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 10 November 2024:

1. Why I’m Leaving OpenAI and What I’m Doing Next – Miles Brundage

So how are OpenAI and the world doing on AGI readiness?

In short, neither OpenAI nor any other frontier lab is ready, and the world is also not ready.

To be clear, I don’t think this is a controversial statement among OpenAI’s leadership, and notably, that’s a different question from whether the company and the world are on track to be ready at the relevant time (though I think the gaps remaining are substantial enough that I’ll be working on AI policy for the rest of my career).

Whether the company and the world are on track for AGI readiness is a complex function of how safety and security culture play out over time (for which recent additions to the board are steps in the right direction), how regulation affects organizational incentives, how various facts about AI capabilities and the difficulty of safety play out, and various other factors.

As a sidenote, I think that AGI is an overloaded phrase that implies more of a binary way of thinking than actually makes sense. One of the things my team has been working on lately is fleshing out the “levels of AI” framework referenced here. I hope that OpenAI and I will be able to publish a related paper before long. But for now I’d just note that when I say “ready for AGI,” I am using this as shorthand for something like “readiness to safely, securely, and beneficially develop, deploy, and govern increasingly capable AI systems.”…

…I think the upsides of AI are already big and could be dramatically bigger, as are the downsides. As someone who has worked in this field for longer than most, it has been very sad to see increasing polarization along the lines of whether people focus on one side of the cost/benefit ledger or the other, or have different risk priorities, etc. My view is that there is a lot to worry about and a lot to be excited about, we don’t have to choose one thing to care about, and we should find common ground where it exists.

I think AI and AGI benefiting all of humanity is not automatic and requires deliberate choices to be made by decision-makers in governments, non-profits, civil society, and industry, and this needs to be informed by robust public discussion. Notably, this is true not just for risk mitigation but also for ensuring equitable distribution of the benefits, as is the case with, e.g., electricity and modern medicine as well. This is true for a few reasons, including, non-exhaustively, collective action problems, various unpriced negative externalities, and unequal starting positions of digital infrastructure access, wealth, etc. that affect who benefits and is harmed by default and to what degrees. As with railroads, electricity, etc., corporate and government policies will be critical to ensuring safe and fair outcomes.

I think AI capabilities are improving very quickly and policymakers need to act more urgently…

..I think quantitative evaluations of AI capabilities and extrapolations thereof, in combination with analysis of the impacts of certain policies, will be critical in truthfully and persuasively demonstrating that urgency. There’s great work happening on measuring frontier models from a safety perspective, measuring trends over time in AI, and a growing body of work assessing the labor market implications of AI, but more is definitely needed.

I think we don’t have all the AI policy ideas we need, and many of the ideas floating around are bad or too vague to be confidently judged. This is particularly true of international competition over AI, where I find the existing proposals to be especially bad (e.g. “race against [competing country] as quickly as possible”) and vague (e.g. “CERN for AI”), although it’s encouraging to see a growing trend towards more nuanced discussion of some of these ideas. There are also many aspects of frontier AI safety and security that will require creative solutions…

…I think that improving frontier AI safety and security is quite urgent, given the number of companies (dozens) that will soon (next few years at most) have systems capable of posing catastrophic risks. Given that that is not much time to set up entirely new institutions, I’m particularly interested in opportunities for action under existing legal authorities, as well as shaping the implementation of already-approved legislation such as the EU AI Act.

As noted above, and explained in more detail in this paper and similar work, companies and governments will not necessarily give AI safety and security the attention it deserves by default (this is not a comment specifically about OpenAI, as discussed above). There are many reasons for this, one of which is a misalignment between private and societal interests, which regulation can help reduce. There are also difficulties around credible commitments to and verification of safety levels, which further incentivize corner-cutting: people assume others are going to cut corners to gain an advantage and can’t tell what the ground truth is, or think they will change their minds later. Corner-cutting occurs across a range of areas, including prevention of harmfully biased and hallucinated outputs as well as investment in preventing the catastrophic risks on the horizon. There are, to be clear, some ways in which commercial incentives encourage safety, though I think it would be irresponsible to assume that those incentives will be sufficient, particularly for ambiguous, novel, diffuse, and/or low-probability/high-magnitude safety risks.

I’m excited about understanding how companies can credibly demonstrate safety while protecting valuable and potentially misusable IP. The difficulty of demonstrating compliance without compromising sensitive information is a major barrier to arms control agreements, which requires innovation to address. This issue is also at the core of effective domestic regulation. I’m excited to collaborate with people working on this and other related technical AI governance questions.

While some think that the right approach to the global AI situation is for democratic countries to race against autocratic countries, I think that having and fostering such a zero-sum mentality increases the likelihood of corner-cutting on safety and security, an attack on Taiwan (given its central role in the AI chip supply chain), and other very bad outcomes. I would like to see academics, companies, civil society, and policymakers work collaboratively to find a way to ensure that Western AI development is not seen as a threat to other countries’ safety or regime stability, so that we can work across borders to solve the very thorny safety and security challenges ahead.

Even if, as I think is very likely, Western countries continue to substantially outcompete China on AI, there is more than enough “gas in the tank” of computing hardware and algorithmic progress in autocratic countries for them to build very sophisticated capabilities, so cooperation will be essential. I realize many people think this sounds naive but I think those people haven’t thought through the situation fully or considered how frequently international cooperation (enabled by foresight, dialogue, and innovation) has been essential to managing catastrophic risks…

…I think it’s likely that in the coming years (not decades), AI could enable sufficient economic growth that an early retirement at a high standard of living is easily achievable (assuming appropriate policies to ensure fair distribution of that bounty). Before that, there will likely be a period in which it is easier to automate tasks that can be done remotely. In the near-term, I worry a lot about AI disrupting opportunities for people who desperately want work, but I think it’s simultaneously true that humanity should eventually remove the obligation to work for a living and that doing so is one of the strongest arguments for building AI and AGI in the first place. Likely some will continue to work in the long-term but the incentive to do so might be weaker than before (whether this is true depends on a variety of cultural and policy factors). That is not something we’re prepared for politically, culturally, or otherwise, and needs to be part of the policy conversation. A naive shift towards a post-work world risks civilizational stagnation (see: WALL-E), and much more thought and debate about this is needed…

…Compared to software, data, and talent, computing hardware has unique properties that make it an important focal point for AI policy: “it is detectable, excludable, and quantifiable, and is produced via an extremely concentrated supply chain” (quoted from this paper I worked on). This makes it worrying that the part of the US government responsible for overseeing what happens when that compute is shipped overseas is severely understaffed and underfunded, and that more generally there is little serious policy discussion of what the endgame is here (besides occasionally tightening export controls and requiring companies to report their big datacenters and training runs).

To the extent that there is serious analysis of compute governance happening in the academic literature, it generally lags behind developments in industry by a fair amount – e.g., to those within frontier AI companies, it has become increasingly clear in recent years that scaling up inference, not just training, can enable higher performance, but public analysis of the policy implications of this has only begun in earnest relatively recently. Ideas for distributing computing power (and the associated benefits of AI) more widely, such as via the government providing greater compute for academics, are generally too little too late and neglect issues specific to the developing world, which is in a quite different situation.

2. Industry Is Not Destiny – Greg Obenshain

We’d go as far as to argue that industry analysis generally is much less valuable than fundamental investors or strategy consultants might hope.

Mauboussin’s new study, Measuring the Moat: Assessing the Magnitude and Sustainability of Value Creation, grapples with this issue. Mauboussin’s study includes a chart that is difficult to unsee once you’ve seen it (h/t Edward Conard’s Macro Roundup for highlighting this)…

…This chart shows that profitability varies more within industry (the vertical bars) than across industries (the dots). Over the long run, the fate of a company is not primarily determined by its industry—a finding consistent with Chicago school research from the 1980s that dealt a death blow to structure-conduct-performance theory in antitrust law.

Mauboussin notes that while industry analysis matters when it comes to deciding where to compete, ultimately the right unit of analysis is not the industry level but the company level…

…Industries with higher overall profitability have more companies that are profitable, but even within industries with low profitability, there are still companies that have returns well above the cost of capital and some companies that have profitability substantially above.

Industry is not destiny. Great companies can emerge from mediocre industries.

3. Watch Out: Wall Street Is Finding New Ways to Slice and Dice Loans – Matt Wirz

Goldman Sachs GS 2.14%increase; green up pointing triangle this month sold $475 million of public asset-backed securitization, or ABS, bonds backed by loans the bank makes to fund managers that tide them over until cash from investors comes in. The first-of-its-kind deal is a lucrative byproduct of the New York bank’s push into loans to investment firms, such as these so-called capital-call lines.

Goldman’s new deal reflects two trends transforming financial markets. Increasingly large managers of private-debt and private-equity funds are moving up in the Wall Street pecking order, but they often need money fast. Banks, once again, are reinventing themselves to adapt…

…The transactions are relatively small for now. Still, they are intertwining banks (in Wall Street parlance, the sell side) with investors (the buy side) in ways that are new and difficult to parse for analysts, regulators and others…

…Capital-call loans function like credit cards for private-fund managers. The funds borrow money to invest quickly in private debt, private equity, real estate and infrastructure. They then “call up” cash commitments from clients in the funds, mostly institutions such as pensions and insurers, and repay the loans when the clients deliver.

Defaults on capital-call commitments from large institutions “have been historically close to 0%,” according to a marketing document for Goldman’s bond viewed by The Wall Street Journal. That makes the bonds extremely safe, said debt fund managers to whom Goldman offered the deal.

Even so, the shiny new products that banks are inventing have yet to be tested through market cycles…

…As Goldman and other banks make more capital-call loans to private-fund managers, they are also buying insurance from many of the same investment firms to protect against potential losses from corporate, consumer and real-estate loans. The so-called synthetic risk transfers, or SRTs, help banks reduce risk to meet new regulatory requirements and give fund managers investments to put into their wildly popular private-credit funds.

Some private-credit funds are developing another product that is similar to capital-call lines called net-asset-value, or NAV loans, made to private-equity fund managers. Rising interest rates have made it harder for private-equity funds to sell companies they own to repay their limited partners. NAV loans help them to start returning cash to clients until they can dispose of the companies. Many of the firms that manage private-equity funds also manage private-credit funds…

…The International Monetary Fund published a report in April warning that “interconnections and potential contagion risks many large financial institutions face from exposures to the asset class are poorly understood and highly opaque.”

4. Big Banks Cook Up New Way to Unload Risk – Matt Wirz

U.S. banks have found a new way to unload risk as they scramble to adapt to tighter regulations and rising interest rates…

…These so-called synthetic risk transfers are expensive for banks but less costly than taking the full capital charges on the underlying assets. They are lucrative for the investors, who can typically get returns of around 15% or more, according to the people familiar with the transactions.

U.S. banks mostly stayed out of the market until this autumn, when they issued a record quantity as a way to ease their mounting regulatory burden…

…In most of these risk transfers, investors pay cash for credit-linked notes or credit derivatives issued by the banks. The notes and derivatives amount to roughly 10% of the loan portfolios being de-risked. Investors collect interest in exchange for shouldering losses if borrowers of up to about 10% of the pooled loans default…

…The deals function somewhat like an insurance policy, with the banks paying interest instead of premiums. By lowering potential loss exposure, the transfers reduce the amount of capital banks are required to hold against their loans.

Banks globally will likely transfer risk tied to about $200 billion of loans this year, up from about $160 billion in 2022, according to a Wall Street Journal analysis of estimates by ArrowMark Partners, a Denver-based firm that invests in risk transfers…

…Banks started using synthetic risk transfers about 20 years ago, but they were rarely used in the U.S. after the 2008-09 financial crisis. Complex credit transactions became harder to get past U.S. bank regulators, in part because similar instruments called credit-default swaps amplified contagion when Lehman Brothers failed.

Regulators in Europe and Canada set clear guidelines for the use of synthetic risk transfers after the crisis. They also set higher capital charges in rules known as Basel III, prompting European and Canadian banks to start using synthetic risk transfers regularly.

U.S. regulations have been more conservative. Around 2020, the Federal Reserve declined requests for capital relief from U.S. banks that wanted to use a type of synthetic risk transfer commonly used in Europe. The Fed determined they didn’t meet the letter of its rules…

…The pressure began to ease this year when the Fed signaled a new stance. The regulator said it would review requests to approve the type of risk transfer on a case-by-case basis but stopped short of adopting the European approach.

5. Xi Stimulus Clues Found in Protest Data Showing Economic Stress – Rebecca Choong Wilkins

From a basement in Calgary, often accompanied by his pet cat, Lu Yuyu spends 10 hours a day scouring the internet to compile stats on social instability before they are scrubbed by China’s censors. The 47-year-old exile won’t reveal his exact method because it risks jeopardizing the overall goal of the project called “Yesterday,” which documents cases of group protests.

“These records provide an important basis for people to understand the truth of this period of history,” said Lu, who started the effort in January 2023 but didn’t make it public until he arrived in Canada a year ago. “I didn’t want to go to jail again,” he explained.

While Lu’s interests are political, his database — available for free — is among a growing number of metrics tracking dissent in China that investors are watching to figure out when Xi will open up the spigots to bolster growth. And some banks are now starting to develop similar products.

Morgan Stanley in September debuted a new gauge of distress that could be used to predict policy swings in China. Robin Xing, the bank’s chief China economist, says it’s nearing the low levels reached two other times in the past decade: in 2015, when Beijing took drastic steps to arrest a $7 trillion stock market rout, and in 2022 — the point at which the Communist Party abruptly dropped its strict Covid controls after simultaneous street protests in major cities…

…While China’s opaque political system makes it difficult to attribute policy moves to any single factor, investors and analysts who track instances of unrest say authorities may be especially sensitive to them when deciding on whether to roll out stimulus and how much to deploy. Economic protests have become more frequent in recent years as China’s youth unemployment rate soared and its housing crisis worsened…

…Getting a read on what’s happening on the ground is a challenge for academic researchers and finance professionals alike. Widespread censorship, heavy surveillance and suppression of dissent have made it hard to assess the depth of economic malaise in the country of 1.4 billion people…

…The rising prominence of dissent metrics is part of a blossoming industry of so-called alternative data aimed at decoding the state of the world’s second-biggest economy…

…Life has become tougher for many in recent years as pandemic lockdowns, a real estate crisis and trade tensions have slowed growth in China.

Incomes are still rising, but gains under Xi have been the weakest since the late 1980s. Faith in the country’s meritocracy also appears to be waning, leaving white-collar workers feeling increasingly disillusioned. Companies mired in fierce price wars are laying off employers, while college graduates are struggling to find work.

China Dissent Monitor’s data shows that cases of dissent rose 18% in the second quarter compared to same period last year, with the majority of events linked to financial issues.

“If you look at everything regarding social well-being — be it wage growth, urban unemployment rate, consumer confidence and even tracking labor incidents — I think it’s deteriorating,” Morgan Stanley’s Xing said.

Although protests aren’t particularly rare in China, they’re typically small scale, uncoordinated with other places and lacking in overt criticism of Beijing. Still, political criticism can bubble up, usually in cases linked to rural land actions where the local governments find themselves the target of discontent, according to China Dissent Monitor research…

…Even so, there are few signs that the unrest is coalescing around a particular instance of perceived injustice or a single issue. Unlike the Tiananmen Square protests and unrest in the late 1980s, current dissent doesn’t present an existential threat to the regime. A more likely response is therefore a dose of economic medicine that will keep the market guessing.

Disclaimer: The Good Investors is the personal investing blog of two simple guys who are passionate about educating Singaporeans about stock market investing. By using this Site, you specifically agree that none of the information provided constitutes financial, investment, or other professional advice. It is only intended to provide education. Speak with a professional before making important decisions about your money, your professional life, or even your personal life. We currently have no vested interest in any companies mentioned. Holdings are subject to change at any time.

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 27 October 2024:

1. China’s Fiscal Policy Update – Leonid Mironov

Ministry of Finance top brass spoke at a press briefing, and outlined the extent of the Fiscal policy support they can offer *within the confines of the current budget*. However, while the steps laid out suggest a cautious and structured approach, notable gaps in specific figures leave room for market speculation…

…China is set to enhance its strategy for managing local government debt, which remains a critical issue. The central government will issue large-scale debt swaps, a move aimed at addressing the opaque “hidden debt” local authorities have accumulated off the books. Local governments still hold 2.3 trillion yuan in available funds, providing some breathing room to manage obligations in the final quarter of 2024. These steps aim to steady the debt situation, though the path forward will undoubtedly be closely watched…

…There is commentary out there to say that this is not new spending, I would counter with that yes, its not new per se, but its spending that would go in to this gap (authorised/unspent) but won’t anymore. So this is stimulative…

…With property markets showing persistent weakness, local governments now have the authority to deploy funds from special bonds to purchase unsold homes. These homes will be converted into subsidized housing—a dual-purpose measure to both alleviate property inventory and address housing affordability. It signals a nuanced, albeit gradual, approach to propping up the beleaguered real estate sector.

This is likely where most of that 2.3trn RMB mentioned in (1) will go. Again since the the property market is such a significant drag on the economy, this is reasonable…

…In line with recent People’s Bank of China (PBOC) directives, four major state-owned banks announced forthcoming cuts to existing mortgage rates. These rate reductions, effective from October 25, are part of broader efforts to ease financial pressures on households and further stimulate economic activity. Again given the sheer amount of total mortgages outstanding (38 trn RMB at the end of ‘23, see chart), this is significant. PBOC expects an effective cut of about 50pbs on average…

…Perhaps the most telling aspect of the press conference was what remained unsaid. There were no specifics on the magnitude of additional fiscal stimulus or further bond issuances. Additionally, there was no precise indication of how much the fiscal deficit might increase—a critical piece of information many market participants were hoping for…

…The Ministry of Finance’s approach at this juncture reflects a cautious yet deliberate strategy. While existing resources are being leveraged, and flexibility is maintained, major new initiatives have not yet been unveiled. All eyes now turn to the late October NPC meeting, where the prospect of more significant fiscal interventions could reshape the economic landscape for the year ahead.

2. The Bitter Lesson – Rich Sutton

The biggest lesson that can be read from 70 years of AI research is that general methods that leverage computation are ultimately the most effective, and by a large margin. The ultimate reason for this is Moore’s law, or rather its generalization of continued exponentially falling cost per unit of computation. Most AI research has been conducted as if the computation available to the agent were constant (in which case leveraging human knowledge would be one of the only ways to improve performance) but, over a slightly longer time than a typical research project, massively more computation inevitably becomes available. Seeking an improvement that makes a difference in the shorter term, researchers seek to leverage their human knowledge of the domain, but the only thing that matters in the long run is the leveraging of computation. These two need not run counter to each other, but in practice they tend to. Time spent on one is time not spent on the other. There are psychological commitments to investment in one approach or the other. And the human-knowledge approach tends to complicate methods in ways that make them less suited to taking advantage of general methods leveraging computation…

…We have to learn the bitter lesson that building in how we think we think does not work in the long run. The bitter lesson is based on the historical observations that 1) AI researchers have often tried to build knowledge into their agents, 2) this always helps in the short term, and is personally satisfying to the researcher, but 3) in the long run it plateaus and even inhibits further progress, and 4) breakthrough progress eventually arrives by an opposing approach based on scaling computation by search and learning. The eventual success is tinged with bitterness, and often incompletely digested, because it is success over a favored, human-centric approach.

One thing that should be learned from the bitter lesson is the great power of general purpose methods, of methods that continue to scale with increased computation even as the available computation becomes very great. The two methods that seem to scale arbitrarily in this way are search and learning.

The second general point to be learned from the bitter lesson is that the actual contents of minds are tremendously, irredeemably complex; we should stop trying to find simple ways to think about the contents of minds, such as simple ways to think about space, objects, multiple agents, or symmetries. All these are part of the arbitrary, intrinsically-complex, outside world. They are not what should be built in, as their complexity is endless; instead we should build in only the meta-methods that can find and capture this arbitrary complexity. Essential to these methods is that they can find good approximations, but the search for them should be by our methods, not by us. We want AI agents that can discover like we can, not which contain what we have discovered. Building in our discoveries only makes it harder to see how the discovering process can be done.

3. Austan Goolsbee Explains the Fed’s Big Rate Cut – Tracy Alloway, Joe Weisenthal, and Austan Goolsbee

Joe (12:30):

You mentioned lags. I want to ask you a question about that. When the Fed started jacking up rates aggressively, one of the theories for why it didn’t have a sharper impact on the economy is that so many households and corporations had in, say, 2020, first half of 2021, termed out their debt and so there was not a lot of sensitivity to debt.

The flip side of that now — and people have been writing about this — is that even though the Fed has now commenced a cutting cycle, that the weighted average cost of debt is probably going to rise in 2025 basically just mathematically, right? Because eventually that’ll have to be refi-ed at higher rates and so forth. How do you think about that dynamic now when you’re thinking about these lags? You’re starting a cutting cycle, but at the same time probably cost of debt is actually going to rise for a fair number of economic actors in this economy.

Austan (13:19):

You have remarked on this subject and thought it through. In my world that goes into the economic conditions and there are many things that have made this a hairy, strange time for central banks because the business cycle, both down and up, looked almost nothing like historical precedents. This is one aspect of that.

We’ve analyzed this specifically thinking about mortgages. Okay, so if I had told you, the premise of your question, I a hundred percent agree with, six years ago, if you said ‘The Fed is going to raise 500 basis points in a single year, what is going to happen?’ I think most all economists would say ‘Yikes, there’s going to be a major, major contraction and it’s going to be concentrated. Autos down the tubes. Consumer durables, bye-bye. Business fixed investment construction, all going to collapse because they’re very interest rate sensitive.’

We didn’t really see the economy go into the steepness of collapse that you would’ve expected. And so that brings us back to this question. It’s kind of a twofold. Is there something about this unusual business cycle that makes economic activity less sensitive to the interest rate? Or is there something strange about this moment that the lag effect is longer. And it can be both and they can run together, but in the case of mortgages, one of the things that has made monetary policy transmission less direct, is the fact that a vastly higher share of mortgages are 30-year fixed mortgages now, than they were in 2005, 2009, whenever you want to look at.

And so when they change the interest rate — in some countries virtually all mortgages are adjustable rate mortgages. So when their central bank raises rates, they bring out parents onto TV, ‘The central bank is killing us. You know, our mortgage payment went up.’

In the US, if everybody’s on a 30-year fixed, in a way that’s just a delay, but it’s a 30-year delay. So I do think that notion that there are companies that don’t have a lot of debt so they aren’t as especially sensitive to the interest rate, that the term structure of their debt may be such that the average rates they’re paying might even be higher as the Fed cuts. I think that’s not a problem, that’s just a fact and we just need to understand it and see what the magnitude is…

…Tracy (16:29):

Yeah, but my question is going to be ultra simplistic. Can you explain to us in excruciating detail what exactly you expect happens in the economy now, as you cut interest rates? How does that cut get transmitted?

Austan (16:45):

Oof. Okay, as a general matter, the Fed has only one tool really, which is a screwdriver that can tighten or can loosen and I always say if your problem is, you know, a loose fender, that’s great. If your problem is can you make breakfast? No, you kind of can’t do that with a screwdriver.

So the main channels of monetary policy impact on the economy, I think are on the real economy side and they are on interest rate sensitive parts of the economy — like consumer durables, business fixed, investment construction, things like that.

Now there are other channels of monetary transmission where there’s a lot of argument. How important are they and they are, well if you change the value of assets, like the value of housing, the value of stocks, etc., is there a wealth effect so that consumer spending might go up as the asset values go up. Or if you contract and asset values go down, would that limit spending?

There’s a dollar channel that if rates in the US are moving relative to how rates are moving in other places, can affect the currency and that could affect imports and exports.

Those are probably a lot of the main channels and it’s always in the counterfactual. What would be happening if we didn’t do this? So to the extent that there’s already a debt structure or to the extent that we went through a business cycle that for the first time ever was not driven by cyclical industries, but was driven by services because nobody could spend money on that, and services aren’t especially interest rates sensitive, that’s another reason why you might think the monetary transmission mechanism, which is actually a whole bunch of different transmission mechanisms, just looks different this time than before.

Now everything that looks different is not bad. Okay, in a way this is frustrating that monetary policy doesn’t have the same impact, but at the same time in 2023 we hit what I called the golden path. Inflation came down almost as much as it ever came down in a single year, and there was no recession. And that never happened before. And so the unusualness of this thing, sometimes it’s good!…

…Austan (21:27):

Yes, does not necessarily. I agree with [that]. So let me finish two thoughts. One, did the Fed have anything to do with it? That’s kind of the question. If it was all supply shocks, then the Fed didn’t really, yes, the Fed can’t be blamed for the inflation going up, but then the Fed shouldn’t take credit for it coming down.

There is some component that as supply shocks heal, you get immaculate disinflation. I do think that the fundamentally different thing that happened this time than the last time we were getting supply shocks, like at the end of the 70s, is that the market expectations of inflation basically never went up. In the 70s, as actual inflation went up, the expectations went up. And part of what made the Volcker experience so hard is you didn’t have to just slay the inflation dragon. You had to go convince everyone that we will hold this thing underwater for as long as it takes until it surrenders and that’s a brutal process.

I do think that expectations stayed — even as actual inflation was almost double digits — stayed exactly at PCE 2% as the inflation target said, was fundamentally the Fed making a promise it may look bad but we’re going to get it back, and that the market de facto believed it. And that is to the Fed, is about Fed credibility, and I do think it made a big difference…

…Tracy (38:43):

That was perfect. Can I ask one more serious question before we wind it down? But you talked about restrictiveness earlier in this conversation and I get where that comes from and people look at things like real yields and stuff.

But if you look at stock market prices, we’re recording this on October 9th, I think stock indices are at records again. If you look at credit spreads, those are at multi-year lows. Where’s the restrictiveness? Because I don’t see it in parts of the financial market, let me put it that way.

Austan (39:14):

I’d say two things. I told you, my focus is primarily on the real side of the economy. I think those are the biggest, most impactful parts of the monetary policy transmission mechanism, historically.

So I’m less of a fan of interpreting financial conditions indices as a measure of monetary restrictiveness or what monetary policy should do because, in my view, it’s got a major reflection problem that, let’s say the market, which is forward-looking, decides they think it’s going to work, that there will be a soft landing, that rates are going to come down because inflation has been tamed and is at 2%. Then equity markets go up, long rates would come down and that would then be interpreted as a loosening of financial conditions and it would be like ‘Oh, you better stop cutting, you better raise.’ But that’s just self-referential. So I think that’s a little problematic.

And the inverted yield curve, for two years, which everybody has been saying is an indication that there’s about to be a recession, that’s not normal. If we go back to a regularly-shaped yield curve like we’re in more normal conditions, that’s not the end of the world.

My view of restrictiveness is we set the Fed funds rate, we set it high and held it there for more than a year and as inflation came down, the real Fed Funds rate just kept going up, passive tightening. That’s the highest the real Fed Funds rate had been in decades. And so to me that’s where the restrictiveness is.

4. Investing lessons from a mini-Berkshire Hathaway – Chin Hui Leong

Gayner believes mistakes of omission are far more costly than mistakes of commission.

He shared a personal example of passing on investing in Berkshire Hathaway (A shares) in 1984 when he first discovered the company.

At the start of 1984, shares were trading at around US$1,300. By the time he got around to buying some shares, the stock price had risen to US$5,750. Hence, he missed out on a gain of over 340 per cent.

I’ll add a second lesson to his point.

Shares of Berkshire Hathaway (A shares) closed at nearly US$694,000 per share last Friday. In other words, even though Gayner did not invest earlier, his shares are worth about 120 times more than what he paid.

While his returns could have been over 530 times if he invested earlier, I don’t think anyone would lose a smile with a 120-fold return.

So, here’s my take: if you find a great company with a promising future, it may not be too late to invest, even if the stock has already appreciated…

…When selecting stocks to invest, Gayner looks for four key factors.

The first is about finding a profitable business with minimal to no debt and a good return on capital. The reason is clear; starting with this pool of stocks increases your chances of finding a winner.

Secondly, he wants to have a talented management team with integrity.

Gayner may have taken a leaf out of Buffett’s playbook here. As Buffett once said, without integrity, the other positive management qualities, will work against you.

Interestingly, Gayner also connected the use of debt with management’s character.

For him, debt is a character marker.

In a podcast recorded earlier this year, Gayner recalled the advice of Shelby Davis, another legendary investor and mentor. Davis pointed out that in the absence of knowledge about a new business, the use of debt can be telltale sign.

Simply said, if a business is entirely equity-financed, the management team will have no incentive to steal from their own funds.

To be sure, this does not mean that a debt-laden company is fraudulent.

However, Gayner argued that leverage creates conditions for a dishonest management team to exploit since the money does not belong to them.

5. A Message From the Past (Thoughts on Nostalgia) – Morgan Housel

I was recently asked at a conference how investors should feel about the stock market given that it’s basically gone straight up over the last 15 years.

My first thought was: you’re right. If you started investing 15 years ago and checked your account for the first time, you would gasp. You’ve made a fortune.

Then I thought, wait a minute. Straight up for the last 15 years? To echo my wife: What are you talking about?

Are we going to pretend like the 22% crash in the summer of 2011 never happened?

Are we supposed to forget that stocks plunged more than 20% in 2016, and again in 2018?

Are we – hello? – now pretending that the worst economic calamity since the Great Depression didn’t happen in 2020?

That Europe’s banking system nearly collapsed?

That wages were stagnant?

That America’s national debt was downgraded?

Are we now forgetting that at virtually every moment of the last 15 years, smart people argued that the market was overvalued, recession was near, hyperinflation was around the corner, the country was bankrupt, the numbers were manipulated, the dollar was worthless, on and on?

I think we forget these things because we now know how the story ends: the stock market went up a lot. If you held on tight, none of those past events mattered. So it’s easy to discount – even ignore – how they felt at the time. You think back and say, “That was so easy, money was free, the market went straight up.” Even if few people actually felt that way during the last 15 years.

So much of what matters in investing – this is true for a lot of things in life – is how you manage the psychology of uncertainty. The problem with looking back with hindsight is that nothing is uncertain. You think no one had anything to worry about, because most of what they were worrying about eventually came to pass.

“You should have been happy and calm, given where things ended up,” you say to your past self. But your past self had no idea where things would end up. Uncertainty dictates nearly everything in the current moment, but looking back we pretend it never existed.

Disclaimer: The Good Investors is the personal investing blog of two simple guys who are passionate about educating Singaporeans about stock market investing. By using this Site, you specifically agree that none of the information provided constitutes financial, investment, or other professional advice. It is only intended to provide education. Speak with a professional before making important decisions about your money, your professional life, or even your personal life. We currently have a vested interest in Markel (Tom Gayner is the CEO of Markel). Holdings are subject to change at any time.

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 20 October 2024:

1. Actual Reform has materialised – Leonid Mironov

The Ministry of Justice and the NDRC have put out the draft of the Law on Private companies, or to give it’s full name, People’s Republic of China Private Economy Promotion Law. And it’s really good one…

…It emphasizes innovation, technological advancement, and participation in strategic industries, while also providing improved legal protections and equal treatment to address longstanding concerns. In return, private businesses are expected to follow Party leadership, contribute to national development, and operate in compliance with laws and regulations…

…The takeaway is that the private enterprises are now not discriminated against in the key project deployment. They will have similar cost of capital to the SOEs and will be able to supply most major national projects.

Chinese SOEs have been told to get more competitive earlier in the year, now the playing field is being somewhat levelled. The government, to my mind, is taking onboard the idea that employment is employment, whether SOE or not, and if a private enterprise can provide it, its fine.

I honestly think that this is the most consequential announcement, as it’s an example of a long-term reform that the government has committed to, and it is carrying out. This gives us hope for land and hukou reforms, as well as pension reform eventually. But also, this is a sign that there no decision to increase direct state participation in the economy but rather, assuming that companies follow guidance form the CCP, the more efficient actors, whether private or SOE, will drive the new policies.

2. Becoming Berkshire: 1969 – Illinois National Bank – The Weekend Investor

Around this time, Buffett and Munger sought a bank to purchase and found a candidate in Rockford, Illinois.

On April 3, 1969, Berskhire Hathaway, Inc. acquired 81,989 shares, out of a total of 100,000 shares outstanding, of the common stock of the Illinois National Bank and Trust Co. of Rockford, Illinois, at a cash price of $190.00 per share. They also have made a tender offer to acquire the remaining outstanding shares at the same cash price.

Buffett considered Rockford Bank one of the most well-run and profitable he had ever seen. It was managed by Eugene Abegg, who was 71 years old.

Abegg, who owned one-quarter of the shares, had been negotiating to sell the business to someone else before Buffett came along. The potential buyer had started criticizing the deal and wanted an audit. This affected Abegg, and he decided not to go ahead with the deal. Meanwhile, Buffett worked out what he was willing to pay, which turned out to be about $1 million less than the other buyers.

Abegg was so fed up with the other bidders that he pressured his fellow shareholders to accept Buffett’s offer, threatening to resign if they did not.

The crusty Abegg was just the type of fellow that Buffett liked.

In 1931, Eugene Abegg, a young man with only $250,000 of capital, formed a bank in Rockford, Illinois… It had $400,000 of deposits. Since then, no new capital had been added to the bank by its owners. Nevertheless, by 1969, Abegg had built, piece by piece, a bank with a net worth of $17m and $100m of deposits.

He carried thousands of dollars of cash in his pocket and cashed checks for people on the weekends. He carried a list of the number of unrented safe deposit boxes with him everywhere and would try to rent you a safe deposit box at a cocktail party. Mind you, this is the biggest bank in the second-largest city in Illinois at that time…

…The Illinois National Bank, which Buffett soon came to refer to by its colloquial name of Rockford Bank, had been chartered in the days before the U.S. Treasury assumed the exclusive right to coin money. Buffett was fascinated to discover that it still issued its own currency. The ten-dollar bills featured Abegg’s picture. Buffett, whose net worth was now more than $26 million, could have bought almost anything he wanted, but not this. Gene Abegg had done him one better. He and the United States Treasury had the privilege of issuing their own currency, but not the Buffett Partnership or Berkshire Hathaway. The idea of legal tender with your own picture on it captivated him. He began carrying a Rockford bill in his wallet…

…Berkshire paid $190 per share to acquire Illinois National, plus $2 per share to an investment bank for services rendered in the transaction…

…With total Assets of $117.3 million, shareholder equity of $16.8 million, and a net profit of $1.7 million in 1968, the bank had an ROE of 10% and ROA of 1.4%.

3. Jigar Shah on the Nuclear Power Revival in the US – Tracy Alloway, Joe Weisenthal, and Jigar Shah

Joe (04:30):

Thank you so much. So I’m going to start off with this question, which is: Okay, we went for a long time basically without building new nuclear power plants. It’s starting to pick up again. How much is it because something has changed policy-wise with subsidies and tax credits, et cetera, versus demand is back, therefore the economics of nuclear makes sense? Or would you say it’s not binary?

Jigar (04:53):

Well, look, I think that when you think about what happened through a historical context in the 1970s, we had high inflation and nuclear power was subject to high inflation. And so part of this is people were already worried about building new nuclear plants before the incident occurred because things were just getting more expensive. And when you think about the utility bankruptcies that occurred way back when, it was because they had cost overruns on nuclear power. And so I think that in general, it goes to when America stopped believing in itself and its ability to do big things and infrastructure. And I think this moment, with load growth and with the president saying we are going to build big things here, has gotten people thinking again, “Hey, what would it take to actually figure this out this time around?”

Tracy (05:48):

This is actually exactly what I wanted to ask you about because I was reading that the initial construction cost for Unit One of Three Mile was about $400 million. And I guess today the cost of building a nuclear plant would be like $5 billion, $10 billion. Obviously the $400 million isn’t adjusted for 1960s prices, but it does seem in general like it’s more expensive to build nuclear plants, certainly since the 1960s. Where did that additional cost increase actually come from?

Jigar (06:23):

So when you think about building things… like if you were to build multifamily housing and you would build one multifamily housing building versus building 12 throughout the city. You can imagine if you’re using the same design, it would be cheaper. The workers would get better. The first one would cost more, the second one would cost less, the third one would be even less.

You get faster. I mean, you see that when you go into a new home construction place. The first home takes it seems a lot longer and then suddenly the homes start popping up every week. This is the same with nuclear power. We trained 13,000 people to build the Vogtle nuclear plant in Georgia, and then we were done. And where did all those workers go? To other jobs. So now if we wanted to build Units Five and Six — we wanted to rebuild V.C. Summer [Nuclear Station] in South Carolina, which is like a hundred miles away — we’d have to go out and find another 13,000 workers. And so one of the things that we have to figure out how to do is to figure out how to build 10, right? And have those same workers that we trained, all those same EPC [engineering, procurement, and construction] contractors, all of those same suppliers, not have to stop and start, but we continue to do these one-off things…

…Jigar (09:42):

So when you restart a nuclear plant, the nuclear plant is viewed as new additional capacity, right? Because it was shut down. And so as a result, this technology agnostic credit that was created by Senator Wyden, right?

Because remember we always had the solar tax credit and the wind tax credit and all these other things. So over time the IRA moves us to a technology-neutral tax credit so that everything that is clean gets this technology-neutral tax credit. It’s a pretty lucrative tax credit. Depends on the technology, but let’s say 3 cents a kilowatt hour. And so now you’re in this place where you actually have a bonus production credit. Now you separately can choose to get an investment tax credit, but it happens to be that the production tax credit is more lucrative for these restarts of nuclear plants. But if you decide to do the investment tax credit, then you get the 30% tax credit, then there’s bonus tax credit.

So if it’s part of an energy community, you get an extra 10%, right? If you have a lot of domestic content, you get another 10%, right? So you could imagine that some of the folks who are building brand new nuclear plants might go that direction, but as a result of these incentives, nuclear power is now very cost effective.

Then the question becomes who actually wants to buy this power? Because wholesale market prices have been low. And so then the question becomes who wants to buy it? And it happened to be that two different utility groups in Michigan competed over wanting to buy all the output out of the Palisades restart. And so he picked one of the groups to buy that power and then that led to the project becoming financeable, right? And so once that succeeded, then Constellation was like, hell, maybe we could do this…

…Jigar (11:49):

So for a restart, you generally choose a production tax credit, not the investment tax credit. And that’s because the cost of restarting a reactor is a lot lower than the cost of building a brand new reactor. So you make more money by getting that extra 3 cents a kilowatt hour for the next 20 years. So the math there is you put up, it depends on where the final cost runs out, but let’s call it $1 [billion] to $2 billion to do the restart. And then you get this 3 cents a kilowatt hour multiplied by the number of kilowatt hours that plant creates. And remember, a nuclear power plant runs on average, in the United States, 92% of the time. So that’s a lot of kilowatt hours that comes out of that plant. Whereas with a solar farm, you might get 25% of the time production, with a tracking system. The math means that you could get almost all of your money back on the $1-to-$2 billion from the tax credits.

Then you’ve got the sale of the power that you’re signing a long-term contract for, and that’s where you make your return…

…Jigar (14:42):

Into the PJM [Interconnection.] And Microsoft says, depending on what happens with this power, we will make you whole on the payment. So if we said that we’re going to pay 9 cents a kilowatt hour and you end up getting 7 cents a kilowatt hour, we’ll pay that 2-cent difference. And that includes not just the kilowatt hour price, but also includes the capacity payment. So you may have heard that the PJM had a very large increase in the price that the capacity payment cleared and the capacity payment is essential, because it convinces the coal plants or the natural gas plants or others who are sort of at the end of life to make investments to last a little longer because they got paid a capacity payment to stay open. So the pieces that come here are both a capacity payment and the energy payment, and Microsoft is saying that we get all the attributes, so we get to call our usage green, but separately, if for whatever reason the wholesale market value for what the nuclear power plant is creating is less than the strike price that we agreed to, then we will make you whole…

…Jigar (19:34):

It really is an extraordinary thing. I think that most people view electricity like water. So you just put a bigger pump in, you put in more pipe, it gets to your house, you got hot water, that’s great. It’s not like that at all. There is this complex physics equation that you have to solve for.

Joe (19:53):

Because the grid has to be in perfect balance all the time, right?

Jigar (19:56):

Well, so there’s the perfect balance between supply and demand. But then there’s also figuring out what the constraints are of each individual segment on the transmission line.

So if you’re using power in New York City and you’re creating a lot of extra power out of the nuclear plants in Illinois, then that power has to go via Indiana and Ohio and then through Pennsylvania to New York City, and they may or may not be able to carry that much. And so they have to do these studies. So every time you try to add something to the grid, they have to do a study and they have to figure out whether that capacity is there, how often it’s there, whether it would continue to be balanced or whether it would be imbalanced.

And so the big fight there is that… so in Texas what they do is they just look at the safety part of it, but they don’t look at the capacity part of it. They just say, “You connect at your own risk and if we’re clogged, we’re just going to tell you to shut down, and that’s on you.” That’s why they’re approving people super fast. Whereas with the PJM and others, they’re saying, “Not only are we’re going to do a safety study, we’re also going to do a capacity study and we’re not going to let you connect until this other generator shuts down and frees up capacity for your generator.” And so that then makes the wait time much longer.

4. Invest Local? – Victaurs

Well, a community bank in the U.S. is generally defined as a depository or lending institution that primarily serves businesses and individuals in a small geographic area. These banks emphasize personal relationships with their customers and often have specialized knowledge of their local community and customers. They tend to base credit decisions on local knowledge and nonstandard data obtained through long-term relationships, rather than relying solely on models-based underwriting used by larger banks…

…As of a year or two ago there were roughly $25 trillion in assets in the entire U.S. Banking system.

And the entire amount of assets in the Community Banking system is … drumroll please … $4.8 trillion for a grand total of 19.2%. Only 1/5 of all the assets in the system are controlled and managed by these smaller banks…

...When people don’t bank locally, they inadvertently contribute to a cycle that can harm their local economies:

Capital Drain: Deposits in non-local banks are often invested in national or international ventures, rather than being reinvested in the local community

Reduced Access to Credit: As community banks disappear, so does their deep understanding of local economic conditions and business opportunities.

Loss of Personalized Service: Large banks often use standardized lending criteria that may not account for local economic conditions or individual circumstances.

Economic Homogenization: As local banks disappear, communities lose a key institution that helps maintain their unique economic character.

Decreased Local Decision-Making: When banking decisions are made in distant headquarters, local economic needs and opportunities may be overlooked.

I don’t want to over dramatize the situation, but do any of these things sound good to you? And given lots of us grew up in small towns, love where we came from, owe our position in life to the kindness of a HS coach or the first job at a local restaurant, do you want capital to move away from these people? I don’t think I do.

Banking is numbers, so here are some numbers because they paint a stark picture:

• For every $100 deposited in a local bank, $58 is reinvested locally. For large banks, that number drops to just $36. This isn’t to demonize big banks, only to point out the facts.
• Community banks make 60% of small business loans, despite holding only 12% of all banking assets. (I know their 12% doesn’t jive with my 19%).
• When a community bank closes, the local area experiences an average 33% reduction in small business lending for several years. I highly recommend checking out this study. This is an awful second level impact of losing community banks…

…As of 2023, the United States is home to a staggering 33.2 million small businesses. These enterprises employ 61.7 million people – that’s 46.4% of all U.S. employees. To put it in perspective, if small business employees formed a country, it would be the 23rd most populous nation on Earth, just behind Italy. That was pretty crazy to me. Imagine if all of the small businesses went away?

But it doesn’t stop there. Small businesses are the dynamos of American innovation and economic activity:

• They generate 44% of U.S. economic activity.
• They create 1.5 million jobs annually (64% of new jobs created) – that’s like creating a new city the size of Philadelphia every year, filled entirely with new job holders. And even for those of us who aren’t Eagles or Phillies fans, we can agree this is a massive deal.
• They contribute to 33.6% of known export value and represent 97.5% of all exporters in the United States.

“Small businesses are more than just economic units,” says Dr. Emily Chen, economist at the Small Business Administration. “They’re the innovation labs of America, constantly adapting and evolving to meet new challenges and opportunities.”…

…This is a repeat stat, but worth mentioning again. Community banks provide 60% of all small business loans, despite holding only 12% of all banking industry assets. It’s as if the local high school football team was outscoring all the pro teams combined!

They make 80% of agricultural loans, forming the financial backbone of rural America.

During the COVID-19 pandemic, community banks processed 57.5% of all Paycheck Protection Program (PPP) loans, saving countless small businesses.

Community banks have over 50,000 locations nationwide, compared to about 18,000 locations for the largest banks. That’s like having a friendly neighbor on every block, compared to a distant acquaintance every few neighborhoods…

…Community banks have consistently demonstrated resilience in the face of economic challenges:

During the 2008 financial crisis, community banks continued to lend when larger banks pulled back, increasing their small business lending by 5.2%.

In the recent COVID-19 pandemic, community banks were often the first to step up, offering forbearance and emergency loans to struggling local businesses.

66% of small businesses that received PPP loans from community banks said the process was “easy,” compared to 51% for large banks…

…In 2005, Hamdi Ulukaya bought a defunct yogurt factory in New Berlin, New York, with the help of a Small Business Administration loan backed by a local bank. From this modest beginning, Chobani has grown into a billion-dollar company, employing thousands and revolutionizing the yogurt industry.

“Without that initial loan and the trust of our local bank, Chobani might never have existed,” Ulukaya has said. “They believed in us when no one else would.”

5. The next tectonic shift in AI: Inference – Rihard Jarc

To simplify it, the o1 model has a backtracking ability. The model predicts something, realizes it did something wrong, goes back, erases that, and comes back and predicts again from that point.

The most significant implication of this kind of model is that inference workloads should grow substantially more than we were expecting in the pre-o1 period.

The calculation for Inference is now not just the number of users using it multiplied by the number of times they use it. The model can now take 10x or even more time on inference compute to come up with an answer. So inference also becomes part of the accuracy process.

The second big implication for investors is that inference computing is now becoming a new scaling paradigm. So, you not only scale the model with what is now known as data and training compute, but you can also scale them with more inference.

Noam Brown, an OpenAI researcher, has said that a study on the board game Hex using AI found that if you have 15x the inference compute, it equals 10x the training compute.

The fact that you can now scale LLMs via inference means that:

A. You can have smaller models that you dedicate more inference compute that can be as good as bigger parameter models with less inference compute

B. Inference computing is much cheaper than training computing, but the market for inference will be vastly bigger than training computing. In my discussion with Sunny, I asked Sunny how big he thinks, as an industry insider, the Inference market will be; Sunny revealed that he had the chance to preview an interview with Jensen Huang, the CEO of Nvidia, where Jensen said that Inference will be 1 billion times larger than Training. Sunny added that it makes sense to think that a model is going to be used billions of times before it is updated (trained) again.

It is also important to note that the Inference chip market has much more competition than the training market, where Nvidia dominates. From an industry expert:

»Training also is notoriously hard because you need special architectures and special cards and interconnects between the clusterand RDUs and stuff like that. It’s mostly dominated by NVIDIA because they’ve done the best work there. Inference is interesting because inference can be done anywhere. Inference is very, very easy to do on any hardware. Training is harder.«

This means that other companies will be able to reap the benefits of inference chips besides Nvidia. It also means margins on inference chips are not going close to Nvidia’s margins on its training GPUs, where it basically has a monopoly.

It also opens a path for some companies to lower some of their costs, and instead of going heavy on training GPUs and scaling there, they can split some of that on inference chips and still scale the models. Inference for customers is vastly cheaper than training…

…The thing that I also didn’t mention, but because of the o1 model release and the fact that we are coming to the start of Big Tech reporting earnings, I believe there is a high chance that the hyperscalers and companies like Meta, who are building these LLMs will increase their CapEx expectations now even more in the short-term than what they did before and much higher than analysts expect. The reason is that they now have to account for spending on Inference compute to improve these models. Inference was before a cost that they could gradually introduce and control more with users getting limited access to AI features, etc. This has changed, and you can use inference to scale the model. This might not be what investors will like in the short term. Still, it is something that, in the long run, brings us even more capable models, possibilities of easier agentic AI use cases, and SLMs that have a good enough accuracy to be used more often compared to bigger LLMs. There are already estimates on how much the inference costs are more expensive with an o1 model than with »pre o1 models«. This industry expert quantifies how much more expensive it is:

» Analyst: Strawberry o1, I’ve been told it’s 4X-5X more expensive than ChatGPT?

Industry Expert: Yeah. That’s the right level. That adds up given that it will essentially use 4X-5X more tokens on average. In the worst case, it will be 10X possibly. The 4X-5X is an average number of how much more expensive it is.«

Disclaimer: The Good Investors is the personal investing blog of two simple guys who are passionate about educating Singaporeans about stock market investing. By using this Site, you specifically agree that none of the information provided constitutes financial, investment, or other professional advice. It is only intended to provide education. Speak with a professional before making important decisions about your money, your professional life, or even your personal life. We currently have a vested interest in Meta Platforms, and Microsoft. Holdings are subject to change at any time.

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 13 October 2024:

1. An Interview with Meta CTO Andrew Bosworth About Orion and Reality Labs – Ben Thompson and Andrew Bosworth

Orion, Meta’s AR glasses, is spectacular. I must start with the caveat that this is not a shipping product; the glasses that I tried felt like a consumer-ready product, but they reportedly cost $10,000 each, and Meta has decided to hold off on shipping a consumer version until they can bring the price down. That will be a tall order, and that challenge should be kept in mind with everything that follows.

What follows is unadulterated praise. Orion makes every other VR or AR device I have tried feel like a mistake — including the Apple Vision Pro. It is incredibly comfortable to wear, for one. What was the most striking to me, however, is that the obvious limitations — particularly low resolution — felt immaterial. The difference from the Quest or Vision Pro is that actually looking at reality is so dramatically different from even the best-in-class pass-through capabilities of the Vision Pro, that the holographic video quality doesn’t really matter. Even the highest quality presentation layer will pale in comparison to reality; this, counter-intuitively, gives a lot more freedom of movement in terms of what constitutes “good enough”. Orion’s image quality — thanks in part to its shockingly large 70 degree field of view — is good enough. It’s awesome, actually. In fact — and I don’t say this lightly — it is good enough that, for the first time ever, I felt like I could envision a world where I don’t carry a smartphone.

Orion is a standalone product, at least in terms of needing a phone; instead there is a “puck”, an oblong unit that holds the compute for the operating system and connectivity, and which connects to the glasses wirelessly. The glasses themselves contain the compute necessary for low-latency calculations that pertain to the actual display. One challenge I see in this model is input: voice works well, and the wristband that detects the electrical signals in your arm worked flawlessly for me — you can control your glasses with your hand without anyone knowing — but I wouldn’t mind if that “puck” contained a Blackberry-style keyboard for extended text entry…

…Was there any aspect of the Vision Pro going so high end that also made you re-anchor on the low end, or do you think you would’ve ended up here regardless?

AB: No, I think we would’ve ended up here regardless. I mean, listen, I love that the Vision Pro — people won’t believe me — I love that they went maximalist. Just like, “What if we just take this dial and turn it to 11, and let the rest of the system fall where it does?”, and you see why we haven’t done that, just in terms of weight and cost. Like yep, that’s what it takes to take this dial and turn it to 11.

And this is why I think you and Mark right away seemed almost relieved by the Vision Pro.

AB: Your only real fear when a competitor launches a product is that they’ve had a breakthrough that you haven’t had. That there’s something that they’ve figured out, some technical thing that you haven’t figured out, because then they have a sustaining advantage potentially for some period of time until you can beat them on it. So I think whenever a device comes out, it’s like, “Oh good, this is all made with materials that we are aware of, this is all made with technologies that we have access to”.

“We understand why it costs this much, why it weighs this much.”

AB: We could have built this, we chose not to build this. It is both great for the world that there’s people exploring different quadrants of the space. By the way, if the Vision Pro had sold really well, of course we’d be changing our strategy. We’d be like, “Oh, okay, cool, there’s actually a bigger market than we’ve realized up there, let’s go do it”. And I think, by the way, I do actually think there will be a market there, when there’s software.

Are you surprised at how little content Apple’s released with Vision Pro?

AB: It’s how do you get the content? Before you have the devices and it’s a chicken-and-egg problem where it’s like, “Hey, okay cool, you have these devices out there, but there’s not enough for me to build my content for”.

Is part of the low cost a bet that if the egg is the end market, that’s the most important part?

AB: One hundred percent. We’ve talked about this all the time, you almost always hear me talk about the Quest ecosystem. I’m not talking about the Quest line of devices, I’m talking about building as big an audience as I can for developers to target so they can sell their software, so there’s more developers, that brings more consumers, and you have flywheel that way. Then at some point, that’s how you power your way up market. That’s how you power your way to, “Hey, we can now sell higher margin, higher end devices because there’s plenty of stuff”.

Well, to that point, Mark talks about, “In every market there’s the integrated version and the modular mass market one”, but if you go back to the PCs, Microsoft swept the market. Now one thing that’s important about that era that’s different from the smartphone era is in the smartphone era, Apple was first. In the PC era, DOS was first, so Microsoft was actually first, so they actually had developers first. At this point, seeing the Vision Pro, seeing what’s happened over the last six to nine months, are you shifting from a, “Yeah, we can both be winners here”, to, “We’re going to win the whole thing”?

AB: Man, I feel good about our position, if that’s what you’re asking.

I want to pretend I’m turning off the mic and getting your honest thought.

AB: With me, you’ll always get an honest thought, I have to make sure I’m phrasing this in a clever way.

The only reason I’m being careful here is I think — I don’t really want to be antagonistic with anybody, including Apple, I think it’s great that they’re investing, I want them to continue invest. Actually the Vision Pro has caused a surge of interest in investing in the entire space, including in us. I’ve gotten calls in the last couple of months especially that I would not have gotten, had Apple not launched the Vision Pro, and if they weren’t courting people to consider that there’s going to be a follow-on version. So it’s really, really healthy to have that competition. Good for consumers, good for us.

I also think that right now, if you’re a developer, you’d be an idiot not to build for us first, we have an audience that can actually go buy your software. It’s big enough to sustain you, and then yeah, no problem, bring it over to Apple Vision Pro after that.

Is your bigger concern losing to Apple, or that a market never materializes for these devices?

AB: Oh, good question. Yeah, my biggest concern is that the market gets capped somehow, like it doesn’t take off. The thing I worry about with Apple specifically is that they have their phones and devices so locked down that they can self-preference a ton. So they can easily, you look what our Orion glasses, these full AR glasses, incredible. We’ve got custom silicon in the glasses, we’ve got custom silicon in the puck, but Apple could build all that and just be like, “Oh, it only works with us,” which they’ve already done with the AirPods.

They don’t need a puck because they have a phone.

AB: They already have a phone, and they did this with Airpods.

Or the Apple Watch.

AB: Apple Watch. Those aren’t the best possible things you could build, but no one else is allowed to build those things, so it’s like, “Oh cool”, so if I have a concern about Apple, it’s not the competitiveness or non-competitiveness of their headsets, it’s that they’re going to bundle into their ecosystem in a way that really makes it hard for us to compete…

…This is the first device I’ve ever used that — I know you guys have been saying it — that genuinely feels post-phone.

AB: It could do it, right?…

…I’ll be totally honest, after using Orion, I’m excusing you all your billions of dollars a year spent, that’s how incredible it is, but I do think one of the critiques, and you talked about it when you went in, this was an entity that had two completely different camps that want to go in totally different directions. Then even a few years after you were there, you’re having an operating system bake-off for years instead of months and then it’s, “Should we do processors? Should we partner with someone doing processors?”. What is the forcing function that is getting you into, “Okay, we’re going to stop experimenting and actually start building”? What got you to that point? Was it the Year of Efficiency? Was there a bit where, “Look, we have to lay off half the team, so we’ve got to decide which half”?

AB: I love this question. It was before the Year of Efficiency hit. I think it’s not uncommon, you have these expansionary periods where you’re like, “We don’t know what matters yet, we truly don’t know what technology is the right technology, we don’t know what operating system is the right operating system, we don’t know what trade-offs matter yet”. So if you want to be successful with high confidence in a certain timeframe, it pays to parallel path a ton of stuff.

But how long do you parallel path it?

AB: We honestly turned the corner with Quest 2, especially when we had mixed reality in sight. That started the process, and now you’ve got to a point with a mixed reality with our metaverse division where it’s extremely focused, have a very clear vision of what good looks like, have a very clear ability to discern this is the path, this is not the path. As a consequence, you can be really, really much more efficient with your resourcing, your parallel pathing list, you’re just blitzing the things that matter more.

With augmented reality, Orion, a year ago, we actually hit this point where we’re like, “Okay, we believe in this, we see it, we have a really clear sense of where we’re going with this”, and you know what really helped a lot with that was the Ray-Ban Meta glasses as well. Cool, it’s not just that we have this distant AR thing, we actually have an entire family of devices coming before that that also matter.

Did AI save Reality Labs?

AB: Oh my gosh. So AI, because FAIR, the Fundamental AI Research group reported to me until this year. We just moved it over to join the rest of the AI stuff with Chris, and I don’t know if it saved us, but it’s a wonderful tailwind, it’s the first tailwind I can remember having. For us, it’s mostly just headwind after headwind after headwinds like, “Oh, guess what? This thermal performance is worse than you thought, this battery life is worse than you thought, the efficiency is worse than you thought”, and so we finally got a tailwind. We finally got a thing that showed up before it was expected, which was AI.

So I think to answer your first question, each of these devices has gone through an expansionary period and a contractionary period where it expands until you feel like you have a good understanding and intuition of what good looks like, and then you can start to prune and then you can get really good about pruning. Today our architecture is really tight, hand tracking, eye tracking, face tracking, Codec Avatars, these are shared technologies, they work in both VR and AR, and we have a single shared team building those technologies. Separately, the operating system for AR has to be its own operating system because it turns out the use cases, what you actually do, the interaction paradigm, completely different.

2. Xi Jinping is worried about the economy – what do Chinese people think? – Kelly Ng and Yi Ma

What is less clear is how the slowdown has affected ordinary Chinese people, whose expectations and frustrations are often heavily censored.

But two new pieces of research offer some insight. The first, a survey of Chinese attitudes towards the economy, found that people were growing pessimistic and disillusioned about their prospects. The second is a record of protests, both physical and online, that noted a rise in incidents driven by economic grievances.

Although far from complete, the picture neverthless provides a rare glimpse into the current economic climate, and how Chinese people feel about their future…

…The slowdown hit as the pandemic ended, partly driven by three years of sudden and complete lockdowns, which strangled economic activity.

And that contrast between the years before and after the pandemic is evident in the research by American professors Martin Whyte of Harvard University, Scott Rozelle of Stanford University’s Center on China’s Economy and Stanford masters student Michael Alisky.

They conducted their surveys in 2004 and 2009, before Xi Jinping became China’s leader, and during his rule in 2014 and 2023. The sample sizes varied, ranging between 3,000 and 7,500.

In 2004, nearly 60% of the respondents said their families’ economic situation had improved over the past five years – and just as many of them felt optimistic about the next five years.

The figures jumped in 2009 and 2014 – with 72.4% and 76.5% respectively saying things had improved, while 68.8% and 73% were hopeful about the future.

However in 2023, only 38.8% felt life had got better for their families. And less than half – about 47% – believed things would improve over the next five years.

Meanwhile, the proportion of those who felt pessimistic about the future rose, from just 2.3% in 2004 to 16% in 2023.

While the surveys were of a nationally representative sample aged 20 to 60, getting access to a broad range of opinions is a challenge in authoritarian China.

Respondents were from 26 Chinese provinces and administrative regions. The 2023 surveys excluded Xinjiang and parts of Tibet – Mr Whyte said it was “a combination of extra costs due to remote locations and political sensitivity”…

…In 2004, 2009 and 2014, more than six in 10 respondents agreed that “effort is always rewarded” in China. Those who disagreed hovered around 15%.

Come 2023, the sentiment flipped. Only 28.3% believed that their hard work would pay off, while a third of them disagreed. The disagreement was strongest among lower-income families, who earned less than 50,000 yuan ($6,989; £5,442) a year…

…There are other indicators of discontent, such as an 18% rise in protests in the second quarter of 2024, compared with the same period last year, according to the China Dissent Monitor (CDM).

The study defines protests as any instance when people voice grievances or advance their interests in ways that are in contention with authority – this could happen physically or online. Such episodes, however small, are still telling in China, where even lone protesters are swiftly tracked down and detained.

A least three in four cases are due to economic grievances, said Kevin Slaten, one of the CDM study’s four editors.

Starting in June 2022, the group has documented nearly 6,400 such events so far.

They saw a rise in protests led by rural residents and blue-collar workers over land grabs and low wages, but also noted middle-class citizens organising because of the real estate crisis. Protests by homeowners and construction workers made up 44% of the cases across more than 370 cities…

…Between August 2023 and Janaury 2024, Beijing stopped releasing youth unemployment figures after they hit a record high. At one point, officials coined the term “slow employment” to describe those who were taking time to find a job – a separate category, they said, from the jobless.

Censors have been cracking down on any source of financial frustration – vocal online posts are promptly scrubbed, while influencers have been blocked on social media for flaunting luxurious tastes. State media has defended the bans as part of the effort to create a “civilised, healthy and harmonious” environment. More alarming perhaps are reports last week that a top economist, Zhu Hengpeng, has been detained for criticising Xi’s handling of the economy.

3. Will Hurricane Helene Cause a Chip Shortage? What the Major Chipmakers Are Saying – Tae Kim

Hurricane Helene flooded and damaged the local infrastructure in Spruce Pine, making some roadways impassable, according to local news reports. Sibelco and The Quartz Corp., the two companies that manage the quartz mines in the town, have both temporarily shut down mining operations.

High-purity quartz found in Spruce Pine is a key material used in the production of silicon wafers that are used to make semiconductors. Quartz’s ability to withstand extreme temperatures is useful for making crucibles or containers that hold the melted polysilicon material used to produce wafers and solar cells.

According to Vince Beiser, author of The World in a Grain, the two companies’ Spruce Pine mines provide 70% to 90% of the world’s production of high-purity quartz used for the semiconductor industry…

…Ed Conway, author of Material World, a book about raw materials, posted that Spruce Pine quartz mines are unique in terms of purity and consistency, and finding another high-purity source would take months or possibly years.

But in a bad scenario, where the mines are offline for months, the chip industry may be insulated. “The significance of supply disruptions from the [Spruce Pine] mines is exaggerated,” Dylan Patel, chief analyst at SemiAnalysis said.

Patel added that the raw wafer companies had months of inventory, there are other countries that have high-purity quartz mines, and there are methods to purify lower-quality quartz or create synthetic quartz crucibles.

4. The Truth Behind the Highlight Reels – Thomas Chua

People are often shocked when seemingly perfect couples announce a divorce or breakup, even though their social media showed nothing but happiness just days earlier. What’s hidden from view are the realities that unfold behind the scenes—disagreements, financial pressures, or emotional distance.

The same kind of comparison happens in investing. We look at people like Warren Buffett, who delivered an incredible 30.4% annualized return during his partnership years (1957–1969), or Peter Lynch, who achieved 29.2% annualized returns at Fidelity’s Magellan Fund (1977–1990). Their results are awe-inspiring, but we rarely consider the personal price they paid to achieve them.

Warren Buffett’s biography, The Snowball, talks about how he spent his days working and his nights poring over Moody’s Manual. While his wife, Susie, took care of him wholeheartedly and assumed the responsibility of managing their household and raising the children, Buffett’s mind remained elsewhere. Even during family trips—like a visit to Disneyland—he would sit alone, engrossed in reading.

This single-minded focus on work created a widening distance between Buffett and his family. His children longed for his attention, and Susie craved a deeper connection. The strain eventually became too much, leading to Susie’s departure…

…Buffett and Lynch’s legendary results required intense focus and commitment, often at the expense of their relationships. This is not unlike the sacrifices elite athletes make, dedicating everything to training, diet, and recovery to reach the pinnacle of their sport…

…When it comes to investing, we need to ask ourselves: What’s the price we’re willing to pay? How much time, energy, and money are we truly prepared to invest? 

5. X (or Twitter) thread on China’s stimulus – Adam Wolfe

Why is China’s stock market booming if most economists, including me, don’t think the stimulus measures announced or reported go far enough to solve China’s economic problems or even its cyclical slump? I think it’s how the stimulus has been designed. 1/

The measures aimed at the real economy are mostly incremental, small, and inconsequential. But the measures that the PBoC announced to support the stock market are new, unlimited, and significant. 2/…

…Start with the 20-bps policy interest rate cut. 3/

PBoC Governor Pan was at pains to say this is as much as he could do. Rate cuts pass through to loan rates faster than deposit rates, so to keep banks profitable, he couldn’t offer any more. That also implies he won’t be cutting rates further soon. 4/…

…Existing mortgage rates were cut, too, saving some CNY150bn in interest payments per-year, according to Pan. But they would have adjusted in January anyway, so the actual savings are ¼ of that. They did the same thing last year. It had no macro impact. 7/

Lower down payment requirements for second home purchases? Done that before, too. It didn’t lead to higher sales of new homes. 8/…

…Another CNY1tn will be used to support consumption. About half of that would go toward extending the cash for clunkers programs. Those have helped specific industries but have had little macro impact. And the impact is getting smaller the longer these programs run. 11/

The only new thing for the real economy is the reported program that would give a monthly allowance to families with two more kids. I estimate that a bit over 10% of families would qualify, so it would cost about CNY500bn/year. 12/

This could have a big multiplier effect on growth, but CNY500bn is small beans. Plus, temporary support measures like this tend to be saved…

…But what about the stock market? The PBoC will set up a CNY500bn facility for institutional investors to higher risk assets for safe assets from the PBoC. This higher-quality collateral would then allow the investors to take on more leverage to buy more stocks. 14/

The PBoC will also open a CNY300bn re-lending window to encourage banks to finance the repurchase of stocks by listed companies. Pan made a point to say that both programs could be doubled or tripled if they work. The sky is the limit! 15/…

…Inflating a bubble in stock prices without doing much to boost earnings could end in tears. Alternatively, sucking liquidity out of safer assets like bonds could lead to another “redemption crisis” for WMPs/bond funds and losses for households. 19/

Disclaimer: The Good Investors is the personal investing blog of two simple guys who are passionate about educating Singaporeans about stock market investing. By using this Site, you specifically agree that none of the information provided constitutes financial, investment, or other professional advice. It is only intended to provide education. Speak with a professional before making important decisions about your money, your professional life, or even your personal life. We currently have a vested interest in Apple, Meta Platforms, and Microsoft. Holdings are subject to change at any time.