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 08 December 2024:
1. Why China’s Economy Opened Up in the 1970s – Joe Weisenthal, Tracy Alloway, and Odd Arne Westad
Joe (13:32):
What does it mean when you talk about history being “contingent?” You used that word a couple of times and I actually don’t know if I fully understand what that means, but when you’re telling these stories, or this story, and you’re keeping in mind the contingency in history, can you talk a little bit more about this idea?
Odd (13:48):
So you’ll see from the book that we go in and out from the sort of micro to the macro level of telling history. And if you look at the night when the coup against the radicals — the so-called Gang of Four within the party — took place, which we describe in some detail, you know, what happens from hour to hour…
Joe (14:10):
Right, this was the moment in which the left faction, after Mao dies, was arrested, and allowed for a sort of more moderate path to emerge.
Odd (14:21):
That’s right. And it was in effect a military coup. I mean, it was undertaken by the military and the security forces against the people who Mao himself had put in charge of the party, including his widow who was most prominent of all, Jiang Qing. Now that night, and the following few days, things could have ended up very differently. I mean, Shanghai, the biggest city in China by far, was still under control of the radicals. There were military units that supported the radical approach to politics. This could have ended up very differently from what it did.
And as we describe in the book, some of the plotters, some of the coup-makers themselves, in those days that followed the coup itself, were completely surprised by how little resistance there had been from the left. And how little chaos there had been on the streets. So that’s what I mean with it being contingent. I mean, this is something that obviously connects to the larger picture that we see today — going back to your sort of three level version of what happened in China. But it didn’t seem that obvious at the time. And it could have gone in very different directions from what we’re seeing today.
Tracy (15:30):
How important was the fraying of the relationship between China and the Soviet Union in the 1960s, early 1970s to spurring or catalyzing that opening up? Because it does feel like the sudden emergence of the Soviet Union as an external enemy, it feels like that led China in some respects to open up to the US and some other countries.
Odd (15:56):
This is a sort of trajectory that I think it’s really important to get right, because what Mao and his group of leaders did in the late 1960s was to turn to the United States as an ally — a pseudo ally, security ally — against the Soviet Union because they were so deadly afraid that there would be a war with the Soviets — a war that China certainly would have lost, given the state that Chinese communists themselves had pulled China into during the Cultural Revolution. So what Mao did was to turn to the enemy far away, the United States, to help back him against an enemy much closer to home, the Soviet Union, which they had this falling out with mainly for ideological reasons.
From Mao’s perspective, this was always intended to be a strictly security oriented pseudo alliance. It was directed against the Soviet Union. Mao to the end of his days was puzzled that United States would support the real communists, meaning him, against the fake communists, meaning the Soviet Union. But as long as they were willing to do that, he was certainly willing to reap the benefits. But he never intended that this would have any effect in terms of the increasingly radical communist direction that he was taking for China internally, domestically.
So that’s when what happens in 1976, after Mao’s death, becomes so significant, because the people who then took over, they thought, ‘Aha! We have this relationship between United States. They are supporting us for their own reasons in the Cold War against the Soviet Union. We can now also make use of this to supercharge Chinese reform.’ If it hadn’t been for that relationship, strictly security oriented, that already existed between China and the United States, I doubt that that would be possible. So it’s very important when about the longer term US-China relationship to think about that origin and how this actually got started. Very different from the way most people think about it, where the security element and the reform element are sort of conflated into one…
…Odd (36:05):
I think it was both. I mean in the Xi Jinping case, I think he was picked by the party as the, what Chinese would call, the core leader, back in the early twenty-teens, in response to what was seen as a bunch of real problems, from a Chinese Communist Party perspective, over liberalization, decentralization, corruption, strength of private companies that meddled in a lot of things that the communists didn’t want them to meddle in. They wanted to get a strong leader in who could deal with those issues, in a way that his predecessors, Jiang Zemin [and] Hu Jintao, had not been able to do it. So they wanted a strong leader. It’s just that, I think even for many communist leaders of that generation, they got more than they bargained for. So that’s where the personality aspect comes in. They got a leader who really wanted to return, at least on some issues, to the Maoist or even the sort of pre-Mao period, in terms of the CCP’s history and emphasizes the party’s position over what even many party leaders back 10 [or] 15 years ago thought would be good for China.
And it’s a classic example of responding to real world problems — not unknown in this country, right? — by going very far in one direction, hoping that that would resolve the problem that is there, and then getting stuck in a way with the kind of leader that you have in this case, in Xi Jinping. So I think that’s the story, the way we can tell it now. I hope at some point to be able to tell that story based on archives and primary documents, as an historian, we can’t do that yet. But I think at some point, we’ll be able to do that, and then it’ll be fascinating to test that hypothesis about how this happened.
Tracy (37:54):
So just on the revolution from below point, one of the things that you emphasize in the book is a lot of the stuff that happens in this time period is a result of people feeling that they are heading somewhere, that there’s a grander Chinese vision that can be achieved. And so that motivates people to actually do something. I’m curious, just going up to the present day, do you get a sense that people feel that? That there’s like a direction that China is heading in that it’s clear to people what they are trying to do?
Odd (38:33):
At the moment, absolutely not. I think it’s very, very clear that a lot of people in China do not understand where the country is heading and what the reasons are. And you know, you don’t spend much time in Beijing before you realize that these days. I think it was very different in the time period that we are talking about, which was generally a time of uplift, at least in economic and and social terms. And it’s right to say, I mean as many historians have said, that there was an element of a bargain in this. That, at least for some Chinese, not not everyone, but for some Chinese, maybe particularly in business, that would accept a dictatorship for what it was and then went on getting rich and and establishing some of these great or middling fortunes that you find so many of in China today. And that is good. I mean that was positive. It was much, much better than the dark past that we described at the beginning of the book.
It was just that, China wasn’t able to take what, in our view, is a necessary step to improve its political system, its overall attempt at trying to become a more open, more pluralistic country in the period when the going was good, when there was a general sense that China was making advances, domestically and internationally. Now, I think even if people from within the Chinese Communist Party after Xi Jinping would try to move in a direction of increased liberalization — which I think they will have to do at some point because people are just very unhappy with the kind of system that is there at the moment — it would be much more difficult, because the going is not that good. And probably it’s never going to be that good again. I mean, it was a remarkable period of economic transformation, 10% per year growth rates. It would’ve been possible to carry out necessary reform. But these people didn’t want to do it because they had become so preoccupied with holding onto power themselves. And I think, historically, that that might turn out to be the biggest mistake that the Chinese Communist Party has made.
2. Tim Cook Wants Apple to Literally Save Your Life – Steven Levy and Tim Cook
Some companies charge for AI-enhanced services. Did you consider that?
We never talked about charging for it. We view it sort of like multitouch, which enabled the smartphone revolution and the modern tablet.
You’ve personally been using Apple Intelligence for a while. What has been most useful for you?
We’re an email-based company, and I get enormous numbers from users, employees, partners, and so forth. Having it summarize author responses is a game changer, and having it prioritize things for you so you’re not doing your usual triage. Then, of course, there are fun things like the Image Playground.
I’ve heard you say that Apple Intelligence could make you funnier, which seems strange.
I think it can make you friendlier, which, in many ways, can be funnier as well.
Having AI speak for people makes me wonder whether the nature of communication will degrade. If Apple Intelligence writes something funny, who’s being funny, the sender or the AI?
It’s still coming from you. It’s your thoughts and your perspective. You and I both remember the productivity that came from the advent of the personal computer. It was no longer you punching your calculator, you were doing something on a spreadsheet. It was no longer you at the typewriter, you were using a word processor. Logic Pro helps musicians create music, but they’re still the author.
One of your demos involves a fictional recent graduate applying for a job. The cover letter is colloquial and somewhat sophomoric, but with Apple Intelligence a single click changes it to look like a savvy, smart person wrote it. If I’m a recruiter who hired that person, maybe I will feel tricked if they don’t live up to the professionalism of that letter.
I don’t think so. By using the tool, it comes across as more polished. It’s still your decision to use the tool. It’s like you and I collaborating on something—one plus one can equal more than two, right?…
…When you’re thinking about things late at night, don’t you sometimes ask what it would mean if computers had superhuman intelligence?
Oh, of course. Not just for Apple, but for the world. There’s so much extraordinary benefit for humanity. Are there some things you have to have guardrails on? Of course. We’re very deeply considerate about things that we do and don’t do. I hope that others are as well. AGI itself is a ways away, at a minimum. We’ll sort out along the way what the guardrails need to be in such an environment…
…Meta and Snap are leading us to mixed-reality glasses that we’d wear continually. Is the bigger, heavier Vision Pro ultimately headed that way?
Yes, it’s a progression over time in terms of what happens with form factors. AR is a huge deal. With Vision Pro, we’ve progressed to what is clearly the most advanced technology we’ve ever done, and I think the most advanced technology in the world in terms of electronics problems. We’ll see where it goes.
Apple has created a lot of consumer tools for medical technology. What’s the strategy for biological metrics and prosthetics?
It’s clear to me that if you zoom out way into the future, and you look back and ask what Apple’s biggest contribution was, it will be in the health area. That’s what I really believe. When we started pulling that string with the Apple Watch, it was a cascade of events. We started with something simple, like monitoring your heart rate, and then figured out we could pick up heart signals to get to an EKG and an AFib determination. Now we are monitoring sleep apnea. I’ve gotten so many notes over time from people who would have not survived had it not been for the alert on their wrist.
Apple plans to give AirPods the ability to correct for hearing loss. I bet the makers of expensive hearing aids are freaking out.
It’s not about competing against hearing aids on the market. It’s about trying to convince people who have hearing loss to use their AirPods. The vast majority of people with hearing issues have not been diagnosed. For some people, hearing aids have a stigma, and we can counter that with AirPods. And we can have people diagnose themselves. It’s the democratization of health…
…We’re doing this interview at Apple Park, which is now seven years old. Have you been surprised by anything that couldn’t have been anticipated when it was just blueprints?
It’s promoted collaboration even more than I thought. That was a key component of the design, but there are so many places here where you just unexpectedly run into people. In the cafeteria, at the coffee bar, outside when you’re going across the pathway. Also, there’s a connection here to Steve that is incredible and very deep. We have the theater named after him and think about him all the time, but I can feel him in other spaces too.
3. 2024: The State of Generative AI in the Enterprise – Tim Tully, Joff Redfern, Derek Xiao, with Claude Sonnet 3.5
AI spending surged to $13.8 billion this year, more than 6x the $2.3 billion spent in 2023—a clear signal that enterprises are shifting from experimentation to execution, embedding AI at the core of their business strategies…
…Today, 60% of enterprise generative AI investments come from innovation budgets, reflecting the early stages of generative AI adoption. However, with 40% of generative AI spending sourced from more permanent budgets—58% of which is redirected from existing allocations—businesses are demonstrating a growing commitment to AI transformation…
…While foundation model investments still dominate enterprise generative AI spend, the application layer is now growing faster, benefiting from coalescing design patterns at the infrastructure level. Companies are creating substantial value by using these tools to optimize workflows across sectors, paving the way for broader innovation…
…In 2024, much of the action happened at the application layer. With many architectural design patterns established, app layer companies are leveraging LLMs’ capabilities across domains to unlock new efficiencies and capabilities. Enterprise buyers are seizing the moment, pouring $4.6 billion into generative AI applications in 2024, an almost 8x increase from the $600 million reported last year…
…Code copilots lead the charge with 51% adoption, making developers AI’s earliest power users…
…Support chatbots have captured significant usage, with 31% enterprise adoption…
…Enterprise search + retrieval and data extraction + transformation (28% and 27%, respectively) reflect a strong drive to unlock and harness the valuable knowledge hidden within data silos scattered across organizations…
…Meeting summarization ranks fifth in use cases (24% adoption), saving time and boosting productivity by automating note-taking and takeaways…
…When selecting generative AI applications, enterprises have clear priorities: Return on investment and industry-specific customization matter most when selecting new tools. Surprisingly, price isn’t a major issue; just 1% of the enterprise leaders we surveyed mentioned price as a selection concern. Buyers are playing the long game: They are far more focused on tools that can deliver measurable value (30%) and that understand the unique context of their work (26%) over those offering the lowest price tag (1%)…
…When AI pilots stutter or stall, it’s often due to challenges not adequately considered during the selection process. Although buyers aren’t checking price tags, implementation costs, cited in 26% of failed pilots, frequently catch them off guard. Data privacy hurdles (21%) and disappointing return on investment (ROI) (18%) also throw pilots off course. Technical issues, especially around hallucinations (15%), round out the top reasons for failure…
…Traditionally slow to adopt tech, healthcare is now leading generative AI adoption with $500 million in enterprise spend…
…Historically resistant to tech, the legal industry ($350 million in enterprise AI spend) is now embracing generative AI to manage massive amounts of unstructured data and automate complex, pattern-based workflows…
…With its complex data, strict regulations, and critical workflows, financial services ($100 million in enterprise AI spend) are primed for AI transformation…
…From Hollywood screens to creators’ smartphones, generative AI is reshaping media and entertainment ($100 million in enterprise AI spend)…
…Foundation models still dominate. The LLM layer commands $6.5 billion of enterprise investment…
…Rather than relying on a single provider, enterprises have adopted a pragmatic, multi-model approach. Our research shows organizations typically deploy three or more foundation models in their AI stacks, routing to different models depending on the use case or results…
…Among closed-source models, OpenAI’s early mover advantage has eroded somewhat, with enterprise market share dropping from 50% to 34%. The primary beneficiary has been Anthropic,* which doubled its enterprise presence from 12% to 24% as some enterprises switched from GPT-4 to Claude 3.5 Sonnet when the new model became state-of-the-art. When moving to a new LLM, organizations most commonly cite security and safety considerations (46%), price (44%), performance (42%), and expanded capabilities (41%) as motivations…
…To power RAG, enterprises must store and access relevant query knowledge efficiently. While traditional databases like Postgres (15%) and MongoDB (14%) remain common, AI-first solutions continue to gain ground. Pinecone,* an AI-native vector database, has already captured 18% of the market.
4. An Interview with Understanding AI Author Timothy B. Lee – Ben Thompson and Timothy B. Lee
As a side note, just as you sort of referenced it in passing, there is always the question of where are the productivity gains, when it came to, first the PC, and then the Internet? Is your sense that those just take a while to show up? Is there just a massive amount of consumer surplus that is not measured? What’s your big picture take on that question?
TL: There’s a couple of things. One is it takes a while to show up because to really get the big gains from a new general purpose technology, often you need to reorganize a lot of other business processes. There’s a famous analogy economists like to use for when they originally electrified the economy. The first thing they try to do is they tried to take the old steam-powered factories that just had one big crank shaft and put an electric motor in and that didn’t get you much improvement because the electricity was not cheap.
It was arguably worse.
TL: But then ten to twenty years later, people figured out, “Oh, we can have a bunch of small electric motors, one at each workstation, and now factories can be a lot more efficient”, but you had to build new factories and new businesses to do that…
…Believe me, I think we’re around the same age, I know exactly what you mean and feel. That said, I feel like the big company — Wikipedia came out back when I was in college, or around that time and of course everyone, professors or teachers, banned the use of it. But what you quickly realized is that the key way to use Wikipedia is the sources. You go to Wikipedia, and then it has links to all the sources, then you have your original source documentation. I do feel like ChatGPT is just such a better version of that, particularly with the search version, and when it does sources, it’s just like, “What if we make a Wikipedia that just fills all sort of weight and space about knowledge”, and it’s pretty tough to beat in that regard.
TL: Yeah, absolutely. And as with Wikipedia, you have to be smart about it. You can’t assume that everything is accurate, you have to check your work. But I definitely find, anytime I have, if I’m trying to make a list of things and I want to know all the companies in a particular category, it’s a pain in the ass to find that on Google. Whereas if you ask ChatGPT, “Here’s like three companies in this category, give me more on the list”, it’ll know a bunch more of them. There’s so many things like that. So yeah, definitely, I don’t want to say never use it or it’s not useful. It’s definitely useful, but it’s 1% to 2% more productive over the course of a week rather than really transformational…
...Again, to go back to your perspective of looking at it over the last 18, 20 months since you started, do you think we’ve hit a wall with AI? You started wondering this publicly actually last December when Gemini came out and you felt a little underwhelmed, particularly given Google’s advantages. You weren’t sure at the time, was Google underperforming for Google specific reasons, maybe have we gotten as far as we can with GPT-4? What’s your evaluation 11 months on from that article?
TL: The thing I’ve noticed is that we keep hearing about there’s going to be a GPT-5—
It’s not here.
TL: There’s going to be a new big model and it hasn’t been released and I don’t have enough sources in the inside to those companies to know why that’s happening. But it could be they’re just still working on it and it’s going to come out next month and blow my mind, but every month that ticks by makes me a little more skeptical. Especially because the other thing trend we’ve seen is these companies are releasing these smaller models that are almost as good as the big models.
And then even to some extent, I was pretty impressed by o1, but what o1 did is kind of different. It wasn’t like scaling up the model, it’s like we’re going to do more inference time compute. In certain ways, it was much better, but it wasn’t better overall.
So my still pretty rough hypothesis, but my hypothesis is that there’s kind of a limit to what the current LLM architectures can do and we’re sort bumping up against that in various — I mean, another thing, we’ve had multimodal models that are much better, so we can do real-time voice and we can do images, so there’s new things it can do. But in terms of just the increase of overall reasoning capability, it doesn’t seem like we’ve had a big jump, really since March of 2023 when GPT-4 came out, and so I’m not going to make a strong prediction because again, it could come out next month and amaze me, but every month that ticks by I get a little bit more wondering what’s going on.
What do you think is the limitation? Is it data, compute or is it just a fundamental limitation of the transformer architecture?
TL: My guess is it’s a fundamental limitation of the transformer architecture, and I think the main issue is that the transformer architecture requires all of the model state to be in these vectors for individual words, and then it keeps a record of that forever — the whole context, there’s no process where you summarize and abstract a way. If you think about your life, you think about something that happened ten years ago, you don’t remember every single thing you said, everything that others said, you have a abstract memory that, “Oh, in 2014 I remember I lived in this place and I had this job”, and things you learn kind work their way into the brain, but it’s organized in a good way. LLMs just don’t have a way to do that.
So if I think about how people expect that at some point you’re going to have an LLM who’s like a personal assistant who maybe will work with you over your career and know all your habits and make all your appointments stuff and to do that, I just think this architecture where you remember every token exactly and do attention over that whole corpus, I don’t have any way of synthesizing and abstracting and forgetting unimportant things, just as a computer scientist, that doesn’t seem viable to me…
…Do you think there’s a bubble now then?
TL: That’s always a hard question to say. Part of what’s hard about bubbles is that often people start calling a bubble pretty early and then the bubble keeps growing and people keep saying there’s a bubble.
Right. If people think there’s a bubble, there is not a bubble, that’s my heuristic.
TL: Well, there’s that, but also, at some point, the stock or the house price or whatever will peak and then go down, and the people who said it was a bubble right at the top will be right, but some people who called it way at the beginning were probably wrong.
I do expect a period where AI gets overly frothy and then crashes. Whether we’re currently there or just headed for that, is a little hard to say. I do not expect a dot-com bust level expansion, because as you were saying, I do think that this technology has clear benefits, it’s mostly big technology companies, it’s not as venture-funded. In fact, some of the early really crazy-funded companies have already been acquired.
So, yeah, I think the level of hype right now is a little too high and there’ll be some pullback, but I don’t think you’ll see a big crash and I don’t think you’ll see much of a pullback from deployment, because I think there really is enough value here that there’s going to be a big market for a lot of people working on it, and a lot of valuable stuff will come out of it in a pretty direct way.
I saw a new theory this week that actually really resonated with me. So this might be new to you, so I’m going to drop it to you on the spot. I think the big question on if you’re thinking about bubbles, you go back to a Carlota Perez model of the importance of bubbles and driving, you go back to the dot-com era, the really important part was the telecoms build out, which was, at the time, some people called it, and in retrospect, clearly insane. If you’re rolling out all this fiber and everyone’s doing it, the costs are going to go to zero, you’re all going to go bankrupt because it’s all financed by debt, as large infrastructure usually is. But the long-term payoff from that was massive, right? That, basically, booted off the whole Web 2.0 era where now everyone, suddenly, had broadband. Recessions suck, but there was a huge societal benefit that did come from that build out.
You go back to previous ones, whether it be electricity or steam, you had these similar cycles and the big question was, “What’s the societal beneficial output of an AI bubble if there is a bubble?” and chips never quite fit, because chips wear out and chips get better. So, if you buy a bunch of chips, but they’re five-year-old chips, what’s the benefit there? Doug O’Laughlin put this tweet out here, that has been really striking to me. He said, “Internet Bubble:Telecom::AI:Power/DCs”, and to me, that makes sense. If you’re going to actually build more nuclear power, or you’re going to do massive investments in solar and batteries, or whatever it might be to fuel these sorts of things, those are investments that, 1) can definitely make you go bankrupt because you’re taking out a bunch of debt to fund it, but 2) will retain value for many, many, many years to come. What do you think of that analogy? To me, it seems pretty compelling.
TL: Yeah, I one hundred percent agree with that. I mean, I was actually going to say the part of it that seems most bubbly is this stuff about Microsoft leasing out Three Mile Island for 20 years. Again, we were talking before is, “Do I think scaling law thing is going to run out of steam?”, my guess is it probably will. I don’t know if we’re on the verge of that, but, anyway, so I would not be surprised if people look back ten years from now, and say, “Oh, man, all that money companies spent on data centers and power is, that was kind of a waste of money”. But then, like you said, the country needs more power, and at some point, probably, we’ll want to be training really big models and so, if we have a bunch of huge data centers that we can use to train models, probably, we’ll get some value out of that. It’s tech companies spending the money so the social cost is not probably that high.
5. 7% of Book Value; 1x EBITDA; Cash is 2.5x Larger than Market Cap – Dirtcheapstocks
Highlands REIT, Inc. (Ticker HHDS) was created in 2016 when it was spun out of InvenTrust Properties Corp.
HHDS was formed to hold non-core assets of InvenTrust.
Today, HHDS owns 13 apartment houses, 3 retail properties, 1 office property and 1 correctional facility…
…HHDS has:
- $205MM of book value.
- $16.7MM of net operating income (NOI) in 2023.
- $17MM of NOI in 2022.
- $85MM of net debt.
- 57% of NOI generated from multifamily assets
What do you think? Is Highlands worth book value? Is it worth half of book value?
If we want to value the business at an 8 cap, the equity must be worth $124MM.
Within the last two weeks, HHDS has been valued as low as $14.4MM.
That’s less than 1x NOI, and 7% of book value…
…Most companies valued at $14MM might have a few hundred shareholders of record. Apple is valued at $3.5 Trillion, and it has 23,000 record holders.
Highlands has 143,000 record holders…
…Here’s my theory: When Highlands was spun out of InvenTrust, every shareholder was given ownership individually. There are 143,000 separate people/entities that own this stock. And this stock was an afterthought. It was just a few noncore assets being spun out of a $2 billion REIT…
…HHDS, perhaps wanting to ward off future material purchases by Mackenzie, announced a tender offer in October 2023. While Mackenzie was tendering at $0.04/share earlier that summer, HHDS was willing to pay $0.12 – $0.17/share. What’s more, HHDS was committing $20MM to the share buyback.
HHDS would repurchase 13-19% of its shares if fully subscribed.
A few weeks later, HHDS increased the buyback to $25MM!
In the end, $23.7MM was spent to buy in 169MM shares – nearly 20% of the outstanding share count…
…HHDS showed up as an expert market security, even though it’s SEC registered.
But I found that the traditional expert market brokers couldn’t buy shares.
Then I went to alternative market brokers. They’d be happy to take my money, and told me I could get as much volume at $0.10 as my heart desired.
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