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 29 September 2024:

1. Digging Into The Coal Industry – Matt Franz

There are two main types of coal: thermal and metallurgical (“met”) or coking coal.

Met coal has more carbon, less ash, moisture, and sulfur than thermal coal. It is rarer and commands a higher price.

Met coal is a key ingredient in steel. To make steel, met coal is first turned into coke by heating it to 1,000ºC in the absence of oxygen. Without oxygen, the coal does not burn. The coal swells and then releases its gaseous volatile matter. Nearly pure crystalline carbon is all the remains. Coke is to coal what charcoal is to wood. Caking is a coal’s ability to be converted into coke. Thermal coal has no caking ability, which is why it is much cheaper.

Coke is mixed with iron ore, flux (e.g. limestone), and hot air in a blast furnace to create iron. Iron is put into a basic oxygen furnace where oxygen reduces the metal’s carbon content. It is further refined to remove impurities and alloys are added to make steel.

The quality of met coal influences the quality of the coke, iron, and steel produced. A blast furnace fed with higher quality coke will require less of it, lowering production costs. Steel makers have an economic incentive to pay more for higher quality met coal.

It takes 0.8-1.05 tons of met coal to produce one ton of steel. (1.3-1.5 tons of met coal make one ton of coke. 0.6-0.7 tons of coke make one ton of steel.) That’s a lot! 70% of the world’s steel is made this way…

… The major met coal exporting nations are the US, Canada, and Australia. The major importers are countries with large steel industries relative to their domestic met coal supplies – China, India, Japan, and South Korea.

The US usually exports ~70% of its met coal. Export contract prices are tied to international benchmark indices. Domestic contracts tend to specify a fixed price and a fixed volume for one year…

…Thermal coal has a lower calorific value (CV) and a lower cost than met coal. It is primarily used to generate electricity. It is also used to make cement. It takes 0.1-0.12 tons of coal to make one ton of cement.

Thermal coal has been in decline in Europe since the 1980s and in the US since the 2000s. It continues to grow in Asia. Worldwide, coal demand reached its highest level ever in 2022 and again in 2023…

…Today, coal remains the world’s largest energy source for electricity generation. Coal may be losing share as an energy source, but it continues to grow in absolute tons.

China is the world’s largest producer and consumer of coal. In the early 2000s coal produced 75-80% of its electricity. Today it’s more like 60-65%. Coal lost share but grew in absolute terms. Chinese electricity production rose eightfold and its coal consumption rose sevenfold…

…Coal’s peak share of U.S. energy occurred around 2007-2008 at 50% of electricity. Today it’s 20-22%. That’s still a meaningful amount. The decline in US coal was driven as much by fracking and its byproduct, cheap natural gas, as environmental considerations. Should US natural gas get expensive, we could see a shift back towards coal…

…Coal’s share of energy production is falling slower than the increase in total energy demand. Jevon’s Paradox describes this situation. Demand for energy is elastic. As energy costs decrease, demand for energy increases even more. On balance, energy demand increases, even as energy consumption becomes more efficient…

…In Energy and Civilization: A History (2017), Vaclav Smil explains that energy transitions often take more than a century. The transition from biomass (wood) to coal in Western Europe took 200 years. The transition from coal to oil began in the late 19th century, but oil didn’t overtake coal as America’s dominant energy source until the 1940s, approximately 50-60 years later. The transition is still far from complete, and that’s despite crude being more energy dense and easier to transport (via pipeline).

One of the factors affecting the speed of the transition is infrastructure. The transition from coal to oil in America was slowed by the need to replace steam engines with diesel engines. The modern analog is the cost and complexity of switching a power plant from coal to natural gas.

Price is another factor. If there’s a new fuel that is much cheaper than the legacy fuel, there’s an economic incentive to rebuild the infrastructure faster. A wide disparity between coal and natural gas prices that is expected to continue will drive more US coal plants to switch to gas. That’s less likely to happen in Asia, where gas is less plentiful and LNG infrastructure is more expensive.

Once again, this suggests that the last ton of coal will be very expensive, not very cheap. Thermal coal may be a sunset industry, but it is going to be a beautiful sunset.

2. Fed up with Fed Talk? Factchecking Central Banking Fairy Tales! – Aswath Damodaran

As I drove to the grocery story on Fed Cut Wednesday, I had the radio on, and in the news at the top of the hour, I was told that the Fed had just cut interest rates, and that consumers would soon see lower rates on their mortgages and businesses on their loans. That delusion is not restricted to newscasters, since it seems to be widely held among politicians, economists and even market watchers. The truth, though, is that the Fed sets only one interest rate, the Fed Funds rate, and that none of the rates that we face in our lives, either as consumers (on mortgages, credit cards or fixed deposits) or businesses (business loans and bonds),  are set by or even indexed to the Fed Funds Rate…

…While the Federal Open Market Committee controls the Fed Funds rate, there are a whole host of rates set by buyer and sellers in bond markets. These rates are dynamic and volatile, and you can see them play out in the movements of US treasury rates (with the 3-month and 10-year rates highlighted) and in corporate bond rates (with the Baa corporate bond rate shown)

There is a final set of rates, set by institutions, and sometimes indexed to market-set rates, and these are the rates that consumers are most likely to confront in their day-to-day lives. They include mortgage rates, set by lenders, credit card rates, specified by the credit card issuers, and fixed deposit rates on safety deposits at banks.  They are not as dynamic as market-set rates, but they change more often than the Fed Funds rate…

…To test whether changes in the Fed Funds rate are a precursor for shifts in market interest rates, I ran a simple (perhaps even simplistic) test. I looked at the 249 quarters that compose the 1962- 2024 time period, breaking down each quarter into whether the effective Fed Funds rate increased, decreased or remained unchanged during the quarter. I followed up by looking at the change in the 3-month and 10-year US treasury rates in the following quarter:

Looking at the key distributional metrics (the first quartile, the median, the third quartile), it seems undeniable that the “Fed as leader” hypothesis falls apart. In fact, in the quarters after the  Fed Funds rate increases, US treasury rates (short and long term) are more likely to decrease than increase, and the median change in rates is negative. In contrast, in the periods after the Fed Fund decreases, treasury rates are more likely to increase than decrease, and post small median increases…

…In the quarter after the Fed Funds rate increase, mortgage rates and fixed deposit rates are more likely to fall than rise, with the median change in the 15-year mortgage rate being -0.13% and the median change in the fixed deposit rate at -0.05%. In the quarter after the Fed Funds rate decreases, the mortgage rate does drop, but by less than it did during the Fed rate raising quarters. In short, those of us expecting our mortgage rates to decline in the next few months, just because the Fed lowered rates on Wednesday, are being set up for disappointment…

…How else can you explain why interest rates remained low for the last decades, other than the Fed? The answer is recognizing that market-set rates ultimately are composed of two elements: an expected inflation rate and an expected real interest rate, reflecting real economic growth…

…Interest rates were low in the last decade primarily because inflation stayed low (the lowest inflation decade in a century) and real growth was anemic. Interest rates rose in 2022, because inflation made a come back, and the Fed scrambled to catch up to markets, and most interesting, interest are down this year, because inflation is down and real growth has dropped…

…The Fed’s major signaling device remains the changes in the Fed Funds rate, and it is worth pondering what the signal the Fed is sending when it raises or lowers the Fed Funds rate. On the inflation front, an increase or decrease in the Fed Funds rate can be viewed as a signal that the Fed sees inflationary pressures picking up, with an increase, or declining, with a decrease. On the economic growth front, an increase or decrease in the Fed Funds rate, can be viewed as a signal that the Fed sees the economy growing too fast, with an increase, or slowing down too much, with a decrease…

…Viewed through this mix, you can see that there are two contrary reads of the Fed Funds rate cut of 50 basis points on Wednesdays. If you are an optimist, you could take the action to mean that the Fed is finally convinced that inflation has been vanquished, and that lower inflation is here to stay. If you are a pessimist, the fact that it was a fifty basis point decrease, rather than the expected twenty five basis points, can be construed as a sign that the Fed is seeing more worrying signs of an economic slowdown than have shown up in the public data on employment and growth…

…If you remove the Fed’s role in crisis, and focus on the effects of just its actions on the Fed Funds rate, the effect of the Fed on equity market becomes murkier…

…The S&P 500 did slightly better in quarters after the Fed Funds rate decreased than when the rate increased, but reserved its best performance for quarters after those where there was no change in the Fed Funds rate. At the risk of disagreeing with much of conventional wisdom, is it possible that the less activity there is on the part of the Fed, the better stocks do? I think so, and stock markets will be better served with fewer interviews and speeches from members of the FOMC and less political grandstanding (from senators, congresspeople and presidential candidates) on what the Federal Reserve should or should not do…

… The truth is that the Fed is acting in response to changes in markets rather than driving those actions, and it is thus more follower than leader. That said, there is the very real possibility that the Fed may start to believe its own hype, and that hubristic central bankers may decide that they set rates and drive stock markets, rather than the other way around…

…I believe that it is time for us to put the Fed delusion to rest. It has distracted us from talking about things that truly matter, which include growing government debt, inflation, growth and how globalization may be feeding into risk, and allowed us to believe that central bankers have the power to rescue us from whatever mistakes we may be making.

3. ‘There Are Real Issues in China Now,’ Ray Dalio Says (Transcript here) – Bloomberg Televsion

I think that there are real issues in China now, and they changed, really in the last four years, and that is that they need a restructuring. Individuals, 70% of their money was in real estate. Real estate has gone down. Stocks have gone down. Salaries have gone down. And so and as a result, they’re not spending and they’re concerned and they’re holding money in cash…

…At the same time, you have the government sector is a problem because most of the government spending – 83% of government spending – is spent by local governments. Those local governments got their money by selling land for real estate. Okay, there are no land sales and they borrowed a lot of money…

…It’s a situation that’s more challenging than Japan in 1990. It needs a restructuring in order to be able to do that. And then there’s also the question: Is the property ownership, is it respected? And Deng Xiaoping during his period said, “It’s glorious to be rich.” Is it still glorious to be rich?…

…Yes, there’s fantastic innovation in terms of technology, there’s nothing like it other than in the United States. Europe certainly isn’t a competitor in that. However, it’s very much government-directed. Can there still be entrepreneurship and that inventiveness? These are the big cosmic questions…

…I see investing in China as largely a very attractively-priced place that now has a lot of questions regarding the issues that I just referred to…

…There’s a small percentage of our portfolio which is in China, and we’ll stay in China, you know, through this process.

4. OpenAI’s New Model, How o1 Works, Scaling Inference – Ben Thompson

There are two important things happening: first, o1 is explicitly trained on how to solve problems, and second, o1 is designed to generate multiple problem-solving streams at inference time, choose the best one, and iterate through each step in the process when it realizes it made a mistake…

…There has been a lot of talk about the importance of scale in terms of LLM performance; for auto-regressive LLMs that has meant training scale. The more parameters you have, the larger the infrastructure you need, but the payoff is greater accuracy because the model is incorporating that much more information. That certainly still applies to o1, as the chart on the left indicates.

It’s the chart on the right that is the bigger deal: o1 gets more accurate the more time it spends on compute at inference time. This makes sense intuitively given what I laid out above: the more time spent on compute the more time o1 can spend spinning up multiple chains-of-thought, checking its answers, and iterating through different approaches and solutions.

It’s also a big departure from how we have thought about LLMs to date: one of the “benefits” of auto-regressive LLMs is that you’re only generating one answer in a serial manner. Yes, you can get that answer faster with beefier hardware, but that is another way of saying that the pay-off from more inference compute is getting the answer faster; the accuracy of the answer is a function of the underlying model, not the amount of compute brought to bear. Another way to think about it is that the more important question for inference is how much memory is available; the more memory there is, the larger the model, and therefore, the greater amount of accuracy.

In this o1 represents a new inference paradigm: yes, you need memory to load the model, but given the same model, answer quality does improve with more compute.

5. Learning from Richard Lawrence of Overlook – 14.3% CAGR for 30 years – Eugene Ng

The birth of Overlook’s Cap on Subscriptions originated when Richard Lawrence had lunch in 1992 in New York with Crosby Smith, a representative of the Dillon Family. Richard was asked why he would not just raise capital to generate fees like other investment managers. Crosby proposed that if Richard limited initial subscriptions into the fund at $30 million, the Dillon Family would invest $1 million. They shook hands, and Overlook had its first investor.

The Overlook Cap on Subscriptions was born in that spontaneous moment for Overlook. Richard Lawrence thought that the Cap on Subscriptions has proven to be the single most significant business decision in their 30-year history. In the early 1990s, Overlook decided to cap new subscriptions at 8-9% growth per year. This policy enabled the company to grow its AUM steadily…

…Effectively, the Cap on Subscriptions can smooth fund inflows, effectively lowering the cyclical volatility of AUM. One can then limit inflows of funds at the top of the market, have a ready queue of investors waiting to jump in during market declines, effectively making an investment fund more anti-fragile especially during market selloffs.

In addition, the Cap incentivizes investors to make a long-term commitment, which is aligned with a long-term investment horizon. Investors usually have to wait 6–12 months to gain access, so there are no short-term gains for investors trying to time the markets. The Cap effectively self-selects patient long-term investors.

What is the downside of having such a Cap? As you can imagine, such a Cap on Subscriptions is not for AUM/asset gatherers. The fund size grows much slower and takes longer to scale, and investment managers collect much lower fees…

…Time-weighted return (TWR): The TWR calculates the compound growth of a portfolio’s Net Asset Value on a per-share basis over a specified period of time. Fund managers most often disclose this number.

Capital-weighted return (CWR): CWR calculates the Internal Rate of Return (IRR) for an individual investor’s return and the return collectively earned by all investors in the fund. The CWR accounts for all cash flows into and out of the investor’s specific account and the fund since inception. Most fund managers do not report CWR, and CWRs typically underperform TWRs for most funds.

The Discount (the “Discount”) and the Premium: The discount is the difference between the TWR and the CWR for a specific fund. Discounts occur when CWRs are lower than and underperform TWRs. Peter Lynch was producing world-leading returns when he ran Magellan (high TWR), but the underlying investors performed far worse (low CWR).

Discounts typically happen for two reasons:

First, a fund manager can generate exceptional results as measured by TWRs at the fund’s inception when assets under management (AUM) are small. Then, the manager gets “discovered” and/or “promoted,” and an explosion of money enters the fund, to the great delight of the fund manager. However, with the larger asset base, the now-famous fund manager performs poorly, dragging down his TWR while crushing his CWR.

Second, CWRs are hurt when investments are poorly timed. Investors chase funds promoting hot themes, then bail out when markets turn down. This behavior inevitably decreases their CWRs. But even buying smartly and selling poorly, or buying poorly and selling smartly, can result in a Discount.

On average, the Discount increases when some of the following conditions prevail:

1. Funds experience fast growth of AUM: the Discount tends to increase as the absolute value of a fund increases.
2. Funds are invested in trendy asset classes.
3. Funds are exposed to excessive valuation risk.
4. Funds have excessive exposure to fund-of-funds’ investors. 
5. Funds are operated in higher volatility sectors…

…Richard realized that the elimination of Overlook’s Discount is overwhelmingly due to their legal Cap on Subscriptions. At first, he thought it was due to the success of their Investment Philosophy or the luck of their investors in timing their investments.

Overlook’s Investment Philosophy has helped them achieve outperformance of our TWR vs. the benchmark, but it did not impact CWR. The investors’ luck is not a factor either, as their investors have added funds consistently over time.

Instead, the answer lies exclusively in the legal Cap on Subscriptions because the Cap has allowed a limited amount of funds to enter Overlook steadily over the past 30 years. Control over the growth of AUM is the key to eliminating the Discount. The legal Cap on Subscriptions is the hero of Overlook’s story.

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 company 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 22 September 2024:

1. Mario Draghi outlines his plan to make Europe more competitive – Mario Draghi

Across different measures, a wide gap in GDP has opened up between the European Union and America. Europe’s households have paid the price in forgone living standards. On a per-person basis, real disposable income has grown almost twice as much in America as in the EU since 2000…

…Europe largely missed out on the digital revolution led by the internet and the productivity gains it brought: in fact, the productivity gap between the EU and America since 2000 is largely explained by the tech sector. The EU remains weak in the emerging technologies that will drive future growth. European companies specialise in mature technologies where the potential for breakthroughs is limited.

The problem is not that Europe lacks ideas or ambition. But innovation is blocked at the next stage: it is not translated into commercialisation, and innovative firms that want to scale up are hindered by inconsistent and restrictive regulations. Many European entrepreneurs prefer to seek financing from American venture capitalists and scale up in the American market…

…EU companies face electricity prices that are two to three times those in America. Natural-gas prices are four to five times higher. Over time, decarbonisation will help shift power generation towards secure, low-cost clean-energy sources. But fossil fuels will still set the energy price for most of the time for at least the remainder of this decade. Unless Europe better transfers the benefits of clean energy to end-users, energy prices will continue to dampen growth…

…As the era of geopolitical stability fades, the risk of rising insecurity becoming a threat to growth and freedom is increasing. Europe is particularly exposed. The EU relies on a handful of suppliers for critical raw materials and is heavily dependent on imports of digital technology.

2. When Chasing More Dividends Leaves You With Less – Jason Zweig

In July and August, as investors became more convinced interest rates will fall, exchange-traded funds specializing in dividend-paying stocks took in $4.5 billion in new money, estimates Ryan Issakainen, a strategist at First Trust, an ETF manager in Wheaton, Ill.

Although funds with big payouts sound safe, high income can lead to a poor outcome. You need to guard against needless tax bills, overexposure to narrow segments of the market and the chance of deep long-term losses…

…To see the potential downside of these funds, though, consider Global X SuperDividend, an ETF with $784 million in assets.

It yields nearly 11%.

That’s huge compared to the income returns of roughly 1.3% on the S&P 500, 2.1% on the Dow Jones Industrial Average and 5% on short-term U.S. Treasurys.

The SuperDividend fund’s supersized yield comes at a cost. Launched in June 2011 at $75, this week the shares traded around $22. That’s a 70% decline.

If you’d bought the ETF at its inception and held continuously through the end of August, you’d have lost 9%—after accounting for all those jumbo dividends along the way…

… A company that pays a steady stream of growing dividends is probably in robust financial health, but one that pays gigantic dividends is probably struggling and may be desperate to attract investors. Put a bunch of those into an ETF, and you get lots of income but even more risk…

…High-dividend funds often hold many more energy and financial stocks than broader portfolios do. That can raise risk.

In 2008, both First Trust’s Dow Jones Global Select Dividend and its Stoxx European Select Dividend had roughly 50% of their assets in financial stocks—right before the global financial crisis struck.

Over the 12 months ended March 31, 2009, as the MSCI World index lost 42.2% and European stocks overall sank 49.6%, First Trust’s Global Select fell 53.2% and European Select lost 63.9%—even after factoring in their dividends…

…Although a moderate dividend can be a sign of robust corporate health, a huge dividend can be a distress signal. A dividend four or five times greater than that of the overall market isn’t a green light; it’s a red flag.

3. Learning From Peter Keefe – John Garrett

The investment philosophy [at our new fund, Rockbridge Capital] is exactly the same: great businesses, great managers, bargain price. That remains unchanged.

The implementation has evolved over time. Great businesses, great managers, great price—it’s kind of like mom and apple pie. I mean, who’s opposed to it? It’s axiomatic that these things work, but I believe your approach to implementation should change over time…

…You don’t really know what makes a business great. You don’t really understand what contributes to compounding. You want a business with all the great characteristics—growth, rapid growth, sustainable growth—but you don’t know how to evaluate one business against another. You don’t know which businesses are mayflies and which are incredibly durable with multi-decade runways.

Learning how to discern and implement those three criteria does evolve over time. Another thing that evolves is the recognition that there are only a tiny number of businesses you will own over the course of a career that will compound and give you that 100-bagger effect or the 300-bagger effect—what Munger called the Lollapalooza effect. Those opportunities are incredibly rare.

But you spend your entire career looking for them. On day one, when you enter the business, you might think, ‘Well, maybe I’ll find it today,’ but you’re probably not going to find it today, tomorrow, or the day after. So what has evolved for me is the realization that when you find a compounder, don’t let it go…

…Every time I’ve trimmed a position and it involved a great business, it wound up being a huge mistake.

Now, we had this conundrum recently. We own a lot of Microsoft, which we bought back in the Balmer days. So it’s been in the portfolio over 10 years. We’ve made 10 times our money in the business, and it’s appreciated to have a very significant percentage of our portfolios.

Microsoft got a big bid recently because of the artificial intelligence stuff, and I don’t know enough about artificial intelligence to have a responsible opinion. But you can argue that there’s a trillion dollars’ worth of value in Microsoft attributable to AI. Do I trim the position? Well, based on the mistakes I’ve made in the past, no. But at the same time, is a 35 or 40 multiple sustainable for a company that’s already worth three trillion dollars? It’s hard to make that argument. And particularly when you’re managing both taxable and tax-exempt capital, you can make a pretty good argument that you should trim it. But again, that’s never worked out for me. So we are where we are.”…

…Every time we’ve had a business that’s compounded more than 10x—and we’ve had a couple that have compounded at 100x—there’s always been a leader and visionary who is a person of humility, thinking about their business in multi-decade timelines. Without exception, 10, 100, 200-baggers were always a person…

… They’re not thinking about an exit or the next thing; they’re thinking in 10, 20, 30-year time periods.

These people are artists. They’re focused on building something of great value—not just to accumulate wealth, but to create something valuable to society. To borrow from Tom Gayner, these are businesses that do something for people instead of to people. They are financially interested, but the finances are a means of keeping score rather than acquiring more things or a better jet. Those are the people I shy away from. The real artists see beauty in what they’re building and are focused on creating value for all stakeholders, especially the owners of the business.

When discussing people who want to serve all stakeholders, it’s not about rank-ordering which stakeholders to reward first. It’s about understanding that a business can do well for its employees, shareholders, and vendors. Munger talked about this all the time…

…People ask, ‘What makes you different?’ Well, it’s not my process. Everybody wants great businesses and great managers and to buy them at a bargain price. Nobody says they’re not a value investor or that they don’t like what Buffett does. So I think a major differentiator in this business is temperament. If I have an advantage, it’s that I don’t feel like I’m coming unglued when the world is coming unglued. I don’t know why that is; it’s just part of my makeup, but it’s an advantage because low prices are good for investors…

…The biggest compounder I’ve ever had in the investment business was American Tower. I was fortunate enough to figure out American Tower before it was even a public company. It was a footnote in the 10-K of a company called American Radio Systems. American Radio Systems was run by a brilliant, thoughtful capital allocator who fits into this liberal arts bucket I talked about earlier. Steve Dodge went to Yale and was an English major there.

Steve did cable transactions for one of the big New York banks. He got the idea that recurring revenue businesses or contractual revenue were great. So he moved into the cable business and then into the radio business. Around the time of the Telecom Act in the mid-1990s, digital networks for cell networks were beginning to roll out. Steve had people come to him and say, ‘We’d like to hang some of these digital antennas on your radio antennas.’ They also owned a portfolio of television broadcast antennas. They needed structures in suitable locations for these antennas.

That’s the genesis of American Tower, which was just a footnote. I remember calling Steve and asking about it. He basically hung up on me. I had a good relationship with him, so I knew I was onto something.

Long story short, American Tower was spun off and went to over $40 a share. Then came the dot-com bust. There had been a land rush in the tower business, and many companies had gotten levered up.

This was when I learned one of my early lessons about leverage, although it eventually helped me. American Tower dropped to under 80 cents a share from $44. Now that’s a drawdown.

I went up to Boston, where American Tower was headquartered. Chuck Akre was with me, and we met with Steve. He said, ‘I’ll tell you anything that I can legally tell you. I want you to know upfront that I don’t have much time. I have a business that needs my attention. It needs more attention than I can possibly give it because there’s only 24 hours in a day. I think that we can save this thing and I’m not sure that we can, but I also want to tell you, I am solely responsible. This is the worst thing that’s happened to me in my business career, but you’re looking at the guy who made the mistakes that got us in the pickle that we’re in.’

There was none of the usual excuses like ‘The dog ate my homework,’ or blaming the pandemic or the dot-com bust. Steve gave us none of that.

Steve figuratively raised his hand and said, ‘I messed it up, and I am sorry. I will do my best to get you and all the other shareholders out of this pickle.’

That kind of character in a moment of great crisis inspired me and others to make American Tower a more significant position, despite its distress.

We were convinced that the business wasn’t going to zero. It had one of the greatest business models in public companies’ history. A business where 100% of incremental revenue flows through to free cash flow and was growing by 20 to 30% a year. It was highly likely the business would be recapitalized. I can’t think of a financing environment where it wouldn’t be.

Steve’s character and willingness to accept responsibility were crucial in our decision to increase our position. It went up 300-fold from there.

4. Light-Based Chips Could Help Slake AI’s Ever-Growing Thirst for Energy – Amos Zeeberg

Recent results suggest that, for certain computational tasks fundamental to modern artificial intelligence, light-based “optical computers” may offer an advantage…

…In theory, light provides tantalizing potential benefits. For one, optical signals can carry more information than electrical ones—they have more bandwidth. Optical frequencies are also much higher than electrical ones, so optical systems can run more computing steps in less time and with less latency.

And then there’s the efficiency problem. In addition to the environmental and economic costs of relatively wasteful electronic chips, they also run so hot that only a tiny fraction of the transistors—the tiny switches at the heart of all computers—can be active at any moment. Optical computers could, in theory, run with more operations taking place simultaneously, churning through more data while using less energy…

…Seeing the potential advantages, researchers have long tried to use light for AI, a field with heavy computational needs. In the 1980s and 1990s, for instance, researchers used optical systems to build some of the earliest neural networks. Demetri Psaltis and two colleagues at the California Institute of Technology created a clever facial recognition system using one of these early optical neural networks (ONNs). They stored images of a subject—one of the researchers, in fact—as holograms in a photorefractive crystal. The researchers used the holograms to train an ONN, which could then recognize new images of the researcher and distinguish him from his colleagues.

But light also has shortcomings. Crucially, photons generally don’t interact with each other, so it’s hard for one input signal to control another signal, which is the essence of what ordinary transistors do. Transistors also work exceptionally well. They’re now laid down on coin-size chips by the billion, the products of decades of incremental improvements…

…The process of multiplying matrices, or arrays of numbers, undergirds a lot of heavy-duty computing. In neural networks, specifically, matrix multiplication is a fundamental step both in how networks are trained on old data and in how new data is processed in trained networks. And light just might be a better medium for matrix multiplication than electricity.

This approach to AI computation exploded in 2017, when a group led by Dirk Englund and Marin Soljačić of the Massachusetts Institute of Technology described how to make an optical neural network built on a silicon chip. The researchers encoded the various quantities they wanted to multiply into beams of light, then sent the beams through a series of components that altered the beam’s phase—the way its light waves oscillated—with each phase alteration representing a multiplication step. By repeatedly splitting the beams, changing their phase, and recombining them, they could make the light effectively carry out matrix multiplication. At the end of the chip, the researchers placed photo detectors that measured the light beams and revealed the result.

The researchers taught their experimental device to recognize spoken vowels, a common benchmark task for neural networks…

…Since that 2017 paper, the field has seen steady improvement, as various researchers have come up with new kinds of optical computers. Englund and several collaborators recently unveiled a new optical network they call HITOP, which combines multiple advances. Most importantly, it aims to scale up the computation throughput with time, space, and wavelength. Zaijun Chen, a former MIT postdoc now based at the University of Southern California, said this helps HITOP overcome one of the drawbacks of optical neural networks: It takes significant energy to transfer data from electronic components into optical ones, and vice versa. But by packing the information into three dimensions of light, Chen said, it shoves more data through the ONN faster and spreads the energy cost over many calculations. This drives down the cost per calculation. The researchers reported that HITOP could run machine-learning models 25,000 times larger than previous chip-based ONNs.

To be clear, the system is still far from matching its electronic predecessors; HITOP performs about 1 trillion operations per second, whereas sophisticated Nvidia chips can chug through 300 times as much data, said Chen, who hopes to scale up the technology to make it more competitive. But the optical chip’s efficiency is compelling. “The game here is that we lowered the energy cost 1,000 times,” Chen said…

…While optical computing has advanced quickly over the past several years, it’s still far from displacing the electronic chips that run neural networks outside of labs. Papers announce photonic systems that work better than electronic ones, but they generally run small models using old network designs and small workloads. And many of the reported figures about photonic supremacy don’t tell the whole story, said Bhavin Shastri of Queen’s University in Ontario. “It’s very hard to do an apples-to-apples comparison with electronics,” he said. “For instance, when they use lasers, they don’t really talk about the energy to power the lasers.”

Lab systems need to be scaled up before they can show competitive advantages. “How big do you have to make it to get a win?” McMahon asked. The answer: exceptionally big. That’s why no one can match a chip made by Nvidia, whose chips power many of the most advanced AI systems today. There is a huge list of engineering puzzles to figure out along the way—issues that the electronics side has solved over decades. “Electronics is starting with a big advantage,” said McMahon.

Some researchers think ONN-based AI systems will first find success in specialized applications where they provide unique advantages. Shastri said one promising use is in counteracting interference between different wireless transmissions, such as 5G cellular towers and the radar altimeters that help planes navigate. Early this year, Shastri and several colleagues created an ONN that can sort out different transmissions and pick out a signal of interest in real time and with a processing delay of under 15 picoseconds (15 trillionths of a second)—less than one-thousandth of the time an electronic system would take, while using less than 1/70 of the power.

5. Warren Buffett Case Study: Arbitrage – Dirtcheapstocks

By 1981, Arcata was the second largest printing services organization in the U.S. In addition, Arcata owned 77,500 acres of Northern California timberlands, which it used for timber harvesting, reforestation and milling.

Arcata was to be acquired by KKR. The stock was trading around $33/share at the time of the deal announcement. KKR’s $37 offer represented a reasonable premium over the current share price. But there was one other interesting bit of information.

“In 1978 the U.S. Government had taken title to 10,700 acres of Arcata timber, primarily old-growth redwood, to expand Redwood National Park. The government had paid $97.9 million, in several installments, for this acreage, a sum Arcata was contesting as grossly inadequate. The parties also disputed the interest rate that should apply to the period between the taking of the property and final payment for it. The enabling legislation stipulated 6% simple interest; Arcata argued for a much higher and compounded rate.” – Warren Buffett

“Buying a company with a highly speculated, large-sized claim in litigation creates a negotiating problem, whether the claim is on behalf of or against the company. To solve this problem, KKR offered $37.00 per Arcata share plus two-thirds of any additional amounts paid by the government for the redwood lands.” – Warren Buffett…

…“We started buying Arcata stock, then around $33.50, on September 30 and in eight weeks purchased about 400,000 shares, or 5% of the company. The initial announcement said that the $37.00 would be paid in January 1982. Therefore, if everything had gone perfectly, we would have achieved an annual rate of return of about 40% — not counting the redwood claim, which would have been frosting.” – Warren Buffett

“All did not go perfectly. In December it was announced that the closing would be delayed a bit. Nevertheless, a definitive agreement was signed on January 4. Encouraged, we raised our stake, buying at around $38.00 per share and increasing our holdings to 655,000 shares, or over 7% of the company. Our willingness to pay up – even though the closing had been postponed – reflected our leaning toward ‘a whole lot’ rather than ‘zero’ for the redwoods.” – Warren Buffett…

…“On March 12, KKR said its earlier deal wouldn’t work, first cutting its offer to $33.50, then two days later raising it to $35.00. On March 15, however, the directors turned this bid down and accepted another group’s offer of $37.50 plus one-half of any redwood recovery.” – Warren Buffett…

…“The trial judge appointed two commissions, one to look at the timber’s value, the other to consider the interest rate questions. In January 1987, the first commission said the redwoods were worth $275.7 million and the second commission recommended a compounded, blended rate of return working out to about 14%.” – Warren Buffett

“In August 1987 the judge upheld these conclusions, which meant a net amount of about $600 million would be due Arcata. The government then appealed. In 1988, though, before this appeal was heard, the claim was settled for $519 million. Consequently, we received an additional $29.48 per share, or about $19.3 million. We will get another $800,000 or so in 1989.” – Warren Buffett

The final result: 39% IRR…

…The greatest investor to ever live earns a 39% IRR in a low-risk arb deal. The most striking part of this case is not the return generated – but the lack of risk taken.

Arcata was a profitable, growing business. Take a look at its five-year history leading up to the deal.

Arcata had strong operating businesses that earned sufficient sums to cover its interest burden with plenty of comfort.

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 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 15 September 2024:

1. The ROI on Generative AI – Tanay Jaipuria

The poster child for this has been Klarna which leveraged AI to elevate their customer support. Their AI assistant has taken over the work of 700 employees, reducing resolution times from 11 minutes to just 2 minutes while maintaining high customer satisfaction levels…

...Microsoft casually dropped that they too are expecting to save hundreds of millions of dollars a year on call centers after adopting Generative AI.

“Dynamics with Gen AI built in is sort of really … the category that gets completely transformed with Gen AI, contact centers being a great example. We, ourselves, are on course to save hundreds of millions of dollars in our own Customer Support and Contact Center Operations. I think we can drive that value to our customers”…

…We’re also hearing examples of measurable, tangible benefits from enterprises, as Amazon shared about their software development assistant Q which has saved them over 4,500 developer years in a recent code migration task…

…”With Q’s code transformation capabilities, Amazon has migrated over 30,000 Java JDK applications in a few months, saving the company $260 million and 4,500 developer years compared to what it would have otherwise cost. That’s the game changer. And think about how this Q transformation capability might evolve to address other elusive but highly desired migrations.”…

…eBay launched a new AI-assisted selling flow, and are already seeing improvements in customer satisfaction as well as faster time to list and get value for Sellers…

…YUM Brands is enhancing customer experiences at Taco Bell by rolling out voice AI driven drive-through systems. This technology is not only improving customer satisfaction but also boosting team member productivity, and the results are so promising they are accelerating their roll-out timelines…

…Manulife is an example of a company already seeing large ROI in using AI to assist salespeople…

…”We’re using GenAI and machine learning models to make it really easy for agents to understand customer opportunities but also to generate these personalized communications at the click of a button to help them engage with more customers more often.

In our first 2 weeks live, about 68% of our agents had already used the new GenAI capabilities. And in July, we will be broadening that user base to about 2,000.

Based on our analysis in Singapore, we anticipate a 17% uplift and repurchase rates for our customer base, when this is fully rolled out to all of our agents.”…

…Rocket Mortgage is utilizing AI to automating the transcription of client calls and completing mortgage applications…

…”Now the Rocket Logic Assistant seamlessly generates over 300,000 detailed transcripts every week from outbound calls. It supports over 100 data points on mortgage applications saving our bankers from inputting tens of millions of data fields each week.”…

…Walmart has harnessed generative AI to enhance its product catalog, improving the quality of over 850 million pieces of data…

…”We’ve used multiple large language models to accurately create or improve over 850 million pieces of data in the catalog. Without the use of generative AI, this work would have required nearly 100x the current head count to complete in the same amount of time.”…

…Mastercard is leveraging the new advances in Generative AI to enhance fraud detection, achieving a 20% increase in accuracy. 

2. The Agent Era – Patrick O’Shaughnessy and Bret Taylor

Patrick

It’s such an interesting story because I think it becomes ultra relevant in today’s world. And you hear a lot about this, maybe the mythical 10x engineer, the 100x engineer, 1,000x engineer, the leverage available to one person with a growing tool kit.

And maybe that’s a great excuse to bridge the conversation into agents. I think everyone listening will have heard that term and maybe have thought about it a little bit, have gotten excited about the prospect of some sort of autonomous agent doing work on their behalf or their company’s behalf. But it would be great for you to ground us in your definition of what one of these things is, if this becomes a really critical part of the world of technology in the next year or two. I think it would be great for everyone just to have a level-set, simple definition from your perspective on what an agent is and does.

Bret

I’ll start with maybe the academic flavor of this, but then I’ll move into what I think is maybe the more — what I believe is the more relevant definition, but agent is like the word app. There’s not one definition, and I think it will be a noun that is quite meaningful in the age of AI. The word agent in the context of AI comes from the word agency and essentially is a system that can reason and take action autonomously is the way I think about it. And a system that is agentic is one where software and AI can reason and make decisions and take action without human intervention, which is really exciting but something that is relatively new though the idea is certainly not new.

I think the effectiveness of reasoning with AI systems has become so meaningfully better over the past couple of years that I think the concept is — like many parts of AI, the ideas are not new, but the effectiveness is, and so we’re living in an era of agents now.

In practice, I think the word agent, just like the word app or site in the age of the web, will become important to all of us. So one agent that I think is important is what my company Sierra does, which is your company’s conversational AI. And so just imagine you’re a retailer. I think you’ll put as much care and attention into your AI agent as you do your website or your mobile app. Or if you’re a bank, and you’ll put as much care and attention to your AI agent, which can help a customer look up the balance of their checking account or perhaps be an interface to your investment banking arm or wealth management arm. Or if you’re a streaming service, your agent might help people sign up for a plan or upgrade or downgrade their subscription, as an example.

In that case, an agent is something like website or mobile app that’s branded and it’s yours. And there are parts of it that are about agency and sort of the AI definition of the word. But more importantly, it’s your thing. It’s your digital asset. It becomes the digital manifestation of your brand.

And that’s what my company Sierra does. And we think that’s one really important part of an agent. Just like in 1995, the way you existed online was to have a website, we think in 2025, the way you will engage with your customers will be your AI agent, and we think it’s a really important new category.

But then taking, okay, what are the other types of agents out there? One will be, I’d like to think of them as persona-based agents. They’re internally facing. They do a job. You’ve talked about software engineering. I think there’ll be software engineering agents that will work to produce software. I was looking at a start-up called Harvey, I think, that’s making a legal LLM, which is super interesting. And I think across many job functions, there will be AI agents that produce the output of a — whether it’s a paralegal or a software engineer or an operations analyst, things like that. So that’s one.

So there’s your company’s agent, there’s a persona-based agent that does a job, and then the third one — category is probably personal agents. So this is the agent that will work on your behalf, whether it’s helping you plan a vacation or organize your calendar or perhaps triage your inbox and things like that. I think technically, they’re all similar, but my guess is they’re different enough in what job they accomplish for you that there’s — probably different companies will build those different categories of agent.

If you’re building a software to be a personal assistant agent, the breadth of systems you have to integrate with is infinite because different people use different calendars and different this and different that, and there’s lots of interesting investment into that. If you’re building a coding agent, it’s a much more narrow use case but very deep, and you’re probably evaluating it based on benchmarks of the effectiveness of the software produced and the robustness of the software it produces…

…Patrick

What do you think are the next most important unlocks for the power of these agents? You mentioned their access tools, access to the Internet. I’ve heard people talk about the ability to have some sort of stored memory about you, the customer or the specific customer or just memory in general that doesn’t just live inside of a context window that’s always re-fed in or something.

Are those the three things that we need to unlock the next tier of productivity out of agents? Are there other things that you and Sierra are focused on? I’d love to get down to the nitty-gritty capabilities and roadblocks that you’re thinking about and working on that might make these things as ubiquitous as you think they will be.

Bret

Yes. I’ll start with the vantage point of Sierra. We help companies build customer-facing AI agents. Today, if you’re setting up a new Sonos speaker, you can chat with an AI agent they’ve built on our platform to help you set it up. If you’re a SiriusXM subscriber, you can chat with Harmony, which is their AI agent they’ve built on our platform. And if you’re a WeightWatchers member, if you click on the 24/7 live coaching tab in their app, that’s an AI agent they’ve built on our platform.

One of the things that I think is a nuanced problem that is not strictly technical in nature is just the act of actually designing conversational customer experiences is a relatively new discipline. I remember in the early days of the Internet, most websites looked like DVD intro screens, like they’re very graphical, there’s four big buttons. It’s really interesting to go down the Wayback Machine and look at them.

And I would say it took a number of years to evolve into sort of the design idioms that we recognize with websites today. And now if you go to a retailer, they’ll have a hamburger menu on the top left, and the way you filter through items and these — they’re sort of emergent from people’s lived experiences, both designing and using websites.

And now you can talk to almost any web developer. And they’ll not only choose similar technologies to make a website, but even the design process and Photoshop or Figma to design a website, they’re sort of established practices, some of which are obvious and some of which are actually subtle, like why did this become the way these things are done, and it’s the cumulative experience we have building with them.

The difference between a website in a mobile app and an AI agent is both the breadth and non-determinism of AI agents. So if you have a menu on a website, you can control what links are there, and it’s essentially multiple choice, here’s the options available to you. If you have an AI agent with a free-form text box, people can type whatever they want into that. And so your concept of what your customer experience is defined by you, but it’s also defined by your customers, by what they write in there.

It reminds me — going back to my web analogies here, it reminds me of going from Yahoo Directory to Google Search. Rather than having a taxonomy of everything available, it’s just free form, and there’s a much longer tail of queries in Google than there was in Yahoo! because of the expressiveness of a search box versus a directory.

And I think that that’s one of the really interesting and, I think, exciting opportunities with conversational AI for customer experiences is it’s a really authentic way to actually hear from your customers what they want from you. And I think we’ve — and so it sort of stands to reason, your website was the rails on which your customers communicate with you. And this is a free form that I think it’s much more expressive. And we’ve had multiple customers learn things about their customers that they didn’t expect by providing this really free-form experience.

And then similarly, I think the other really interesting thing when I mentioned non-determinism is the word agent comes from agency, and it’s really how much creativity do you want to give your AI in interacting with your customers. I think if you start from a position of control, you can say, I want to put guardrails around everything, but then your conversational customer experience is somewhat robotic. You’ve essentially defined the multiple-choice options of your customers’ experience. If you give your agent too much agency, in the extreme case, it will hallucinate, but in the more practical case, it just might not protect your brand in the way that you want it to.

And I would say that design question is both a technology question, which obviously we’re quite invested in solving, and I’m really excited about some of the work we’ve done there, but there’s a deeper question here, too, that’s actually a philosophical branding and design question as well. And what we’re trying to do at Sierra is not necessarily predefining answers to those questions. I think every company and every brand will have a different perspective on what’s correct for their brand experience but provide a platform that’s powerful and expressive enough. Whatever your answers are personally to that question, you can build your agent on Sierra.

Patrick

It’s so interesting to think about the customer experience going to a website where I buy shoes or something. I think one of your first customers was flip-flops, and there was a funny story around that, but I’m going to buy a pair of sandals, let’s say, on a website. And rather than click around, I just describe what I want and I can imagine like another pane on the right just starts showing me stuff. And then maybe I check out through this same thing as well, and that’s a simple version of tooling or ability to take action.

I’m curious what the hardest parts for you have been to build. It’s quite technically daunting to even think about how to build something like this, let alone one that’s adjustable and tunable to my specific brand. So talk a little bit about how hard of a technical challenge this is for Sierra, like the degree of difficulty you’ve encountered relative to, say, your expectation.

Bret

Yes. It’s a really wonderful question. I think that generative AI broadly is a technology with which it’s very easy to make a demo and very hard to make an industrial-grade system. And I think that’s the area of technical challenge that we’re really trying to dive into. And I think it’s one thing to say that this system does the correct thing 90% of the time. And it’s really an inkblot test whether 90% is a really good number or a horrible number.

And it also depends on the process. And so if it’s a consumer application that was helping you with your homework, maybe 90% is decent. If it’s something operating revenue impacting part of your business or there’s a compliance concern, it’s absolutely unacceptable to be wrong 10% of the time.

And so a lot of the challenges that we’re facing are, we like to say that software systems are moving from rule-based to goals- and guardrails-based. And it’s a very different mental model for building software systems. Rule-based systems, if you think about just the software development life cycle that’s evolved over the past 20 years, it’s really about how you make more and more robust rule-based systems, how do you ensure that the same input produces the same output, that it’s reliable, that it’s stable, and there’s a lot of true innovation in the way we make software to make them more secure and robust.

Now if you have parts of your system that are built on large language models, those parts are really different than most of the software that we’ve built on in the past. Number one is they’re relatively slow compared — to generate a page view on a website takes nanoseconds at this point, might be slightly exaggerating, down to milliseconds, even with the fastest models, it’s quite slow in the way tokens are emitted.

Number two is it can be relatively expensive. And again, it really varies based on the number of parameters in the model. But again, the marginal cost of that page view is almost zero at this point. You don’t think about it. Your cost as a software platform is almost exclusively in your head count. With AI, you can see the margin pressure that a lot of companies face, particularly of their training models or even doing inference with high-parameter-count models.

Number three is they’re nondeterministic fundamentally, and you can tune certain models to more reliably have the same output for the same input. But by and large, it’s hard to reproduce behaviors on these systems. What gives them creativity also leads to non-determinism.

And so this combination of it, we’ve gone from cheap, deterministic, reliable systems to relatively slow, relatively expensive but very creative systems. And I think it violates a lot of the conventions that software engineers think about — have grown to think about when producing software, and it becomes almost a statistical problem rather than just a methodological problem.

And so that’s really what we’ve tried to solve. We shared on our website, but we have a process we call the agent development life cycle, which is the name comes from, say, in the software development life cycle, here’s what you should do with these agentic platforms. It’s also — we’ve developed a lot of unique technology to make these systems more robust with having one AI model supervise another AI model to layer different models on top of each other to produce statistically more robust results.

And then as importantly, we’ve developed ways that folks who aren’t experts in AI can express the behavior that they want in their agent. You shouldn’t have to be an AI expert to make an agent just like you shouldn’t have to have a PhD in computer science to make a website. I don’t think we’re there yet, but that’s really what we’re trying to solve.

And broadly speaking, I would say, on the spectrum of fundamental research institutions like OpenAI, we’re not that we’re applied. We’re really thinking about how do we engineer on top of these foundation and frontier models to produce robust or reliable agents for our customers.

Patrick

I love the title of this one Kevin Kelly book, What Technology Wants, and I’m curious what agents want. If I’m a customer, I’m a prospective customer, and I want to go work with Sierra to make the best possible version of a conversational agent for my customers to use, what can the companies provide that make the agent do the best job?

Bret

Yes, it’s a great question. I would say that there’s two types of knowledge that I think really produce a really robust agent. One is the factual knowledge of your company. This just grounds the agent so that it won’t just make something up.

There’s a pretty widely-used technique called retrieval augmented generation in AI right now that effectively means rather than relying on the knowledge encoded in the model to answer questions, you present the model with knowledge, maybe stored in a knowledge base or a database and say, “Hey, summarize the content from here. Don’t rely on the information you’ve been trained on.”

That has been an effective technique for two reasons. One is that it means that you don’t necessarily need to train or fine-tune a model to use it with proprietary data, which is a much cheaper deployment methodology. And it also can be effective at preventing hallucinations as well because you’re effectively — rather than relying on the AI to determine what it knows or doesn’t know, you present the AI with the knowledge that it’s allowed to know, a simple way of putting it.

And that’s factual knowledge. And I would say that’s necessary, but woefully incomplete because that would enable your AI agent to answer questions, but it wouldn’t necessarily enable it to orchestrate a complex process or take action on your customers’ behalf.

The other type of knowledge is procedural knowledge. We have a Sonos speaker. It stops working. What would the best Sonos engineer ask you and do to figure out whether it’s a problem with your hardware, a problem with your Sonos app or a problem with your Wi-Fi? Like what is the process by which you do that.

If you’re a subscription streaming service, what is the process of processing an upgrade or downgrade to your membership? Are there different offers available based on your membership level? Do you have a promotion running? What’s been the most effective technique to keep people, a subscriber for a long period of time?

This is all the stuff that if you are a person and expert in it, and so coming in with that knowledge of not only here’s the factual knowledge for our company, but here’s the processes that represent our greatest customer experience. What does the best salesperson do? What does the best customer service person do? What is — the most effective marketeer at your company, how do they describe your products? And that’s often there we work with our customers to improve when they deploy AI.

And then the third thing is just access to the underlying systems themselves. I think the AI agents shouldn’t just be about answering questions or having a conversation, they should actually be able to take action on your behalf, whether that’s a retailer processing a return or a subscription service, changing your level of membership or connecting to the telemetry system of a consumer electronics company. So we can say, “Hey, we know your device phoned home. You’re connected. We now figured out this other problem.”

Or even with something like SiriusXM sending a signal down from a satellite to refresh your radio if your radio stopped working. So three ingredients, factual knowledge, procedural knowledge and systems integrations, I think, are the three key ingredients. And then with the right methodology, your agent can do anything that a person could do on a computer, which is just an incredible opportunity for customer experiences.

3. Here’s What Happens When Credit Markets Go Dark – Joe Weisenthal, Tracy Alloway, Jared Ellias, and Elisabeth de Fontenay

Joe (11:53):

You spell out this evolution of the debt markets and the historical things you’re taught in law school about the dangers of single lenders. We’ve talked to people in the industry and they have their explanations for why this particular market has boomed. But from your research, what would you say are the drivers of this? Or when you talk to people, what problems does the private credit market solve for them?

Elisabeth (12:19):

The interesting thing about this is that there’s multiple stories going on at the same time. So one is that, this is just actually substituting for a lot of the activity that banks did because the banks, ever since the financial crisis, have been really constrained for a lot of reasons. One, they’ve primarily been constrained because of regulation, and sort of regulation designed to discourage them from making risky loans and from, you know, to have diversification in their portfolio, and so on. And just their evolving model of doing business, that they prefer to be sort of the middleman and get some fees rather than lend directly. [There are] all kinds of reasons why banks have retreated from particularly the lower middle market, but also all the way to the largest companies. A second story is just that there’s been too much bank regulation. So, I’m not going to take a position on whether that’s true or not, but that bank regulation is stifling the banks and they can’t really lend and so on.

A third story is one that we find really interesting and appealing, which is that, it may just be that it never really made all that much sense to fund loans using bank deposits. That essentially, you have a very short-term liability, which is customer deposits, and very long-term assets. So some of these loans, of course, are multi-year loans. And that’s just a fundamental mismatch that banks have always struggled with and that bank regulation has always struggled with. And this is a really nice, neat solution to that. And the reason it’s showing up now is that, thanks to sort of loosening of some of the securities laws and other things, it’s finally the case that you can get these investment funds that are big enough to actually take over the role of banks. And for them, the sort of positive side of private credit is that you now have a better match between the funding source, which is you have these big institutional investors putting capital into private credit funds that is locked in for a number of years, and you’re matching that really well against the loans that are also multi-year. So in some sense, it’s actually a better fit than banks for financing this type of loan… 

...Joe (20:43):

It sounds pretty good to me. Okay, so there’s less legal fees, less creditor on creditor violence, liability asset matching, the better user experience. So what’s the catch? I don’t see any problems.

Elisabeth (20:57):

One potential problem is, of course, these are, in some cases, absolutely massive loans. And so you do lose diversification benefits. These are very risky investments. I would say, the private credit structure has a partial solution to that problem, which is that, the investors themselves in a private credit fund oftentimes are so massive themselves that they really don’t lose diversification, which is to say, their portfolios are so large that they can make this enormous investment in one private credit fund because that’s a tiny piece of their portfolio. So that’s one downside of private credit. The other of course, is the absence of trading. So before. you had pretty good signals of what your position was worth. There were lots of syndicated loans that had pretty active trading and there were indices tracking all of this. The [Loan Syndications and Trading Association] LSTA provides lots of data on the loan market, and, of course, the bond market is public in terms of the pricing there. So exit is always going to be a concern in this market, and I don’t think this market really has been truly tested yet. So we’ll have to find out. But that illiquidity can be an issue depending on what kind of investor you are and what your expectation is for getting out of these things…

…Tracy (30:14):

Just to play devil’s advocate for a second, I think this is something you actually deal with in the paper, but one of the things you hear from people in the private credit industry is tha, ‘Oh, well, if you’re getting funding from a private entity, maybe a single lender or maybe a club of lenders but it’s a smaller group than you would have in the public market, maybe there’s greater potential for working out your issues if you get into trouble. So you can renegotiate your debt with a smaller group of creditors and maybe they know your business better than like a big fund that is buying pieces of all these different types of bonds and things like that.’ What’s your response to that argument? This idea that, well, private credit actually allows you to have more room for workouts or maybe even stave off bankruptcy for longer?

Jared (31:09):

So, I guess my answer is that, that all sounds great, but it’ll depend. And it’s hard to really understand which way any of these forces cut. The one thing that’s clear cut, that’s important is, we’re losing the claims trading markets. Like, that’s just going to look a lot different. Like, the active market and the claims of Chapter 11 debtors, when that debtor is a private credit funded firm. But, as to the question of, ‘Well, you know, aren’t these private credit lenders smarter, more versatile, more nimble, able to commit capital? And won’t that be good for companies?’ You know, at the end, it depends. So something you worry about is, well, maybe private credit lenders will have incentives, not to adjust their marks on their books and instead, just to do ‘amend and extend’s, and just keep loans going when the company really needed to liquidate or should have filed for bankruptcy sooner.

Think about how different the GM bankruptcy would’ve been had they filed for bankruptcy in like 2005 versus 2009 when their business had already eroded so much. So we think of that erosion as something that limits reorganization options. And it’s not necessarily obvious how private credit interacts with that. Because private credit lenders have their own incentives and maybe their incentives are to say, ‘Look, we make loans to sponsor backed companies and if the sponsor wants to continue, we’re going to keep doing that because we really want to participate in their next deals.’ Or they could say like, ‘Let’s pull the plug on these things earlier.’

So something that I’ve heard from lawyers working in this space is that when private credit lenders replace like your mid-market banks, like your Citizens and that kind of bank, when you have like a private credit lender with a $30 million loan that might have been done by a syndicate of two regional banks, the private credit lenders are much more aggressive and much more willing to pull the plug on the company and to own the asset then that bank might have been, but the world could look very different for larger companies where private credit lenders might be easier for companies to do workouts with. So it’s really hard to tell. But I’m certainly a bit skeptical of the idea that all of this is unidirectional and the private credit is just better in every way for everything. It’s different and there’ll be different pros and cons and we’ll learn more about them, and the law will adapt and hopefully deal with some of the ways in which the incentives of private credit lenders distort bankruptcy outcomes.

Tracy (33:28):

Since you mentioned GM, could you maybe talk about another specific example of a liquidation playing out a bit late, as you describe it? I’m still salty over the collapse of Red Lobster, which you mentioned in your paper. So could you talk a little bit about that one and what it tells us about private credit?

Jared (33:47):

Sure. So, something that has been the case over the past few years is you’ve had private equity owned restaurants and retailers that just ended up doing quick liquidations after stalling for a very long time. Red Lobster is really interesting. Red Lobster had been struggling for a little while and then Fortress Investment Group, which was its private credit lender, came in and took over the company and basically just owned the asset very quickly. And something that is so interesting about that is that, traditionally, other lenders would’ve been a lot more cautious about doing that, because other lenders are very cognizant of what we call ‘lender liability’ and this line of law that suggests that you shouldn’t, if you’re a lender, play too much of a role in business decisions of companies that you lend to.

And like, there’s an example of like a private credit lender just behaving in this really aggressive way, which is interesting. Like, again, it’s hard to tell exactly what’s going to happen, but certainly that example doesn’t fit well with the story of, well, you know, the private credit lender is just like the banker and you know, it’s your corner bank in 1925, who’s going to work with you on your farm. The answer is, maybe some of the time that’s the story, but other of the time, you’re dealing with a very sophisticated party who may have different incentives and be worried about different things than traditional bank lenders or investors in the broadly syndicated market.

4. Flash Crashes Are Getting Faster – Ben Carlson

In the spring of 1962, the stock market was already in the midst of a double-digit correction. Then on May 28, there was a flash crash, sending stocks down nearly 7% in a single day. It was the biggest one day sell-off since the Great Depression…

…It’s becoming clearer by the day that last Monday’s stock market swoon was also a flash crash. As of August 5, the S&P 500 was down more than 6% for the month. It’s now positive in August…

…Flash crashes happened in the 1920s, they happened in the 1960s and they happen today.

The biggest difference between now and then is the interconnected nature of the global markets. You have computer and algorithmic trading. Information flows at the speed of light. Every piece of economic data is parsed in real-time with a fine-tooth comb.

Overreactions can happen much faster now.

Just look at the biggest gap downs over the past 40+ years:

This chart shows the biggest difference between the opening price of the stock market and the prior day’s close. All of them have occurred this decade outside of the 1987 crash…

…We are likely to see more of these flash crashes in the future due to a combination of increased leverage in the system, globalized markets and computer trading.

The hard part for investors is that it’s now easier to lose control during these types of market events. You don’t have to call your broker on the phone to place a trade. You can change your entire portfolio on your phone with the push of a button.

Just because markets are getting faster does not mean your decisions must be made faster.

5. Gaining Currency – Rachel Cheung

In its effort to cement its role as an innovation powerhouse, China’s most ambitious technological debut was also its most controversial: The digital yuan was rolled out as the legal tender of choice for the Olympic games. Instead of cash or Visa (the corporate sponsor that had dominated the sports event for three decades), visitors were encouraged to exchange foreign currencies for digital yuan at automated teller machines and to pay digitally through the e-CNY app on their phones or through a card that can be used offline…

…Yet, despite all the attention, the launch of the digital yuan largely fell flat. The COVID-19 pandemic meant Olympic visitors were confined to “bubbles” with little opportunity to travel, shop and dine out, and very few foreigners chose to use the digital yuan over their credit cards. Beijing saw just $315,000 in digital yuan processed every day over the course of the games — a small fraction of the usual revenues at the Olympics. At the 2008 Olympics in Beijing, for instance, the city generated roughly $264 million per day…

…But while China acknowledged its Olympic failure, it has also quietly doubled down on the digital yuan, including a big push to drive adoption. Last year, several cities began paying civil servants and collecting taxes in digital yuan. Jiangsu province saw the most recorded transactions in the country after it gave away 30 million yuan ($4.18 million) in digital “red envelopes.” And this past May, the digital yuan expanded for the first time outside of mainland China when it became available for use in Hong Kong. Though there is no timeline for a nationwide launch yet, China has rolled out pilot schemes in 26 cities and 17 provinces since 2019.

The efforts have paid off. In a press briefing last week, the PBOC announced that total transactions reached $7 trillion yuan ($982 billion) in June — a four-fold jump since last June.

Digital yuan usage is still only a fraction of China’s $40-trillion payment market, of course. The total number of e-CNY wallets opened — 120 million as of last July — also trails behind that of Alipay, which had over a billion users by 2020 and recorded $118 trillion worth of transactions in one year alone.

But as Beijing continues to crackdown on its fintech giants, it is creating room for the digital yuan to rise. In fact, officials see the transition to digital currency as both necessary and inevitable. According to Yi Gang, former governor of the PBOC, the current moment of transition is not unlike that of the Ming Dynasty, when the government started taking tax payments in silver instead of labor and grains. China’s currency has evolved with time, he said during a speech at Fudan University in April, and “the digital yuan is no exception.”…

…Officials are also trying to expand the scope of e-CNY beyond consumer retail transactions. The Bank of China, for instance, has tested the use of “smart contracts” for afterschool programs in Chengdu of Sichuan province: Parents can pay a deposit in e-CNY to educational institutions, and the latter only receives the money after the lessons are taken.

These business-to-business and government programming applications could be a “game changer,” according to Warwick Powell, a senior fellow at Taihe Institute, a Beijing-based think tank, because they “ensure that the provision of certain funds can only be used for certain activities.”

Yet that same function triggers concern for others. For instance, although some local governments and banks have offered loans in e-CNY, companies are reluctant to take them, says Yang You, a finance professor at University of Hong Kong. “The nature of e-CNY is that a policymaker can generate a loan and see where it flows to,” says You. But companies, he notes, would much prefer non-traceable loans, despite repeated assurances from the People’s Bank of China that it will not hold information against them…

…Instead, the PBOC says the digital yuan follows a principle of “anonymity for small value and traceable for high value” as a way of striking a balance between privacy protection and combating criminal activities, such as tax evasion and money laundering. The e-CNY wallet, for instance, requires users to undergo a more complex verification process in order to unlock higher transaction limits…

… If anything, the search for an alternative to the U.S.-backed Swift, the global messaging network for the banking system, has gained momentum since the U.S.-led sanctions on Russia.

“China has used the sanctions as a reason to advance the cause of de-dollarization,” says Elizabeth Economy, a senior fellow at the Hoover Institution at Stanford University and recent advisor to the Department of Commerce. “It has made the case that the United States is weaponizing the dollar, hence other countries should begin to trade in their own currencies. It’s actually a deft diplomatic move on the part of China.”

According to the Bank of International Settlements (BIS), a survey of 86 central banks last year showed a sharp uptick in experiments with “wholesale CBDC” — transactions between banks and other financial institutions, rather than consumers and businesses. In October, for instance, the e-CNY set a new milestone: At the Shanghai Petroleum and Natural Gas Exchange, the state-owned PetroChina used digital yuan to purchase a million barrels of oil from an undisclosed seller.

“There’s still a conversation about the e-yuan [for domestic retail transactions], but there’s more discussion about a regional payment system,” says Victor Shih, an associate professor of political economy at the University of California. “An alternative to Swift potentially has more legs.”

The oil purchase seems to be a one-off so far, but a new project called mBridge hopes to make such transactions routine. It is a collaborative effort between the “innovation hub” of BIS and the central banks of five jurisdictions: China, Hong Kong, Thailand, United Arab Emirates, and most recently, Saudi Arabia. 

Underpinned by distributed ledger technology (which records transactions in multiple places at the same time), mBridge aims to be a multi-CBDC platform that can support instant cross-border payments. The idea is to make international settlement faster and cheaper than Swift. But it also means things are not dependent on the U.S. dollar.

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 Amazon, Mastercard, 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 08 September 2024:

1. #360 Robert Kierlin: Founder of Fastenal – David Senra

What’s the most important part of Fastenal’s success that outsiders discovering the company for the first time don’t understand? The number one thing is the people aspect. The goal is to unleash entrepreneurial passion, a commitment that I will be self-driven to do better than what you can expect. It is a mindset.

This is what they’re telling their employees, “Run your business like you own it.” When you trust people to solve problems and make decisions and you let them go, that’s where the magic happens. That is the story of this company. Fastenal embraces a spirit of radical decentralization in autonomy.

“Each of its 2,700 stores operates as a stand-alone business with a clear leader and full P&L responsibility. We grow from the ground up based on the actions and decisions of thousands of people who run their businesses like they own it. I want those people to stay with us forever. They will never have to stop at a certain level.”…

…“We are now one giant organization. We are 2,700 small businesses wrapped up into one big company. Society tells us, you’re a big company, act like it. We say no, you don’t get to define us, we define ourselves. We’d go against the grain in almost everything that we do.” And so there’s just crazy stats about the company that, “More than 95% of our current batch of general managers have been promoted from within”.

“For nearly all of our senior leaders have worked their way up from entry-level positions.” So that leads into the second thing I want to tell you about, which is this interview with the CEO that had succeeded Kierlin or Bob — we call him Bob, when he stepped down. His name is Will Oberton.

Oberton started out at Fastenal as a store clerk. So he went all the way from — they mean business. He went all the way from store clerk to CEO…

…“By keeping operating costs very low, Fastenal is able to pay their employees incrementally higher wages, and thus, more effectively develop and retain talented salespeople. The quality of service and depth of knowledge that the employees have eventually brings in more revenue, which grows the business and allows it to further lower operating expenses as a percentage of revenue, thus allowing for more hiring of top-quality employees, which brings in more revenue. This is an overlooked virtuous circle of sorts.”…

…Why is it so important to have everybody working as a single cohesive team, to have everybody thinking that their role that they’re playing is just as important as the person next to them? “At Fastenal, we believe that you could be the best salesperson in the world. But if the order-picker doesn’t pick it right or the truck driver doesn’t get it there on time or the billing clerk doesn’t bill it correctly, you end up with an unhappy customer.”

“Everyone is key. You are better off working to make everyone equal so they stay focused” and he goes back, what do you think he’s about to say? I bet you can already finish his sentence for him. “You are better off working to make everybody equal so they stay focused on the common goal of pleasing the customer.”

[00:26:01] He’s going to give us more advice on how to do that. You need to install a reward system that keeps everyone focused on the common goal. He’s talking about incentives. If you have Fastenal’s common goal of growing our company through customer service, you will avoid any rewards that don’t fit that goal. And so when I got to this part of the book, I thought about Charlie Munger’s like three rules for incentives.

And so this is what he said, “Number one, everyone underestimates the power of incentives. Number two, never ever think about anything else before thinking about the power of incentives. And number three, which Bob is nailing, the most important rule in management, get the incentives right.”

And again, you have to be careful of these subgroups that are going to naturally develop in your company because his whole point is like “Listen, your incentives have to — they have to fit your overall common goal,” right, the common goal of pleasing the customer. And so he gives us an example, “If you do these incentives based on like separate groups, they can optimize for things that go against your common goal.”

So he gives an example that this is a really smart idea. “We do not reward production people for minimizing scrap. If some of that scrap you eliminate comes from the extra parts that guarantee you have a full order quantity ready when the customer wants it.” The incentive superpower that Munger talks about, you clearly see by picking up the book…

… I want to go back to that story of the CEO that was meeting with Buffett, the CEO that succeeded Kierlin. So this giant part of Fastenal’s business now after, this was invented after this book was written, okay, the first version in 1997, was the fact that they have these vending machines.

And the way I think about the vending machine is like think of anytime you’ve been in like a hardware store, right? You’ve got ACE Hardware or Home Depot or anywhere else. And think about how all the equipment and supplies are presented, kind of like searching through, it’s kind of like a chaotic mess.

So Will Oberton, which was the former CEO, but he’s no longer CEO now, but he’s the one that was CEO after Bob, okay? Oberton also developed an industrial vending machine system. There’s a video on YouTube that’s fascinating about this. It’s from Fastenal. Fastenal has their own YouTube channel. You can see the vending machine if you just type in Fastenal vending machine, if you’re interested in this, I thought it’s actually cool.

Oberton developed an industrial vending machine, and I searched for it after I read this because like I got to see what this looks like. Oberton had developed an industrial vending machine system, helping Bob realize a lifelong goal. In 1951, as a 12-year-old working in his father’s auto parts store, Bob was bothered by the fact that his dad had to send customers searching for nuts and bolts to someone else’s store.

He imagined that a vending machine installed at his father’s place might pop out fasteners like gumballs. Once on his own, he tried to convert a cigarette vending machine to this purpose. He couldn’t get it to work. So he started selling fasteners over the counter. Thus, Fastenal was born. 40 years later, working with a snack machine manufacturer and off-the-shelf software, Will Oberton got the job done.

Fastenal’s vending machines have been a big hit with customers. So their vending machines are actually installed in their customers’ locations. It cannot get simpler for this. You got to watch the video, I’m telling you. Oberton got the job done. Fastenal’s vending machines have been a hit with customers, generally helping them save 30% on supplies.

[00:46:05] The machines have cut down on theft and enabled automated reordering. That 4-year-old business, which I think now is like 15-years old, within 4-years old, this new idea already started contributing to 36% of the overall sales of Fastenal, I think it’s like over 40% now. 

2. A French Bank Like No Other in Europe Seeks to Export Its Model –  Phil Serafino and Albertina Torsoli

Bpifrance is a bank like no other in Europe.

The French lender has made more than €50 billion ($56 billion) in loans to small and mid-sized businesses and has €52 billion in stakes in almost 1,000 companies. It has backed everything from a startup wanting to take tourists to the edge of space in balloons and a chain of trendy Parisian nightspots to the automotive giant Stellantis NV. A force to reckon with on French deals for M&A advisers like Goldman Sachs Group Inc. and JPMorgan Chase & Co., it has lured away bankers from firms like UBS Group AG and Rothschild & Co…

…No other European country has an agency quite like Bpifrance: a for-profit, state-owned merchant bank with a mandate to foster national champions. Its wide-ranging lending activities are financed largely by borrowings guaranteed by its ultimate backer: the French taxpayer. And for all the political turmoil at the moment in France, its interventionist policies are likely to find favor no matter which coalition — from the left or the right — ends up forming a new government.

More than a decade after it was created under then-President François Hollande and his economic adviser — one Emmanuel Macron — Bpifrance exemplifies 21st-century French capitalism: Entrepreneurs build businesses with cash, nudges and nurturing from the state, which in turn wants them to create jobs at home and develop innovative technologies. Explicit in the deal: The government will fend off foreign interlopers if necessary…

…Bpifrance’s investment prowess and risk management haven’t really been tested because Dufourcq hasn’t faced a prolonged economic downturn, enjoying a favorable wind at his back almost from the start — even during the pandemic, when the French state opened the cash taps to prevent businesses from going under.

Its stock-picking bets also haven’t always paid off. A stake in train-car maker Alstom SA, for example, has lost about a third of its value since the investment early last year. Shares of Stellantis, in which the bank has a 6.4% holding, have slumped about 45% from their peak in March as the carmaker struggles to fix problems at its US and European operations.

Also, for much of the bank’s existence, it could finance itself at rock-bottom interest rates, something that’s no longer the case. A slowing economy and higher rates also may start to hurt companies that borrow from the bank: Bpifrance’s loans classified as doubtful stood at 4.7% at the end of 2022, up from less than 4% in recent years, according to the bank’s annual reports. It didn’t disclose the statistic in its 2023 report.

Dufourcq shrugs off such concerns, noting that the three decades-old agencies that combined to form Bpifrance survived some deep financial crises, and says his bank often says no to risky investment proposals.

While some European countries have national development banks, Bpifrance is unusual for the breadth of its offerings. It operates 50 offices around France, often sending representatives door to door to drum up business. In addition to debt and equity investments, it offers financing and credit insurance to exporters and training and consulting services to entrepreneurs — including on how to shrink their carbon footprint.

3. How Richmond Fed President Tom Barkin Sees The Economy Right Now – Cale Brooks, Tracy Alloway, Joe Weisenthal, and Tom Barkin

I talked to someone from Germany yesterday — this is going to make me interested but not your [audience]. Our savings rate went up at the beginning of Covid to about 15 or 16%. Same thing happened in Germany. Our savings rate has come down to about three and a half. Theirs is still at 17. So, why are German consumers not spending the way American consumers are? That’s an interesting topic. It’s something we spent some time on. It’s the kind of thing we spent some time on…

Tracy (08:01):

What’s the theory?

Tom (08:04):

Well, so the thing that really makes it crazy interesting is, there’s a whole social safety net in Europe that doesn’t exist here. And so, most of the time you think people are saving for retirement, they’re saving for a rainy day, or they’re saving because they’re worried about losing their job. Well, in Germany, they kept everyone’s job during the pandemic and you’ve got a pension. So why are they saving? And I think the best explanation I’ve gotten, it’s actually something on my list to study going forward, is there’s just a lot more precautionary feeling about the situation in Europe, the risk versus the Ukraine, and what’s happening over there. And it’s just a culture that maybe has just gotten a lot more cautious due to geopolitics if nothing else.

Joe (08:43):

That does sound really interesting. By and large, I mean obviously, the situation in the Argentina economy is radically different than it is here in the US. Germany is probably still, all things considered, similar cyclically to the us. Does it feel like, by and large, at least among developed countries’ central bankers, that there is a strong set of common mysteries perhaps? Or are they really like, everyone’s sort of seeing different things in their own country? I mean I’m sure it’s a mix of both, but how much of a global factor is there?

Tom (09:16):

Much more in common than different. The whole practice of central banking has been, I’d say, globalized over the years. And central bankers really do think about inflation targeting, for example, in the same ways. And there are banks, like New Zealand and Australia, that, back in 2000 or even before that, set inflation targets before the rest of us and we learned from them. And so there’s a lot of learning, there’s a lot of discussion. I think there’s very much a common framework. Now, the economies are very different.

I mean, the US economy has come through this unbelievably well, the European economies have not. And so we have a much stronger economy. So much of our economy is services, so much is supplied to ourselves. A lot of this deglobalization is felt much more on the European side. The challenges in China right now are felt much more on the European side. And then emerging market countries, they really just are worried we’re going to increase rates further and they’re going to end up offside. And so, they’re very dependent on our strength of our dollar and the weakness of our dollar…

…Tom (11:43):

I think the economy, since we were together three or four months ago, the economy’s moved in a very different way. First of all, on the inflation side, I might’ve even said four or five months ago I was looking for inflation to sustain and broaden. So, it’s sustained. We’ve got very low readings for four months in a row. And it’s now across the basket, whereas six months ago [or] eight months ago, it was really just in goods. And so the concern about inflation, reaccelerating has definitely come down significantly. At the same time, the labor market stats have also softened. And so, the phrase I’ve been using is, ‘people aren’t hiring but they’re not firing,’ and that’s just not a high likely sustainable outcome. Either demand will continue and people will start hiring again or you’ll start to see layoffs. And so I think there’s more concern on the labor market and less concern on inflation relatively…

…Tom (13:00):

So consumers, you hear a lot of talk about people saying that consumers [are] weak and people are running out of savings. That’s not what I’m hearing. What I’m hearing is consumers are still spending but they’re choosing. And, the way I think about it is, they now have the time when they go into a store and they see something that’s at a price they don’t like to say, ‘I think I’m going to do something else.’ And so if you look at Walmart’s results, they would talk about people trading down. If you look at Target’s results, they talked about the kind of reaction they’re getting to lower prices. McDonald’s results in the $5 value meal. I’ve talked to hotel chains that every room is booked, but they can’t raise price at all because the second they raise price, people just won’t buy it and won’t book it. I talked to a fast food leader who’s rolling out software actually to encourage their franchisees not to raise prices anymore…

…Tracy (15:15):

What’s the urgency, then, on supporting the labor market? And there’s obviously a debate going on right now about how fast deterioration in that market actually happens. We had Claudia Sahm on the podcast recently and she was talking about, ‘maybe it’s different this time,’ but how are you thinking about the pace or the rate of change in the labor market?

Tom (15:37):

So the other thing that’s happening in the labor market is a lot more supply of labor, and part of that is participation, prime age participation hitting 2025 year highs, and immigration, which is up significantly. And so the last jobs report where unemployment went up from 4.1 to 4.3, you actually added jobs, 114,000 jobs. We just added 420,000 people to the workforce. So the denominator got bigger. And so, you know, there’s some people who look at the unemployment rate and say, ‘Oh my gosh, the labor market’s about to fall off a cliff.’ That’s not how I see it. I see a loosening labor market being driven by a lot more supply. Now, what’s the urgency? We’re not in a situation, I don’t believe, where there is this big cliff there, but when we make policy, you’re trying to make it for a year from now, right? Because [of] the lags of monetary policy, you’re trying to meet a year from now.

And so you’ve got a labor market which is slowly cooled and you’ve got inflation which is now gradually cooled. And so, you sort of say, ‘Well, which do I worry most more about?’ And it’s been very clear for the last two and a half years that all you worry about is inflation. And now those are much more balanced…

…Tom (21:57):

Well, I see inflation upside risk in two places. First is, we’re at 2.5% for the last 12 months. Our target’s 2%. So while we’re doing great at bringing it down from when it was once 7.1%, core is still at 2.5%. And even the most optimistic forecast for the back half of this year don’t believe it’ll get to 2% because the numbers were so good on a 12 month basis…

Joe (22:20):

We’re talking we’re about on a year to year basis as opposed to like a three month though sequential, yeah okay.

Tom (22:22):

On a 12 month basis. Because the last half of last year was also very good. And so, we’re at least six months away, even with really good inflation data, from the inflation numbers hitting 2%. And if the numbers are just pretty good, not really good, there’s a risk that we plateau at some level over 2%. That’s one risk. The other risk is I do see medium term inflation pressures that are out there. We have a conflict in the Middle East that could spiral. Deglobalization is a very real risk and that means that the imports of goods could be more expensive going forward, or if we even reshore, more expensive. Housing’s a place where, if rates artists start coming down, one of the things I worry about is that will spool up demand for people who’ve been waiting to buy a house till mortgage rates come down, but there won’t be any new houses built. I mean that effect is two years, three years out.

And so what happens if you have more demand for houses with the same kind of supply? Or even if more houses come on the market, everyone who puts their house in the market is a buyer and a seller. So you’d still have this excess of demand over supply. So those things are potential inflationary risks. Now, good policy works against that, and if we do the right thing with rates we’ll work against, but that’s why I just want to make sure I understand it and see it before I declare victory.

Tracy (23:37):

What’s been the most surprising thing that you’ve heard at Jackson Hole this year? You talked about German savings rate, but beyond that, is there anything that caught your eye or your interest?

Tom (23:48):

Alan Blinder asked a question today that I thought was pretty interesting. He said, ‘When you think about monetary policy lags, why aren’t you talking about how to shorten them?’ And I’ve said, almost as it’s a given, that when we raise or lower rates, it takes 12 to 18 months for the full effect to go into the economy. Well, part of that is because the economy doesn’t behave in a way that would allow it to happen quicker. An example: I think the number is, in 2009, 60% of [the] mortgages in this country were adjustable rate. Today it’s 8%. And so when we raise or lower rates, it doesn’t flow through to mortgages quickly and certainly not even like it did 15 years ago. And I’m not saying we should change the mortgage market, but it does make you stop and think, how much of our policy, the effectiveness of our policy tools, is a given or how much could actually change over time as the economy changed?

4. No Priors Ep. 78 | With AWS CEO Matt Garman (Transcript here) – Sarah Guo, Elad Gil, and Matt Garman

…Now that we’re at a $100 billion run rate, I think 85% of workloads are still running on-prem today by most estimations, somewhere in that range. Pick your number, whether it’s 80 to 90, or whatever it is. That’s enormous. If there’s still 10x growth of just existing workloads – forget all the new genAI workloads that are being created every day – these are just existing workloads to move, there’s a 10x number in there, so that business is massive…

…Gil (12:40): You mentioned that 80% of workloads still haven’t migrated over. What do you think are the main blockers to that today? is it just momentum? Are there specific features? Are there big things still to build?

Garman (12: 48): There’s some technologies that I think… Look, if I had an easy button, and by the way we’re trying to build an easy button, but if I had an easy button that would just migrate mainframes to a modern cloud architecture today, almost everyone will push that button. But it doesn’t quite exist today and it’s not as simple as like, “Great, I’ll go run your mainframe in the cloud.” That’s not what customers want. They want to actually modernize those workloads and have them into microservices and containerized workloads and other things like that. So that’s one, is there’s just a bunch of workloads like that that are old and and their customer’s running a big SAP thing and they want to move it to the cloud but it just takes time because it’s tied to a bunch of other things like that. There’s also a bunch of workloads that as you get out of core IT workloads that are in line of business, that are the next set of things. Whether that’s say telco workloads that are running the 5G infrastructure around the world, we’ve slowly been moving those to the cloud and helping those customers get that flexibility and that agility of of running those in the cloud as well. But they’re slower to move.

If you think about all the compute that runs factories out there today on factory floors, most of those have not been modernized. And there’s a huge opportunity, by the way for AI, to totally revolutionize how you think about factory workflows and efficiency there. But a lot of that hasn’t moved. There’s on-prem infrastructure that people are still amortising, there’s still people whose jobs it is to run on-prem data centers, and so they’re resistant to moving things. There’s a bunch of factors in there and so some of it is just takes time, some of it is technology pieces, some of that is we still have stuff to go build and innovate and help make it easier for customers to do that.

Guo (14:37): I’d love to hear about just the initial investigation of generative AI as a technology change and how AWS began to react to it, invest in it, because to some degree it puts us all back in the on-prem co-lo era of the world, where to get one of these, if you’re doing any sort of real pre-training, to get your startup off the ground, you’re back to, “I’ll buy a bunch of DGX boxes somewhere and I need to think about the cost and management of that.”

Garman (15:07): Actually most people are still buying those but in the cloud. But it’s not a serverless type of thing. Most people are still not buying H100s and hosting them in a co-lo or anything like that. And increasingly, I think that’s going to get harder and harder as you move to liquid cooling and larger clusters. It is a super interesting space. I think we’ve been working on this space for how many years now – we’ve been investing in AI broadly for the last 10 years, and it’s why we started five or six years ago investing at the infrastructure layer and building our own processors, because we knew this was coming, we saw this path coming and we knew that that’s also not a short-term investment. It’s one of those things you got to invest way ahead. And then we were investing and building generative AI models, and then OpenAI made a generational leap forward with what they were able to do, what was possible, and many people have talked about this. But it really in some ways was a discovery as much as anything about just what was possible and unleashed the new set of capabilities.

So we actually as a business took half a step back and said, “These are going to be transformational abilities and assuming that this technology gets better and better and better over time, how do we make it so that every company out there can go build using those technologies?” Different than, “How can I go build a consumer application that people are going to be interested in?”, we took it from the point of view of AWS, “Just what are the building blocks that I can help all of our customers, whether they’re startups, whether they’re enterprises etc, go build interesting generative AI applications.” We started from first principles. Customers are going to care a ton about security. That’s not going to change. They’re not going to all of a sudden not care about securing their infrastructure.

We also had two more hypotheses. One, the idea that there wasn’t just going to be one model. We thought that there was going to be a lot of models for a lot of different purposes, and there’d be big models and small models, and people would want to combine them in new and interesting ways. I think the last two years have probably played that out. I think when OpenAI first launched, it wasn’t as obvious, but that was one of the bets that we made. The third one is that we view that every enterprise that was building on us, the interesting IP that they were going to bring to the table was mostly going to be their data, and they were going to care that their data didn’t leak back into a model or escape from their environment. So we built a bunch of what we did starting from those principles of how do we make sure that these things are secure, that their data is secure, that they can have access to every piece of technology that the customers need to go build interesting applications, and they can do it in a cost effective way. That’s how we approach the space.

I think we now have a platform in Bedrock, in Trainium chips and Inferentia chips, and then a bunch of the other capabilities around as well as the suite of models that we offer, both proprietary as well as open source ones – or open weights ones. I think we’re starting to see that momentum pick up and we’re seeing more and more customers really like that story. They like that platform to build from, and we’re seeing enterprises really lean in and want to build in that space because it gives them a lot of that control that they want as they go and build applications…

…Gil (26:25): The other place that a lot of people are spending time right now in terms of bottlenecks to utilization or usage or future-proofing, is actually more on the chip side or semiconductor or system side and in terms of DC capacity. Obviously you all have been building Trainium chips and other things which I think is really exciting to see that evolution. How do you think about future GPU shortages? Does that go away, when? I’m sort of curious about how you think about forward-looking capacity, and is the industry actually ready in terms of building out data centers, building out semiconductors, all the rest of it, packaging.

Garman (26:56): I think we’re probably going to be in a constrained world for the next little bit of time. Some of these things, they take time. Look how long it takes to build a semiconductor fab. It’s not a short lead time and that’s several years and TSMC is running fast to try to ramp up capacity, but it’s not just them. It’s the memory providers and frankly data centers that we’re building. There’s a lot of pieces in that value chain that I think as you look at the demand for AI which has been – exponential might be undershooting it – some of those components that support that I think are catching up and I think AWS is well positioned to try to do that better than others are.

We’ve spent a long time thinking about – in the last 18 years, learning how do we think about smart investing, how do we think about capital allocation. We’ve spent a bunch of time thinking about how do we acquire our own power, how do we ensure that it’s green and carbon neutral power, all super important things. We’re the largest purchaser of renewable energy over the last… new contracts, so actually going out and adding and supporting new renewable energy projects. We’re the largest provider I think, each of the last four or five years. So we’ve been leaning into that for a while to ramp up this and this is just a step up. So we’re thinking about how are we acquiring enough power. Our own chips is a way to support the growth of Nvidia chips, and so I think the more diversity there, the better off we are. We’re a huge partner of Nvidia’s. Nvidia actually runs their AI training clusters in AWS because we actually have the most stable infrastructure of anyone else, so they actually get the best performance from us. We love that partnership and we have a great and growing relationship with them. We think things like Trainum are a good diversification and I think there will be some workloads that run better on Trainium and are cheaper on Trainium over time, and as well as Inferentia.

I think inference is one of those workloads that – today it’s 50/50 maybe of training and inference. But in order for the math to work out, inference workloads have to dominate, otherwise all this investment in these big models isn’t really going to pay off, so hopefully for the industry that all happens. But I think we’re probably going to be tight for the next little bit of time, because the demand is almost infinite. I mean it seems infinite right now.

5. Timing the Stock Market Using Valuations – Ben Carlson

I’ve never found a legitimate way to utilize valuations to determine entry or exit points in the stock market. Maybe when things get to extremes but even then valuations can be unreliable.

In early 2017, I wrote a piece for Bloomberg about stock market valuations:…

...This was the lede:

Something happened in the stock market this week that has only occurred twice since 1871: Robert Shiller’s favorite valuation method for the S&P 500, the cyclically adjusted price-to-earnings ratio, reached 30. So, is it time to worry?

The only other times in history when the CAPE ratio reached 30 were in 1929 and 2000, right before massive market crashes. So it made sense that some investors were worried about the stock market being overvalued.

The S&P 500 is up nearly 170% since then, good enough for annual gains of roughly 14% per year.

Sometimes valuations matter, but other times, the market doesn’t care about your price-to-earnings ratios.

The same is true during bear markets. Sometimes stocks get downright cheap but not all the time…

…Three of the four bear markets this century didn’t see the CAPE ratio come close to previous bear market valuation levels. If your plan was to get more aggressive when the market got cheap enough, you would still be waiting.

The problem with using valuations as a timing indicator is that even if they do work on average, missing out on just one bull market can be devastating. You could be waiting a mighty long time to get back into the stock market and miss out on big gains in the meantime.

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 Amazon (parent of AWS). Holdings are subject to change at any time.