What We’re Reading (Week Ending 18 January 2026) - 18 Jan 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 18 January 2026):
1. “The Compute Theory of Everything” – Abdullah Al-Rezwan
Albanie referred two seminal essays by Hans Moravec: “The Role of Raw Power in Intelligence” (1976), and “When will computer hardware match the human brain?” (1998)
I glanced through the first essay, but read the second one. I was moved just by reading the abstract of the paper:
“This paper describes how the performance of AI machines tends to improve at the same pace that AI researchers get access to faster hardware. The processing power and memory capacity necessary to match general intellectual performance of the human brain are estimated. Based on extrapolation of past trends and on examination of technologies under development, it is predicted that the required hardware will be available in cheap machines in the 2020s.”…
…Despite acknowledging valid reasons to harbor skepticism, Moravec relied on his simple observations on computing:
“Computers doubled in capacity every two years after the war, a pace that became an industry given: companies that wished to grow sought to exceed it, companies that failed to keep up lost business. In the 1980s the doubling time contracted to 18 months, and computer performance in the late 1990s seems to be doubling every 12 months…
…At the present rate, computers suitable for humanlike robots will appear in the 2020s. Can the pace be sustained for another three decades? The graph shows no sign of abatement. If anything, it hints that further contractions in time scale are in store. But, one often encounters thoughtful articles by knowledgeable people in the semiconductor industry giving detailed reasons why the decades of phenomenal growth must soon come to an end.”
2. Venezuelan Historical Primer: Friend, Foe, Vassal – Collapse Intelligence Agency
Before the US Shale Revolution (Fracking) in ~2010, the consensus view among energy majors was that US domestic light sweet oil was dying.
It was thought the world had burned all the easy, high-quality oil. Future reserves were geographically concentrated in the Middle East or were “Trash Grade” (Canadian Bitumen, Venezuelan Extra-Heavy, Mexican Maya).
US Refiners (Valero, Chevron, LyondellBasell) decided that to stay profitable, they had to spend billions upgrading their facilities to process the “Trash Grade” oil that nobody else wanted. They built massive Delayed Cokers and Hydrocrackers.
By building machines that could eat $10/barrel sludge and turn it into $50/barrel gasoline, they guaranteed massive margins that simple refineries in Europe couldn’t touch…
…Meanwhile, Gulf Coast refiners weren’t building just for Venezuela; they were building for the neighborhood.
In the 90s, Mexico’s massive Cantarell Field was pumping huge volumes of “Maya” crude (heavy/sour)
Venezuela had Orinoco (extra heavy/sour).
The logic was that the Gulf of Mexico basin was destined to be the global hub for processing heavy oil. Refiners poured tens of billions of dollars into capital expenditures (CapEx) to optimize specifically for this metallurgic sludge…
…When fracking exploded in 2010, the US flooded the market with Light Sweet Crude (LTO).
The US refiners looked at all this light oil and realized, “We can’t use it efficiently.”
If you put Light Oil into a refinery built for Heavy Sludge, you run the equipment inefficiently. You under-utilize the coker units (billions in wasted sunk costs).
The US exports its own high-quality light oil to Asia/Europe (who have simple refineries) and must import heavy oil to satisfy the diet of the Gulf Coast processing complex.
The capacity exists because Venezuela effectively paid to build it (via Citgo) and US executives in the 90s bet the house that heavy oil was the only game in town. Formerly permissive national economic policies supercharged the technological development.
The recent US military operation isn’t just about seizing new resources; it’s about feeding a starving industrial monster that was specifically designed to eat only what Venezuela produces. And that industrial monster must feed the US economy because now the shale party is about to end. The US administration knows this. They have made a 100% rational decision to force a bloody showdown with Venezuela to fund US energy needs.
3. The AI revolution is here. Will the economy survive the transition? – Michael Burry, Dwarkesh Patel, Patrick McKenzie, and Jack Clark
Jack: Yes, something we say often to policymakers at Anthropic is “This is the worst it will ever be!” and it’s really hard to convey to them just how important that ends up being. The other thing which is unintuitive is how quickly capabilities improve—one current example is how many people are currently playing with Opus 4.5 in Claude Code and saying some variation of “Wow, this stuff is so much better than it was before.” If you last played with LLMs in November, you’re now wildly miscalibrated about the frontier…
…Dwarkesh: The million-dollar question is whether the METR productivity study (which shows that developers working in codebases they understood well had a roughly 20% decrease on merging pull requests from coding tools) or human equivalent time horizons of self-contained coding tasks (which are already in the many-hours range and doubling every four to seven months) is a better measure of how much speedup researchers and engineers at labs are actually getting. I don’t have direct experience here, but I’d guess it’s closer to the former, given that there isn’t a great feedback verification loop and the criteria are open-ended (maintainability, taste, etc.).
Jack: Agreed, this is a crucial question—and the data is conflicting and sparse. For example, we did a survey of developers at Anthropic and saw a self-reported 50% productivity boost from the 60% of those surveyed who used Claude in their work. But then things like the METR study would seem to contradict that. We need better data and, specifically, instrumentation for developers inside and outside the AI labs to see what is going on. To zoom out a bit, the massive and unprecedented uptake of coding tools does suggest people are seeing some major subjective benefit from using them—it would be very unintuitive if an increasing percentage of developers were enthusiastically making themselves less productive…
…Michael: Do you think the podium will keep rotating? From what I’m hearing, Google is winning among developers from both AWS and Microsoft. And it seems the “search inertia” has been purged at the company.
Dwarkesh: Interesting. Seems more competitive than ever to me. The Twitter vibes are great for both Opus 4.5 and Gemini 3.5 Pro. No opinion on which company will win, but it definitely doesn’t seem settled.
Jack: Seems more competitive than ever to me, also!…
…Jack: Coding has a nice property of being relatively “closed loop”—you use an LLM to generate or tweak code, which you then validate and push into production. It really took the arrival of a broader set of tools for LLMs to take on this “closed loop” property in domains outside of coding—for instance, the creation of web search capabilities and the arrival of stuff like Model Context Protocol (MCP) connectivity has allowed LLMs to massively expand their “closed loop” utility beyond coding.
As an example, I’ve been doing research on the cost curves of various things recently (e.g. dollars of mass to orbit, or dollars per watt from solar), and it’s the kind of thing you could research with LLMs prior to these tools, but it had immense amounts of friction and forced you to go back and forth between the LLM and everything else. Now that friction has been taken away, you’re seeing greater uptake. Therefore, I expect we’re about to see what happened to coders happen to knowledge workers more broadly—and this feels like it should show up in a diffuse but broad way across areas like science research, the law, academia, consultancy, and other domains.
Michael: At the end of the day, AI has to be purchased by someone. Someone out there pays for a good or service. That is GDP. And that spending grows at GDP rates, 2% to 4%—with perhaps some uplift for companies with pricing power, which doesn’t seem likely in the future of AI.
Economies don’t have magically expanding pies. They have arithmetically constrained pies. Nothing fancy. The entire software pie—SaaS software running all kinds of corporate and creative functions—is less than $1 trillion. This is why I keep coming back to the infrastructure-to-application ratio—Nvidia selling $400 billion of chips for less than $100 billion in end-user AI product revenue.
AI has to grow productivity and create new categories of spending that don’t cannibalize other categories. This is all very hard to do. Will AI grow productivity enough? That is debatable. The capital expenditure spending cycle is faith-based and FOMO-based. No one is pointing to numbers that work. Yet.
There is a much simpler narrative out there that AI will make everything so much better that spending will explode. It is more likely to take spending in. If AI replaces a $500 seat license with a $50 one, that is great for productivity but is deflationary for productivity spend. And that productivity gained is likely to be shared by all competitors…
…Michael: At some point, this spending on the AI buildout has to have a return on investment higher than the cost of that investment, or there is just no economic value added. If a company is bigger because it borrowed a lot more or spent all its cash flow on something low-return, that is not an attractive quality to an investor, and the multiple will fall. There are many non-tech companies printing cash with no real prospects for growth beyond buying it, and they trade at about 8x earnings…
…Michael: Well, value accrues, historically, in all industries, to those with a durable competitive advantage manifesting as either pricing power or an untouchable cost or distribution advantage.
It is not clear that the spending here will lead to that.
Warren Buffett owned a department store in the late 1960s. When the department store across the street put an escalator in, he had to, too. In the end, neither benefited from that expensive project. No durable margin improvement or cost improvement, and both were in the same exact spot. That is how most AI implementation will play out.
This is why trillions of dollars of spending with no clear path to utilization by the real economy is so concerning. Most will not benefit, because their competitors will benefit to the same extent, and neither will have a competitive advantage because of it.
I think the market is most wrong about the two poster children for AI: Nvidia and Palantir. These are two of the luckiest companies. They adapted well, but they are lucky because when this all started, neither had designed a product for AI. But they are getting used as such.
Nvidia’s advantage is not durable. SLMs and ASICs are the future for most use cases in AI. They will be backward-compatible with CUDA [Nvidia’s parallel computing platform and programming model] if at all necessary. Nvidia is the power-hungry, dirty solution holding the fort until the competition comes in with a completely different approach…
…Jack: The main thing I worry about is whether people succeed at “building AI that builds AI”—fully closing the loop on AI R&D (sometimes called recursively self-improving AI). To be clear, I assign essentially zero likelihood to there being recursively self-improving AI systems on the planet in January 2026, but we do see extremely early signs of AI getting better at doing components of AI research, ranging from kernel development to autonomously fine-tuning open-weight models…
…Michael: If I had the ear of senior policymakers, I would ask them to take a trillion dollars (since trillions just get thrown around like millions now) and bypass all the protests and regulations and dot the whole country with small nuclear reactors, while also building a brand-new, state-of-the-art grid for everyone. Do this as soon as possible and secure it all from attack with the latest physical and cybersecurity; maybe even create a special Nuclear Defense Force that protects each facility, funded federally.
This is the only hope of getting enough power to keep up with China, and it is the only hope we have as a country to grow enough to ultimately pay off our debt and guarantee long-term security, by not letting power be a limiting factor on our innovation.
4. Is Venezuela’s Oil Worth the Hassle? – Tomas Pueyo
This depends on how much oil can be extracted from Venezuela. Today, it’s ~1.1M barrels per day.
A barrel of oil is currently worth about $60:
But Venezuela’s oil is worse quality than most, so it sells for cheaper, ~$8 less as of today, or $52…
…But how much does it cost to extract a barrel of Orinoco oil and transport it and treat it to be sellable?
So of these $52, about $23 are hard costs, and each barrel yields around $29 in profit…
…The oil [in the Orinoco Valley] is extremely dense (heavier than water), extremely viscous (like pitch or molasses) and extremely dirty (over 5% sulfur and masses of metals like vanadium). The only deposit like this elsewhere in the world is Canada’s Athabasca oil sands.
To extract the oil, you have to first pump large amounts of steam into the formation, to melt the hydrocarbons, then use electrical pumps at the surface or in the bottom of the well, up to a kilometer deep, to lift it to the surface. Once there, the “oil” is far too viscous to transport by pipeline or ship, and far too heavy and dirty for most refineries to tackle. So it is diluted by mixing with a much lighter crude oil, or the “condensate” liquids from a gas field, or refined naphtha (a solvent which you can buy as “white spirit” in UK DIY stores). The resulting diluted crude oil (DCO) is exported as Merey blend. This is still one of the heaviest, dirtiest crude oils in the world (16 API, 3.5% sulfur, high acidity and metals content), but it flows just well enough to be transported if kept warm, and some of the world’s more complex refineries can handle it, and make transport fuels from it, although usually alongside other lighter crudes…
…The two best estimates suggest it would take tens of billions to maintain the existing infrastructure, and tens of billions more to go beyond that.
5. A Few Things I’m Pretty Sure About – Morgan Housel
I think the majority of society problems are all downstream of housing affordability. The median age of first-time homebuyers went from 29 in 1981 to 40 today. But the shock this causes is so much deeper than housing. When young people are shut out of the life-defining step of having their own place, they’re less likely to get married, less likely to have kids, have worse mental health, and – my theory – more likely to have extreme political views, because when you don’t feel financially invested in your community you’re less likely to care about the consequences of bad policy…
…There’s a long history of Americans cycling through how they feel about government and how politicians treat each other.
The 1930s were unbelievably vicious. There was a well organized plot to overthrow Franklin Roosevelt and replace him with a Marine general named Smedley Butler, who would effectively become dictator. The Great Depression made Americans lose so much faith in government that the prevailing view was, “hey, might as well give this a shot.”
It would have sounded preposterous if someone told you in the 1930s that by the 1950s more than 70% of Americans said they trusted the government to do the right thing almost all the time. But that’s what happened.
And it would have sounded preposterous in the 1950s if you told Americans within 20 years trust would collapse amid the Vietnam War and Watergate.
It would have sounded preposterous if you told Americans in the 1970s that within 20 years trust and faith in government would have surged amid 1990s prosperity and balanced budgets.
And equally absurd if you told Americans in the 1990s that we’d be where we are today.
Disclaimer: 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 (parent of AWS), and Microsoft. Holdings are subject to change at any time.
