have been great for training, they are increasingly poorly suited for inference, especially on decode. If inference hardware becomes increasingly disaggregated into prefill and decode and is even less suited for agentic AI, where workloads are dominated by orchestration, GPUs could become less dominant.

Prefill on compute-dense silicon (i.e., NVIDIA and AMD GPUs, custom ASICs). Decode on bandwidth-dense silicon (i.e., Groq LPU, Cerebras WSE, SambaNova RDU, etc). Then orchestrate both from a CPU (i.e., NVIDIA, Intel, AMD, Graviton CPUs) and carry the KV cache on the fabric.

It makes sense to further disaggregate the AI inference hardware stack. Numerous launches to disaggregate prefill and decode have been announced over the last few months by the majors, including NVIDIA, AWS, Intel, AMD, with Groq, Cerebras, and SambaNova for decode-specialized chips.

2. Inside Google’s $200bn Wall Street finance machine for Anthropic – Ryan McMorrow

Google has assembled one of the largest infrastructure financing programmes in history to supply more than $150bn of artificial intelligence chips to Anthropic…

…To support the relentless surge in demand for the AI chips, Google, Broadcom and Wall Street investors have each taken on different pieces of the financial risk. 

Google guarantees the data centres. Broadcom commits to buying the chips and helps finance them. Apollo and Blackstone provide much of the private-credit capital that purchases the hardware before leasing it to Anthropic…

…In June, the first tranche of TPU hardware passed from Google through Broadcom into this financing blender. A special-purpose vehicle known as Compute SPV paid $35bn for roughly 1GW of the AI hardware, representing around 1mn TPUs, according to people familiar with the matter. 

The SPV’s cash came from three tranches of debt anchored by Apollo and Blackstone. Broadcom, in effect, guaranteed the two senior tranches by agreeing to cover any shortfall if Anthropic stopped paying and the SPV could not sell the hardware for enough to make the senior investors whole.

The arrangement, known as residual value support, covers about $30bn of the $35bn financing, with Broadcom’s exposure declining as Anthropic makes its lease payments…

…Financing the chips solved only half of Google’s problem. The company also needed enough powered data centres to house them. “We have a schedule and we’re looking for capacity that will fit the schedule,” the Google executive said. “Crypto miners with excess capacity were helpful.”

It has helped transform several crypto miners with secured power into a new breed of AI infrastructure developers, with a small outfit called TeraWulf the first to land a Google backstop to add a 360MW data centre on its campus in upstate New York. 

Google guaranteed the lease payments on the Anthropic-bound site, which Morgan Stanley packaged into a construction bond that in October raised $3.2bn to get it built…

…People familiar with the matter said the Big Tech company had so far backstopped 10 developments with 2.4GW of power for TPUs. Google’s guarantees put it on the hook for as much as $44bn if all the leases go bad, though it marks the liability at $815mn on its balance sheet. It could also step into the leases itself.

3. Drug Discovery Has No Magic Wands – Daphne Koller

To understand where AI fits, it helps to decompose drug discovery into its three essential stages:

1. Disease-to-mechanism: Identifying a biological mechanism — a pathway, a target, a molecular interaction — where therapeutic intervention will alter the course of disease in humans.

2. Mechanism-to-drug: Creating a molecular intervention in the right therapeutic modality — a small molecule, antibody, siRNA, gene therapy — that achieves the desired mechanistic effect with acceptable safety and pharmacological properties.

3. Drug-to-patient: Designing a clinical development program that identifies the right patients and assesses the molecule’s effects — beneficial as well as adverse.

The vast majority of AI work in drug discovery has focused on stage 2…

…More than 90% of drugs that enter clinical trials fail — a dismal statistic that has barely improved in several decades. In the large majority of cases, the molecule was engineered just fine. The mechanism it targeted was wrong. We are doing a pretty good job at manufacturing keys, but they are generally for the wrong locks. Even if AI lets us make better keys at an accelerating pace, that won’t improve our ability to identify the right locks. The real bottleneck in making a novel medicine is disease understanding: identifying a biological mechanism whose modification actually changes the course of disease in patients. That, far more than molecular design, is where drug discovery succeeds or fails.

This mechanistic understanding is a rare commodity. And because no-one likes to fail in the clinic, we are seeing industry trends that are truly destructive. There are currently 38 targets that have over 50 programs against each of them — slightly better keys for those few locks where we have strong conviction. How many variants of GLP-1 do we really need? Even worse than this misallocation of capital is the disservice to patients: the number of novel targets the industry advances each year fell from ~100 in 2015 to about 30 in 2024. That collapse is the far bigger cost: the inability to help the hundreds of millions of people for whom medicine currently offers nothing…

…The challenge is that human biology is incredibly complex, spanning multiple interconnected biological layers — DNA, protein, cells, multi-cellular environments, entire organisms. Individual components respond dynamically to even subtle changes in related components or in the environment. Moreover, biology wasn’t engineered; it is the result of billions of years of messy, stochastic evolution, which produced staggering variation — countless genes, cell types, states, and contexts, each behaving in its own way. There is too much of it, too idiosyncratic, to reason about in the abstract. You have to measure it…

…Which brings up the greatest data challenge. While some processes are conserved across all forms of life, others are far more specific. The folding of a single protein is a self-contained process, highly conserved — closer to physics than to biology; this allows protein folding models to be trained on sequences collected across thousands of species. Metabolism involves at least a dozen distinct cell types and might be conserved across mammals. Brain function and dysfunction involves dozens of distinct cellular identities; and these processes are exquisitely specialized to humans: rodents do not get Alzheimer’s disease; non-human primates do not recapitulate ALS. The diseases where we have made the least progress tend to be precisely those that are most human-specific, and therefore those for which the data is most expensive to collect, least available, and most fraught with ethical constraints…

…But agentic iterated optimization relies on a fundamental attribute: agents thrive when there is a fast, accurate, and cheap scorecard to evaluate progress. If you give a sufficiently smart model an instant feedback loop, it will grind against that benchmark until it wins. This is why coding assistants and molecular design tools advanced so rapidly — the feedback is cheap, accurate, and fast…

…Drug development is the exact opposite. The ultimate scorecard — whether a drug actually provides therapeutic benefit to a patient — cannot be captured well by computational models or high-throughput assays. The only true ground truth is a human clinical trial. This feedback loop currently takes years, costs millions, and is strictly bound by human ethics and living biology. It is the ultimate slow feedback loop, and no amount of compute or process optimization can change this…

…Some have argued that the most important AI unlock in drug discovery is in the third stage — drug-to-patient — taking a drug candidate through preclinical testing and clinical trials. This is the fourth AI Magic Wand: reduce the time and cost of this very expensive phase, and drug discovery becomes faster and cheaper. Sadly, if you accelerate a pipeline full of drugs aimed at the wrong mechanisms, all you get is faster failures…

… An AI-enabled, deep mechanistic understanding of a disease enables the identification of novel clinical readouts that serve three distinct purposes: selecting the patients most likely to respond, confirming that the drug is hitting its intended target, and detecting early and reliable signals that it is actually modifying disease biology. Together, these allow trials to enroll the right patients, read out faster, and catch failures earlier — changes that transcend clinical trial operations, transforming the trial design itself. This capability is inseparable from solving the disease-understanding problem; they are one and the same. Better trials, in the end, are downstream of better biology…

…To fulfill the promise of AI for the millions of patients lacking any meaningful treatment, we must direct our efforts toward the problem that really matters: the identification of biological mechanisms with disease-transforming clinical benefit. This is arguably the hardest problem in drug discovery, because the only conclusive test of whether we have correctly identified a novel biological mechanism is a human clinical trial. There are multiple other paths in this space with shorter timelines and clearer near-term proof points. Those paths are shorter because the problems are more tractable: the feedback loops are faster and the benchmarks are cleaner. But a shorter path to a smaller destination is still a smaller destination — process improvements for problems we already know how to solve.

4. The 1970s: Warren Buffett’s Defining Decade – Dirtcheapstocks

In the early 1970’s the Nifty Fifty were all the rage. Investors would seemingly pay any multiple for blue chip growth stocks (funny how history repeats itself).

Polaroid was selling for 91x earnings.

McDonald’s sold for 86x earnings…

…Then the music stopped.

The market was down nearly 50% from its highs in 1972.

That’s when Buffett got busy buying…

…Buffett’s most important purchase in the early part of the 1970’s was Blue Chip Stamps as the zero cost float provided leverage for other investments. He added to his position throughout the decade. Some of the prices paid were absurdly low…

…Buffett bought O&M throughout 1973 and 1974.

His basis valued the business at $29mm.

Ogilvy’s enterprise value was roughly the same as its market cap. So, Buffett was buying the shares at ~3x EBIT.

O&M grew its operating profit at a 23% CAGR from 1970 to 1974.

Buffett’s shares doubled in value within 2 years of his purchase…

…Interpublic fell 73% from its 1972 highs when Buffett began buying.

Buffett’s basis valued the business at a $25mm market cap. The enterprise value was only $19mm. Interpublic earned $14.8mm of operating income in 1973.

Buffett’s investment was up 10x in 10 years!..

…Berkshire’s book value compounded at a 30% CAGR from 1973 to 1985…

…The 1970’s made Buffett, but he was also perfectly prepared for the opportunity.

He didn’t stretch to buy businesses at lofty valuations. He waited for the prices to come to him.

This was a difficult time for American business, but it was hardly unprecedented. I believe there is a reasonable probability that businesses get this cheap again.

5. Ways to think about token pricing – Benedict Evans

There are only two things you can say with certainty about token prices: we’re in a supply crunch, and this is unstable. All of the variables are in play, and the market will get shaken out over the next few years to arrive at a new equilibrium. Right now we have a lot of frantic analysis of ‘time to power’, but the question at the end of that remains whether the foundation models have sustainable pricing power, strategic leverage and value capture, or whether they become low-margin commodity infrastructure providers. At the moment, I think every dynamic we can see points to the latter…

…First, how many people will pay to be at the top right of the curve – to be at the frontier? At one extreme there are already use cases that already work just fine with a small, old, perhaps open source model that runs for ‘free’ on-prem or on your phone; at the other extreme there will be some that get better results from the latest, most expensive frontier model, consuming lots of tokens for lots of money; and then there will be many that are somewhere in between…

…Second, does the frontier keep moving significantly? This is obviously the most basic science question in AI: how long does the frontier keep getting better, how long does that keep needing more and more compute, and does that continue to happen at a rate that keeps it ahead of downward pricing pressure from efficiency and capacity gains?…

…Third, will there still be fierce competition between frontier models? Does the field shrink to fewer and fewer frontier models, perhaps with network effects emerging? Do frontier models diverge, with different models having much clearer leads in different fields? That could be another path to sustainable pricing power…

…Fourth, how much of the value from those high-end use cases is captured by the frontier model itself? How much needs to be wrapped in tooling, process, proprietary data, go-to-market, networks, support, and everything else associated with a traditional software company, even if you do need the big expensive frontier model underneath? Can that model do the whole thing, or is the model, no matter how good, still a piece of infrastructure that you use to make the actual product?…

…Meanwhile, there is structural uncertainty at the early stages of every big new technology, but the uncertainty now is different, because we don’t have a good theoretical understanding of why these models work so well and so we don’t know how much better they can get. In 1995, we didn’t know how the internet would evolve but we knew that there were less than 100m PCs on earth (and they were expensive) and that telcos couldn’t give everyone FTTH next year; in 2010 we didn’t know what the next iPhone would be but we knew it wouldn’t have retinal projection. We knew the physical limits in ways we don’t really know with LLMs. Next month a new approach could cut inference compute needs by 90%, or double demand, or both…

…That makes mobile data a more fruitful comparison here. Mobile networks have marginal cost for capacity, and like AI they had an enormous surge in usage 15 years ago, that overwhelmed capacity and had carriers scrambling to add capacity and rebalance their pricing. Meanwhile, selling bits looks superficially similar to selling tokens: it’s an opaque measure of marginal cost that doesn’t map in any transparent or intuitive way to use cases or value, and needs to be replaced with bundles of some kind. But most importantly, in the last 20 years cellular data traffic has risen by several orders of magnitude, and this has become an enormous industry, with annual revenue of a trillion dollars and capex of $200 billion, but the stocks have gone nowhere, and all the value was captured by other people further up the stack. This, of course, is one of the core questions for AI: is this going to be low-margin commodity infrastructure with all the value captured by other people further up the stack?…

…However, these examples do tell us, empirically, that something can be very important, very expensive, change the world, and be full of very sophisticated science and engineering, and yet have a wide range of possible outcomes. There isn’t one inevitable path here: you can have price equilibrium at high margins and at low margins, and with and without market concentration, and you can’t hand-wave that away by talking about AGI and saying “you don’t understand exponentials!”

However, if one thread in everything I’ve written above is how much we don’t yet know, the other thread is that every path to foundation models having market dominance, strategic leverage, value capture, winner-takes-all effects, or anything else other than becoming commodity infrastructure, requires something to change.

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