What We’re Reading (Week Ending 16 November 2025) - 16 Nov 2025
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 16 November 2025):
1. Berkshire Hathaway Inc. News Release – Warren Buffett
One perhaps self-serving observation. I’m happy to say I feel better about the second half of my life than the first. My advice: Don’t beat yourself up over past mistakes – learn at least a little from them and move on. It is never too late to improve. Get the right heroes and copy them. You can start with Tom Murphy; he was the best.
Remember Alfred Nobel, later of Nobel Prize fame, who – reportedly – read his own obituary that was mistakenly printed when his brother died and a newspaper got mixed up. He was horrified at what he read and realized he should change his behavior.
Don’t count on a newsroom mix-up: Decide what you would like your obituary to say and live the life to deserve it.
Greatness does not come about through accumulating great amounts of money, great amounts of publicity or great power in government. When you help someone in any of thousands of ways, you help the world. Kindness is costless but also priceless. Whether you are religious or not, it’s hard to beat The Golden Rule as a guide to behavior.
I write this as one who has been thoughtless countless times and made many mistakes but also became very lucky in learning from some wonderful friends how to behave better (still a long way from perfect, however). Keep in mind that the cleaning lady is as much a human being as the Chairman.
2. BlackRock Faces 100% Loss on Private Loan, Adding to Credit Market Pain – Davide Scigliuzzo and Silla Brush
About a month ago, BlackRock Inc. deemed the private debt it had extended to Renovo Home Partners, a struggling home improvement company, to be worth 100 cents on the dollar. As of last week, the firm had a new assessment: zero.
The drastic revision comes as Dallas-based Renovo — a roll-up of regional kitchen and bathroom remodeling businesses created by private equity firm Audax Group in 2022 — abruptly filed for bankruptcy last week, indicating it plans to shut down…
…It was no mystery Renovo was in a tough spot. In April, lenders had agreed to take losses and convert some of their loans into equity as part of a recapitalization that was supposed to give the company a chance to turn its business around, the people said. In the third quarter, they also allowed for deferred cash interest payments on its restructured debt, an arrangement known as payment-in-kind, regulatory filings show.
Yet at the end of September, funds managed by BlackRock and MidCap Financial were still marking the new Renovo debt at par, which typically indicates investors expect to be paid back in full.
3. Not Joined at the Hip: The Relationship between the Fed Funds Rate and Mortgage Rates – David Pendered
A time-honored, but flawed, assumption about the relationship between mortgage rates and interest rates has been turned on its head as the two have moved in opposite directions following the Federal Reserve’s interest rate cuts over the past year…
…But the Federal Reserve doesn’t set mortgage rates. Instead, the Fed sets short-term interest rates—often called the fed funds rate—in an effort to fulfill its dual mandate from Congress: promoting maximum employment and stable prices. The Fed’s short-term rates factor into how banks and financial institutions set many other rates, such as those for business loans, credit cards, and auto loans. And, of course, mortgages…
…Kris Gerardi and Domonic Purviance, both of the Atlanta Fed, explained that the presumed connection between mortgage rates and the fed funds rate is a misconception. For the past 20 years, mortgage rates have been more closely associated with the interest paid on 10-year Treasury notes than with the fed funds rate set by the FOMC, according to Gerardi, a financial economist who studies real estate finance and housing economics, and Purviance, a subject matter expert who analyzes risk in the housing market and threats it could pose to the financial system.
“While mortgage rates do, typically, move fairly closely with short-term interest rates like the fed funds rate, they are more strongly linked to longer-term rates such as the 10- or 20-year Treasury yield,” Gerardi said. “This is because the average life of a mortgage is around seven to 10 years.”
Gerardi observed that many factors determine longer-term yields on Treasuries and that the Fed’s short-term interest rates are just one factor. Others include the market’s expectation for economic growth, the federal government’s fiscal policies on spending and taxation, inflation expectations, lender capacity as homeowners refinance their mortgages, borrowers’ credit risk, and so forth. Gerardi said, “This means that, at times, mortgage rates and short-term rates can move in opposite directions.”
4. The Benefits of Bubbles – Ben Thompson
Late last year Byrne Hobart and Tobias Huber made a new contribution to our understanding of bubbles with their book Boom: Bubbles and the End of Stagnation. While Perez focused on the benefits that came from financial speculation leading to long-term infrastructure, Hobart and Huber identified another important feature of what they called “Inflection Bubbles” — the good kind of bubbles, as opposed to the much more damaging “Mean-reversion Bubbles” like the 2000’s subprime mortgage bubble. First, here is Hobart and Huber’s definition of an inflection bubble:
Inflection-driven bubbles have fewer harmful side effects and more beneficial long-term effects. In an inflection-driven bubble, investors decide that the future will be meaningfully different from the past and trade accordingly. Amazon was not a better Barnes & Noble; it was a store with unlimited shelf space and the data necessary to make personalized recommendations to every reader. Yahoo wasn’t a bigger library; it was a directory and search engine that made online information accessible to anyone. Priceline didn’t want to be a travel agent; it aspired to change the way people bought everything, starting with plane tickets.
If a mean-reversion bubble is about the numbers after the decimal point, an inflection bubble is about orders of magnitude. A website, a PC, a car, a smartphone — these aren’t five percent better than the nearest alternative. On some dimensions, they’re incomparably better. A smartphone is a slightly more convenient tool than a PC for taking a photo and quickly uploading it to the internet, but it’s infinitely better at navigation. A car is not just slightly faster and more reliable than a horse (although in the early days of the automobile industry, it was apparently common for pedestrians to yell “Get a horse!” at passing motorists); cars transformed American cities. Modern-day Los Angeles is inconceivable on horseback. The manure problem alone beggars the imagination.
This is what makes inflection bubbles valuable:
The fundamental utility of inflection bubbles comes from their role as coordinating mechanisms. When one group makes investments predicated on a particular vision of the future, it reduces the risk for others seeking to build parts of that vision. For instance, the existence of internet service providers and search engines made e-commerce sites a better idea; e-commerce sites then encouraged more ad-dependent business models that could profit from directing consumers. Ad-dependent businesses then created more free content, which gave the ISPs a better product to sell. Each sector grew as part of a virtuous circle…
… In this case, the optimistic take would be that AI is already delivering tangible benefits, that those benefits are leading to real demand from companies and consumers, and that all of the money being spent on AI will not be wasted but put to productive use. That may still be the case today — all of the hyperscalers claim that demand for their offerings exceeds supply — but if history is any indication we will eventually overshoot.
There is, however, a pessimistic way to ask that question: will the AI bubble be beneficial like the positive bubbles chronicled by Perez and Hobart and Huber, or is it different? There have been reasons to be worried about both the physical buildout and the cognitive one.
Start with the physical: a huge amount of the money being spent on AI has gone to GPUs, particularly Nvidia, rocketing the fabless design company to a nearly $5 trillion valuation and the title of most valuable company in the world. The problem from a Perez perspective is that all of this spending on chips is, relative to the sort of infrastructure she wrote about — railroads, factories, fiber, etc. — short-lived. Chips break down and get superseded by better ones; most hyperscalers depreciate them over five years, and that may be generous. Whatever the correct number is, chips don’t live on as fully-depreciated assets that can be used cheaply for years, which means that to the extent speculative spending goes towards GPUs is the extent to which this bubble might turn out to be a disappointing one.
Fortunately, however, there are two big areas of investment that promise to have much more long-term utility, even if the bubble pops.
The first is fabs — the places where the chips are made. I’ve been fretting about declining U.S. capacity in this area, and the attendant dependence on Taiwan, the most fraught geopolitical location in the world, for years, and for much of that time it wasn’t clear that anything would be done about it. Fast forward to today, and not only are foundries like TSMC and Samsung building fabs in the U.S., but the U.S. government is now a shareholder in Intel. There is still a long path to foundry independence for the U.S., particularly once you consider the trailing edge as well, but there is no question that the rise of AI has had a tremendous effect in focusing minds and directing investment towards solving a problem that might never have been solved otherwise.
The second is power. Microsoft CFO Amy Hood said on the company’s earnings call:
As you know, we’ve spent the past few years not actually being short GPUs and CPUs per se, we were short the space or the power, is the language we use, to put them in. We spent a lot of time building out that infrastructure. Now, we’re continuing to do that, also using leases. Those are very long-lived assets, as we’ve talked about, 15 to 20 years. And over that period of time, do I have confidence that we’ll need to use all of that? It is very high…
…It’s hard to think of a more useful and productive example of a Perez-style infrastructure buildout than power. It’s sobering to think about how many things have never been invented because power has never been considered a negligible input from a cost perspective; if AI does nothing more than spur the creation of massive amounts of new power generation it will have done tremendous good for humanity. Indeed, if you really want to push on the bubble benefit point, wiping away the cost of building new power via bankruptcy of speculative investors — particularly if a lot of that power has low marginal fuel costs, like solar or nuclear — could be transformative in terms of what might be invented in the future…
…I’ve been less worried about the cognitive capacity payoff of the AI bubble for a while: while there might have been concern about OpenAI having an insurmountable lead, or before that Google being impregnable, nearly everyone in Silicon Valley is now working on AI, and so is China. Innovations don’t stay secret for long, and the time leading edge models stay in the lead is often measured in weeks, not years. Meanwhile, consumer uptake of AI is faster than any other tech product by far.
What is exciting about the last few weeks, however, is that there is attention being paid to other parts of the stack, beyond LLMs. For example, last week I interviewed Substrate founder James Proud about his attempt to build a new kind of lithography machine as the center of a new American foundry. I don’t know if Proud will succeed, but the likelihood of anyone even trying — and of getting funding — is dramatically higher in the middle of this bubble than it would have been a decade ago.
It was also last week that Extropic announced a completely new kind of chip, one based not on binary 1s and 0s, but on probabilistic entropy measurements, that could completely transform diffusion models. Again, I don’t know if it will succeed, but I love that the effort exists, and is getting funding. And meanwhile, there are massive investments by every hyperscaler and a host of startups to make new chips for AI that promise to be cheaper, faster, more efficient, etc. All of these efforts are getting funding in a way they wouldn’t if we weren’t in a bubble.
5. An Interview with Michael Morton About AI E-Commerce – Ben Thompson and Michael Morton
What we started to do is we took a couple different products and we ran them through the traditional funnel and we’ll go back to the first example I used, shoes for flat-footed runners. What I did to start the exercise was I did hours and hours of research reading literally podiatry magazine posts, and every single post about the best running shoes for flat feet, I organized them, I ranked them, so what shoes got first and second, and we came out with some clear winners. “Here are the one, two, and three best running shoes for people with flat feet”, so we know what the best answer is.
Now let’s put it in Google search, and what you found was the PLAs at the top, the carousel you’ll see a set of icons that are horrible for getting the right answer.
So are those pure payment to get there, or is Google actually making determination of what’s the best answer?
MM: Yeah, for the work we did, one of the six was of the top ranked running shoes and when you looked at the models, their slugging percentage was, I would say 60 to 80% of the time, what they showed you out of the five icons were the best running shoe. So if they had five, they’d get one bad one.
Now, that’s a good question people have pushed back, “Well, how can these people be at the top of the feed if they’re paying for it” and this inevitably boils down to a conversion game. Shouldn’t it really only be the best products? And in an ideal state, yes, but this is also an output of which websites have better conversion rates? Who has bigger marketing budgets? Who’s looking to build a brand at this specific time? No one knows a perfect answer for the weightings and outputs of Google Search. Well, there are people, but their emails have @google.com, not our email addresses.
So why did Google’s results get like this, to the extent that you feel one out of six was a good answer? And you contrast the ChatGPT where four out of six are good answers. Is this a matter of, to your point, they’re measuring things like conversion factors, what actually goes through? Is it some people just paid more? Was this something that they can fix or is it that the money flowing in is too much that they can’t actually recommend four out of six because two, three and four might not pay them very much? What happened?
MM: This is probably an hour podcast in itself, but to try to simplify it as best as possible, I think there’s a lot of influencing factors. We are all very familiar with the gamification that has occurred with search, the entire giant industry of SEO, an army of marketing consultants to tell you how to win the keyword bidding game…
…MM: Yes. And look, before I came on here today, I re-ran the exercise, and search was again one for six for the shoe. But then I did AI mode in Google for the flat-footed running shoes — basically batted perfect, just incredible.
So that’s the question. Can Google fix this?
MM: Yeah. Michael Nathanson and I, I was like the devil on his shoulder while Google was going down every day, ChatGPT is just adding users and the bear case is just building and building, building and I’m over there, I’m like, “Oh, they got a problem, Michael, they got a problem”, and Michael’s been doing this for long enough where it’s really hard in these moments to see through this overwhelming wave of negative sentiment. And the day after Google I/O, I go into Michael’s office, I’m like, “Okay, I think they’re going to run towards this problem”, and now you’re sitting on the biggest distribution network in the world, the best AI infrastructure stack, and you’ve increased the friction from moving from being a Google user to a ChatGPT user. So people like you and I were ChatGPT probably day one, my mom and wife are now just going to end up being AI overviews and AI mode and maybe never ChatGPT people. So I think Google has the tool set to win this…
…So, who is the number one winner? Let’s grant this is going to happen, it’s so much better, people are going to be searching on ChatGPT for products. Who wins?
MM: Amazon. (laughing) This is like where movie starts with the ending scene, and then you work towards it — Amazon should win. And the way to work through this is you can go a couple angles. Again, why I like searching this subject so much, and thinking about it is, ask the models. So, we ask ChatGPT, Gemini, Grok, and all the different models, “For a e-commerce query, what do you weight in your decision-making process?”, and from most important to least important. And the top three, number one is price, number two is trustworthiness, and number three is speed. Price, speed, trustworthiness, you start to see where this is going and then I asked them, “Okay, of these weightings, who does the best job at delivering?”, every singly model, Amazon is number one, Walmart is number two and you go down the list, Target, Best Buy, eBay-…
…MM: Yeah, let’s take a step back. I’m Brand A, I sell most of my stuff on Amazon, I order it, it gets sent to the warehouses in Amazon, but I have 40% of this business that’s not on Amazon, but I don’t want to have a 3PL that I use outside of Amazon, it’s just a pain in the butt, why don’t I use Amazon? Now what Amazon will let you do is for the stuff that you sell on your own store, not on Amazon, they will deliver in unmarked boxes. So, it’s not like the Amazon Prime labeled all over it, and it’s just multichannel fulfillment, and for a long time, Walmart said, “You can’t use that, if you’re a third party merchant selling on our marketplace, you have to use our fulfillment network, or UPS or FedEx, but you can’t use the…” — basically, you can’t use Amazon multichannel fulfillment, you got to play within these rules.
I think it was in April of 2025, Walmart removed the multichannel fulfillment limitation. So now if you’re a Walmart and you’re plugging in your first party and third party inventory into ChatGPT, the whole thing about Amazon’s mode is that FBA business.
I just want to make sure I understand this. By multichannel fulfillment, you mean that you can buy on Walmart and it’s delivered by Amazon or Walmart? Or Walmart will deliver for any product?
MM: No. So, you can sell it on a Walmart marketplace. Now one of the Walmart rules is is that it can’t be delivered by a truck with Amazon labeling on it. You’ll see the Amazon Flex workers that drive around in cars with stuff, so who knows exactly? And if everybody is going to follow the rules here. But it’s just interesting because Walmart runs towards this new channel, and, in theory, the third party sellers on Walmart’s marketplace that would be presented in a ChatGPT answer have the ability to use a multichannel fulfillment service that is not Walmart’s and is not their own, and it brings that incredible distribution network to ChatGPT.
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, and Microsoft. Holdings are subject to change at any time.
