What We’re Reading (Week Ending 08 March 2026) - 08 Mar 2026
Reading helps us learn about the world and it is a really important aspect of investing. The late Charlie Munger even went so far as to say that “I don’t think you can get to be a really good investor over a broad range without doing a massive amount of reading.” We (the co-founders of Compounder Fund) read widely across a range of topics, including investing, business, technology, and the world in general. We want to regularly share the best articles we’ve come across recently. Here they are (for the week ending 08 March 2026):
1.Iran: The Day After – Tomas Pueyo
Persia’s Shah used to be aligned with the West: He modernized the country, invited foreign investments, built a lot of infrastructure, improved literacy and healthcare…
The radical Islamists didn’t like this modernization, so they allied with the local Left to gain power, and succeeded in 1979.1 This means the entire legitimacy of the regime is based on opposing the US, its allies, and its values.
This would not have been a problem if Iran had limited itself to hating the US and Israel. Instead, they’ve threatened to attack and eliminate them for the last 47 years, and they haven’t limited themselves to empty threats. They’ve developed ballistic missile and nuclear weapon programs to be able to obliterate Israel, and maybe attack the US too.
For the last few decades, the US and Israel have tried to manage the situation, but the closer Iran is to getting nuclear weapons, the less they can tolerate it. Until recently, they were forced to because Iran was quite strong, with proxies in Palestine, Lebanon, Syria, Iraq, and Yemen. But after October 7th 2023, Israel has systematically eliminated most of them, so it and the US saw an opening last year to weaken Iran and its nuclear program, and took it. But that was just a delay. The truth is they will only be safe when this regime falls.
The problem is that achieving regime change is going to be very difficult…
…The recent strikes have killed the existing Supreme Leader, but there’s a long chain of command to replace him and any other leader killed through strikes. Then, there’s Khamenei’s Bayt, a group of 4,000 close employees who manage Khamenei’s affairs and power, and work as a shadow government mirroring the official one…
…Through this body, Khamenei controlled the BMEE and AQR2, huge conglomerates of over 200 companies with interests in real estate, construction, industry, mining, energy, power, food, agriculture, tourism, transportation, IT, media…
Khamenei’s Bayt was also able to infiltrate the military and the IRGC (Islamic Revolutionary Guard Corps), a kind of Praetorian Guard with over 125,000 members sourced from the Basij militia, a bigger group of ~400,000 poor, Shia radical volunteers (and 25 million members!!) who police the country on behalf of the government…
…45% of the Iranian government’s income comes from oil.3 If the US and Israel prevent Iran from selling its oil, its income will dry up, and it won’t be able to pay salaries. My guess is the Iranian regime will prioritize IRGC, Basij, and military salaries, but even then, losing 50% of your income can’t be easy. Unfortunately, this takes some time to bite, as the government will use other resources to pay its forces for as long as possible, and people can sometimes withstand some time without a salary…
…The vast majority of Iranians are tired of their government.
They are now celebrating the bombings on the streets.
The first consequence is oil. Iran has closed the Strait of Hormuz, many oil pumping stations and refineries have been hit in the area, and oil has stopped flowing. This will put pressure across the world too as oil prices increase…
…Saudi Arabia can ramp up supply, and employ an east-west pipeline that should be able to bypass the strait. It won’t be enough to counter the entire drop in supply, but it might end up benefiting Saudi Arabia through higher oil prices.
Meanwhile, the biggest consumer of Iranian oil is China, but it has historically high oil and gas reserves, so it might be able to withstand the war if it’s short enough…
…Four years ago, China had collected anti-US friends in Russia, Iran and its proxies in Syria, Lebanon, Hamas and the Houthis, Venezuela, Cuba, and a host of satellites considering whether to join them or not. Israel took care of Iran’s acolytes. The US neutralized Venezuela, Cuba is isolated and cut off from oil, Russia is bogged down in Ukraine, and Iran is at risk of falling. Virtually every friend that China has cultivated over the last few years is crumbling.
Not only that, but China’s standing as a provider of technology and military power is completely exposed. If China won’t come to the rescue of its allies, and its weapons can’t stop the US, who will want to side with them?
Then there’s the oil. Venezuela and Iran together accounted for 17% of China’s oil imports.
This is a bad day for China…
…Iran has 90M people, nearly twice South Korea’s population. 42% of them are under 25, and they have a 98% literacy rate. The country birthed one of the oldest civilizations on Earth, the first empire, and has seen a succession of successful ones through the ages. Its diaspora in the world—especially in the US—is educated, rich, and powerful. It could fund and provide the leadership for a renaissance in the country.
But only if the current regime falls.
2. A Munger PA Investment – Joe Raymond
The Alfred C. Munger Foundation (named for Charlie’s father) sold 10,000 shares of Black Hills Corporation (BKH) for $23 each in June 2009, resulting in a short-term gain of 29%…
…A reasonable assumption based on this filing is that Charlie purchased this specific lot of 10,000 shares for $18 apiece in early 2009 and sold in June 2009 around $23.
He could have been buying the stock before that and holding shares after.
The only thing we know with reasonable certainty is that Charlie thought Black Hills was a good buy in 2009 at $18 per share…
…Black Hills is a utility company based in South Dakota.
It was formed in 1941 through a combination of several existing utility companies serving the Black Hills region. The earliest predecessor traces its roots back to 1883…
…Black Hills could be described as a decent and predictable business in the years leading up to Charlie’s purchase. ROE was in the low double digits and book value per share growth (adding back dividends) averaged 11% from 2002 to 2008.
Simple, clean, predictable, decent quality…
…Black Hills earned $105 million in 2008 ($2.75 per share). It paid $1.40 of dividends that year and finished the year with $27.19 of per share book value…
…I think the thesis here was pretty simple.
A durable, safe business that earns double digits on equity shouldn’t trade for 66% of book value.
The crashing economy wasn’t going to kill the utility business. People still needed to turn their lights on and fire up the stove…
…BKH’s average price three years later in 2012 was $33.66 per share, good for a return of 98% (25% CAGR) before dividends…
…The Black Hills case isn’t terribly exciting, but I do find it interesting and useful.
If I had to nail it down to one simple idea it would be this:
Buying an adequately capitalized business, that should earn at least a high-single-digit return on its common equity, at a substantial discount to book value often works very well over short- and medium-term time frames.
3. Anthropic’s AI tool Claude central to U.S. campaign in Iran, amid a bitter feud – Tara Copp, Elizabeth Dwoskin, and Ian Duncan
As planning for a potential strike in Iran was underway, Maven, powered by Claude, suggested hundreds of targets, issued precise location coordinates, and prioritized those targets according to importance, said two of the people. The pairing of Maven and Claude has created a tool that is speeding the pace of the campaign, reducing Iran’s ability to counterstrike and turning weeks-long battle planning into real-time operations, said one of the people. The AI tools also evaluate a strike after it is initiated, the person said.
Claude has also been used in countering terror plots and in the raid that captured Venezuelan president Nicolás Maduro. But this is the first time it has been used in major war operations, according to two of the people…
…“It is notable that we’re already at the point where AI has gone from hypothetical to supporting real-world operations being conducted today,” said Paul Scharre, executive vice president at the Center for a New American Security, and who has written about AI in warfare. “The key paradigm shift is that AI enables the U.S. military to develop targeting packages at machine speed rather than human speed.”
The downsides, he said, are “AI gets it wrong. … We need humans to check the output of generative AI when the stakes are life and death.”
The Pentagon began to integrate Anthropic’s Claude chatbot into Maven in late 2024, according to public announcements. The system has been used to generate proposed targets, to track logistics and provide summaries of intelligence coming in from the field. The Trump administration has vastly expanded the use of Maven into many other parts of the military, with over 20,000 military personnel using it as of last May…
…Ben Van Roo, the CEO and cofounder of Legion Intelligence, a defense software startup, said that in his work over the last two and half years integrating generative AI into software systems at the Department of War, “the baseline use case is chat and advanced search functions — essentially summarizing information.”
It’s not highly integrated into weapons or mission critical systems, he said. He said that he wasn’t aware of its use in Iran, but wondered how it built on existing software that is already able to prioritize targets.
4. The Coase Conjecture in AI Inference Markets – Soren Larson
In 1972 Coase posed a simple question: If a monopolist owns all the land––assumed to be homogenous in kind and quality––in the world, at what price does he sell it?…
…Coase’s argument is interesting and simple. Normally a monopolist would set quantity sold where marginal revenue equals marginal cost. For convenience, let’s say marginal cost is zero.
Once the monopolist land owner has sold a bit of land, he sees the remaining land is also still available, but not monetized. Maybe he should sell a bit more––it’d generate pure profit! To do that, however, he’d have to lower the price to meet demand at the price it’s willing to pay.
Doing this annoys the original buyers.
The land is now worth less than what they paid. Eventually, however, the market catches on. Candidate buyers know the monopolist can’t resist selling more land (marginal cost of selling is zero!) and so they wait.
While the monopolist technically has no competitors, he ends up with one he didn’t expect––his future self. In situations like this, the market can guess a monopolist’s future behavior, so it holds out waiting for the “future self” monopolist to depress his own prices…
…At first glance, Coase seems to apply directly: the monopolist can’t resist selling more inference, buyers anticipate this, and prices unravel.
At first glance it could appear that Coase implies that frontier labs can’t sustain monopoly prices because they can’t resist selling more and more inference at what end up being lower prices.
This, of course, is incomplete in that every inference customer can choose to buy inference from cheaper open source models. It turns out the existence of open-source alternatives protects the monopolist’s pricing power by giving customers a reason to exit the frontier market rather than wait for discounts…
…In cases where buyers have an Outside Option––where they can defect from the monopolist’s market and buy some alternative––the Coasian monopolist unraveling doesn’t happen. The monopolist can sustain the monopoly price indefinitely.
Empirically, this appears to be happening in the inference market…
…Effectively, the outside option is a self-selection device that relieves the monopolist from price-sensitive waiters who’d pressure prices downward over time. The monopolist loses some customers but gets to keep pricing power. This is broadly what we see today…
…There are clear extensions to this setting in inference markets. Suppose you’re considering developing new software using AI: for you, waiting for Anthropic to lower prices could prove costly. A competitor who pays full price today could lock in customers before you enter the market. This dynamic is likely what explains today’s inference market structure: buyers would prefer to pay full price or defect to Minimax M2.5 or GLM 4.7 today than wait and let competitors eat their lunch.
The other extension, of course, is that Outside Options keep getting better. Open source models are improving every quarter: A buyer who defects today to a mediocre alternative might have waited for a better one in a quarter––returning us to the original Coase setting…
…Suppose now that the monopolist wins on all counts: open source improvement is slow enough that buyers don’t bother to wait. Open-source capability might even plateau. The Board and Pycia result holds and the monopolist charges its optimal price at equilibrium.
Is our beloved monopolist now safe?
So far we’ve only discussed pricing power, but what about market capture? Even if the monopolist preserves its pricing power, it could be that so much of the market defects to the Outside Option that pricing power is practically irrelevant.
Consider the buyer’s problem. The inference buyer only pays the monopolist pricing premium if the frontier model offers enough additional value over the open source alternative to justify the price. When open source closes the gap it reduces the collection of buyers for whom the frontier premium is worth the price. These two dynamics compound: a shrinking price corresponding to a lower marginal benefit of frontier v Outside Option mixed with a shrinking customer base means the monopolist’s total revenue erodes faster than the capability gap closes.
Of course, this argument depends on inference buyers actually connecting their buying decisions to value actually delivered.
The market may not be doing this today––many preferring to build Tool Shaped Objects. In fairness, model capabilities are jagged and it’s a reasonable strategy for firms to keep buying frontier, irrespective of underlying value proposition while the technology matures. On the other hand, as the technology matures and firms begin to connect their inference consumption to value delivered, demand shifts from “just buy the best” to “maximize margins” or “buy what’s worth paying for.” In this world, the monopolist’s value proposition reduces to its incremental value over the Outside Option. And that shrinks even as open-source improves…
…Board and Pycia explain why margins are high: outside options remove the price sensitivity of buyers. High margins are an artifact of the Coase selection mechanism, not evidence of a durable business.
The labs clearly can and are charging high margins today. That’s not the question. It’s whether they will be charging high margins in three years.
If open source keeps closing the gap, the answer from Board and Pycia––and from Ronald Coase––is probably not.
5. Biggest AI Prediction & Why I’m Allocating $200,000 to it – ContraTurtle
I categorize the AI stack into six levels:
- Level Zero: Energy (GE Vernova, Cameco Corp, Constellation Energy, etc.)
- Level One: Chips (TSMC, Nvidia, AMD, ASML, Broadcom, etc.)
- Level Two: Infrastructure & Data Centre (Equinix, Arista Networks, Vertiv, Amazon, Google, Microsoft, etc.)
- Level Three: AI Foundation Model Companies (OpenAI, Anthropic, Google DeepMind, Mistral, etc.)
- Level Four: AI Software Infrastructure (Amazon Web Services, Google Cloud Services, Microsoft Azure, Palantir, Snowflake, Databricks, etc.) – Enterprise platforms enabling AI deployment, orchestration, and data pipelines
- Level Five: AI Applications, Apps and Services (Meta, Google, Microsoft, Amazon, ServiceNow, Shopify, Axon, Netflix, etc.) – Companies delivering end user value and capturing economic surplus from AI optimisation
I will be focusing on Level Five in this article because this is where economic validation happens.
You can have:
- The most advanced GPUs
- The cheapest energy
- The largest data centres
- The most powerful foundation models
None of it matters if end users do not generate ROI that justifies capex deployed upstream.
Level 5 determines whether the entire AI stack earns an adequate return on capital.
Over the long term, the bulk of economic surplus accrues to the layer closest to the customer. Historically in technology cycles, infrastructure enables value creation, but applications capture pricing power.
This layer is still early…
…But there is one use case where AI ROI is already direct, measurable, and immediate and that is – Advertising.
Let me explain.
Ads share two structural traits with coding (a use case that has shown the most promise in enterprise):
- Low cost of failure with hallucination, yet provide high ROI
- Built-in verification mechanisms
In coding, hallucinated outputs are caught through testing frameworks. Unit tests, integration tests, and runtime checks validate whether the generated code works. If it fails, it does not ship.
Advertising works similarly.
An advertiser can generate five variations of an AI-created image, headline, or video and deploy them simultaneously. Performance is verified empirically through A/B testing across metrics such as:
- Click-through rate
- Conversion rate
- Return on ad spend
Poor-performing creatives are automatically filtered out by the market. Strong performers scale.
Advertising is therefore a near-perfect commercial application of probabilistic AI.
Disclaimer: None of the information or analysis presented is intended to form the basis for any offer or recommendation. We currently have a vested interest in Alphabet (parent of Google), Amazon, ASML, Meta Platforms, Microsoft, Netflix, Shopify, and TSMC. Holdings are subject to change at any time.
