What We’re Reading (Week Ending 01 February 2026) - 01 Feb 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 01 February 2026):
1. Anthropic Lowers Gross Margin Projection as Revenue Skyrockets – Sri Muppidi
Anthropic last month projected it would generate a 40% gross profit margin from selling AI to businesses and application developers in 2025, according to two people with knowledge of its financials. That margin was 10 percentage points lower than its earlier optimistic expectations, though it’s still a big improvement from the year before…
…If Anthropic also counted inference costs for Claude chatbot users that don’t pay for a subscription, its gross margin would be about 38%, or a few percentage points lower than for paid users, based on The Information’s analysis…
…Anthropic has previously projected gross margins above 70% by 2027, and OpenAI has projected gross margins of at least 70% by 2029, which would put them closer to the gross margins of publicly traded software and cloud firms. But both AI developers also spend a tremendous amount on renting servers to develop new models—training costs, which don’t factor into gross margins—making it more difficult to turn a net profit than it is for traditional software firms.
The inference costs are in addition to costs from training the models. Anthropic last month expected its costs for training its AI models for 2025 to be roughly $4.1 billion, up roughly 5% from its summer projections. OpenAI, meanwhile, expected to spend $9.4 billion on compute for training its AI models last year.
2. A business that scales with the value of intelligence – Sarah Friar
We launched ChatGPT as a research preview to understand what would happen if we put frontier intelligence directly in people’s hands…
…As ChatGPT became a tool people rely on every day to get real work done, we followed a simple and enduring principle: our business model should scale with the value intelligence delivers…
…Looking back on the past three years, our ability to serve customers—as measured by revenue—directly tracks available compute: Compute grew 3X year over year or 9.5X from 2023 to 2025: 0.2 GW in 2023, 0.6 GW in 2024, and ~1.9 GW in 2025. While revenue followed the same curve growing 3X year over year, or 10X from 2023 to 2025: $2B ARR in 2023, $6B in 2024, and $20B+ in 2025. This is never-before-seen growth at such scale. And we firmly believe that more compute in these periods would have led to faster customer adoption and monetization.
3. 50x in 5 Years – Joe Raymond
I discovered Cable Information Systems (CIS) in a page of one-liner descriptions of companies in the OTC edition of Moody’s Manual.
It was trading for a dollar per share…
…Believe it or not, Cable Information Systems had 50,000 subscribers in 1980 which placed them in the top 10 U.S. cable companies.
The company had about 1 million shares outstanding which were inactively trading at a dollar a share in the pink sheets.
At the time, it was said that cable subscribers were going to be worth $1,000 each to an operator of cable services. Thus, it became apparent that Cable Information with 1 million shares outstanding was worth $50 million although it was selling at a market value of only $1 million.
A second way of valuing a cable company was to apply the then-going multiple to cash flow, deduct debt, and divide by outstanding shares. Doing that I also came up with $50 a share.
So, using the two ways of valuing a cable company at the time, I found a $1 stock worth $50.
I asked Peter if he knew of anyone that cared to sell shares, and he told me that some of the employees were shareholders and, from time to time, some of them were interested in selling. I asked him if he would give them my name and number and he said gladly, they’d be happy to know of me.
Over a period of months, some of these employees called and asked if I would buy their shares. I said yes, I am glad to pay the current market price of approximately $1 per share.
Before buying, I told any caller offering shares to me, “Look, I want to make clear to you that I’m buying because I think the shares are worth a heck of a lot more than a dollar and if I were you, I would not be eager to sell.”
As employees, I wanted them to know I felt strongly it was not a good idea to sell. After questioning them and explaining why they should not sell, some people still sold me their shares…
…Late in the year, 1981, Peter telephoned me to tell me that he was selling out at $48 in cash to John Malone, who was the biggest cable operator in the United States.
My first reaction was, “Wow, two dollars short of what we had calculated it was worth.”
But Peter told me that there were two dollars being put into escrow and they will probably be paid to shareholders as well, bringing the total consideration to $50…
…Here’s what Larry was looking at in Moody’s Manual back in 1977:
Sales were growing double digits and accelerating. Margins were expanding.
The stock traded between $0.38 and $1.00 in 1977. The normalized P/E ratio was 1x on the low end and 3x on the high end.
There was some debt, as was common with fast growing cable companies at the time. The EV/EBITDA at $1 per share was 5x.
4. My Interview With Andy Jassy: OpenAI, Trump, Power and the Future of AWS – Jessica E. Lessin and Andy Jassy
Andy Jassy: I think that we’re excited about agentic commerce. I think that it has the chance to make it easier for customers to find what they want. If you know what you want, it’s pretty hard to find a better experience than popping onto Amazon and searching and finding it.
But the one place still where physical retail has some advantages, in my opinion, is the ability to go in, not know what you want, ask questions, refine those questions, have somebody point you to different things. And I think agents are going to help customers with that type of discovery. And it’s part of why we’ve invested so much in Rufus, which is our shopping assistant, which has really gotten quite good.
And I think that over time that we will work with other third-party agents as well. I think today the experience hasn’t been great yet. You know, I think that a lot of these third-party agents, they don’t have your buying history, they don’t have what you like, a lot of the information about pricing and the product is off.
But over time, I do believe that will get better. I also think there needs to be the right value exchange between the agents and between the retailers themselves, but I am optimistic that those will work out. We’re having conversations with lots of people and I’m very bullish on agentic commerce…
…Jassy: As you know, the chips are such an important part of the performance and the cost structure for people running technology infrastructure. We learned in the CPU side of the business, we had this deep relationship with Intel, which we still do. But when you have a significant leader, it’s not always their priority to take price performance down for customers.
And one thing we learn about customers over and over and over again is they want better price performance. And so we built Graviton, our own custom CPU silicon, which is about 40% more price performance than the leading other x86 processors. And that has been really great for our customers and business.
And about 90% of our top 1,000 customers now use Graviton in a very significant way. And we just saw this same movie happening in the AI space. And we have a very deep partnership with Nvidia, and we will for as long as I can foresee. But customers badly want better price performance. And so that’s why we built Trainium.
Our Trainium2 chip has been fully subscribed. Anthropic runs hundreds of thousands of Trainium2 chips as they’re training their next model of Claude on top of it. It’s a multi-billion dollar business. And we just released Trainium3 which is our next version of chip, which is 40% more price performant than Trainium2.
And Trainium2 was about 30% to 40% more price performant than the other leading GPUs out there. If you want to allow customers to be able to use AI as expansively as they want, you must take the cost of inference down. And the chip is a big piece of it…
…[Jassy:] I think we’re just in this stage right now where there is so much demand. And, you know, we’re not at this point, we’re not just trying to guess whether there’s demand. We have so much demand. I think the industry would tell you as a whole, there is still not enough capacity, even though it’s gotten better than it was 18 months ago, we could still be growing faster if we had more capacity…
…[Jassy:] We’re in this really interesting stage of AI adoption, in my opinion. It’s very bar-belled.
You have a lot of use by the AI labs who are consuming gobs and gobs of compute right now, and maybe a runaway app or two like ChatGPT. Then the other side of the barbell are enterprises who are really using AI for cost avoidance or productivity. Customer service, business process automation, things like that.
But the middle of that barbell are all the enterprise workloads in production that are not using inference yet. That will. We’re still at this relatively early stage. I believe that the middle part of the barbell is going to be the largest absolute segment. And I think when enterprises get to deploying their production apps using inference and AI, they’re going to want those applications to run close to the rest of their other applications and where their data is.
And just the largest amount by a fair bit, resides in AWS. And so we’re making it easier and easier for customers to be able to run their core workloads with their AI workloads.
5. An Early Buffett Partnership Investment – Joe Raymond
The first investment Buffett disclosed in his partnership letters was Commonwealth Trust in 1958…
…Buffett started buying Commonwealth at $50 and thought it was worth $125…
…Warren was paying 5x earnings and 80% of book value. Seems like a good deal for a bank earning 20% on equity.
The second is the nature of the bank.
Commonwealth Trust had $50 million of deposits and only $20 million of loans, most of which were residential mortgages. It also had $21 million of government securities.
The asset mix appeared highly conservative, at least from a credit perspective.
While the assets looked solid, there was little equity in the business ($2 million of equity on $53 million of assets). You don’t see this sort of leverage today, but it was common practice amongst small thrifts in the ’50s…
…A sharp increase in reserves, coinciding with rising interest rates, caused a big hit in 1954. This was magnified by the fact that Commonwealth’s equity was only 4% of assets going into the year. Book value per share fell 34%.
By the time Buffett was buying in 1957, interest rates were moderating, reserves were healthy, and earnings and equity were about to resume their growth…
…Warren didn’t hold long.
He sold his shares for $80 apiece about a year after buying them.
This was a 25% premium over the prevailing market price at the time and represented a profit of 57% for the partnerships…
…Buffett said the buyer at $80 could expect to do well over time and that he was selling to recycle the proceeds into a better opportunity (Sanborn Map)…
…About a year after Buffett sold, Commonwealth Trust merged with Hudson County National Bank (HCNB). It was a share-for-share deal, and the combined bank kept the Hudson County name…
…Over the next eight years, HCNB grew its book value from $135 to $183 per share (4% CAGR) and paid $57 per share of dividends. The average stock price in 1968 was $228 (1.25x book value).
So, the buyer from Buffett at $80 in 1958 had $228 by 1968 plus $58 of dividends.
Including dividends, the total annual return was in the mid-teens…
…This is a good example of successful value investing.
Corporate performance was mediocre, but big follies were avoided. Equity grew slowly and dividends were paid.
A cheap entry price and average exit price produced a mid-teens IRR over more than a decade.
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 Amazon (parent of AWS). Holdings are subject to change at any time.
