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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