What We’re Reading (Week Ending 31 December 2023) - 31 Dec 2023
Reading helps us learn about the world and it is a really important aspect of investing. The late Charlie Munger even went so far as to say that “I don’t think you can get to be a really good investor over a broad range without doing a massive amount of reading.” We (the co-founders of Compounder Fund) read widely across a range of topics, including investing, business, technology, and the world in general. We want to regularly share the best articles we’ve come across recently. Here they are (for the week ending 31 December 2023):
1. How Not to Be Stupid About AI, With Yann LeCun – Steven Levy and Yann LeCun
Steven Levy: In a recent talk, you said, “Machine learning sucks.” Why would an AI pioneer like you say that?
Yann LeCun: Machine learning is great. But the idea that somehow we’re going to just scale up the techniques that we have and get to human-level AI? No. We’re missing something big to get machines to learn efficiently, like humans and animals do. We don’t know what it is yet.
I don’t want to bash those systems or say they’re useless—I spent my career working on them. But we have to dampen the excitement some people have that we’re just going to scale this up and pretty soon we’re gonna get human intelligence. Absolutely not…
…Why are so many prominent people in tech sounding the alarm on AI?
Some people are seeking attention, other people are naive about what’s really going on today. They don’t realize that AI actually mitigates dangers like hate speech, misinformation, propagandist attempts to corrupt the electoral system. At Meta we’ve had enormous progress using AI for things like that. Five years ago, of all the hate speech that Facebook removed from the platform, about 20 to 25 percent was taken down preemptively by AI systems before anybody saw it. Last year, it was 95 percent…
…The company you work for seems pretty hell bent on developing them and putting them into products.
There’s a long-term future in which absolutely all of our interactions with the digital world—and, to some extent, with each other—will be mediated by AI systems. We have to experiment with things that are not powerful enough to do this right now, but are on the way to that. Like chatbots that you can talk to on WhatsApp. Or that help you in your daily life and help you create stuff, whether it’s text or translation in real time, things like that. Or in the metaverse possibly…
…One company that disagrees with that is OpenAI, which you don’t seem to be a fan of.
When they started, they imagined creating a nonprofit to do AI research as a counterweight to bad guys like Google and Meta who were dominating the industry research. I said that’s just wrong. And in fact, I was proved correct. OpenAI is no longer open. Meta has always been open and still is. The second thing I said is that you’ll have a hard time developing substantial AI research unless you have a way to fund it. Eventually, they had to create a for-profit arm and get investment from Microsoft. So now they are basically your contract research house for Microsoft, though they have some independence. And then there was a third thing, which was their belief that AGI [artificial general intelligence] is just around the corner, and they were going to be the one developing it before anyone. They just won’t.
How do you view the drama at OpenAI, when Sam Altman was booted as CEO and then returned to report to a different board? Do you think it had an impact on the research community or the industry?
I think the research world doesn’t care too much about OpenAI anymore, because they’re not publishing and they’re not revealing what they’re doing. Some former colleagues and students of mine work at OpenAI; we felt bad for them because of the instabilities that took place there. Research really thrives on stability, and when you have dramatic events like this, it makes people hesitate. Also, the other aspect important for people in research is openness, and OpenAI really isn’t open anymore. So OpenAI has changed in the sense that they are not seen much as a contributor to the research community. That is in the hands of open platforms…
…But isn’t an open source AI really difficult to control—and to regulate?
No. For products where safety is really important, regulations already exist. Like if you’re going to use AI to design your new drug, there’s already regulation to make sure that this product is safe. I think that makes sense. The question that people are debating is whether it makes sense to regulate research and development of AI. And I don’t think it does.
Couldn’t someone take a sophisticated open source system that a big company releases, and use it to take over the world? With access to source codes and weights, terrorists or scammers can give AI systems destructive drives.
They would need access to 2,000 GPUs somewhere that nobody can detect, enough money to fund it, and enough talent to actually do the job.
Some countries have a lot of access to those kinds of resources.
Actually, not even China does, because there’s an embargo.
I think they could eventually figure out how to make their own AI chips.
That’s true. But it’d be some years behind the state of the art. It’s the history of the world: Whenever technology progresses, you can’t stop the bad guys from having access to it. Then it’s my good AI against your bad AI. The way to stay ahead is to progress faster. The way to progress faster is to open the research, so the larger community contributes to it.
How do you define AGI?
I don’t like the term AGI because there is no such thing as general intelligence. Intelligence is not a linear thing that you can measure. Different types of intelligent entities have different sets of skills.
Once we get computers to match human-level intelligence, they won’t stop there. With deep knowledge, machine-level mathematical abilities, and better algorithms, they’ll create superintelligence, right?
Yeah, there’s no question that machines will eventually be smarter than humans. We don’t know how long it’s going to take—it could be years, it could be centuries.
At that point, do we have to batten down the hatches?
No, no. We’ll all have AI assistants, and it will be like working with a staff of super smart people. They just won’t be people. Humans feel threatened by this, but I think we should feel excited. The thing that excites me the most is working with people who are smarter than me, because it amplifies your own abilities.
But if computers get superintelligent, why would they need us?
There is no reason to believe that just because AI systems are intelligent they will want to dominate us. People are mistaken when they imagine that AI systems will have the same motivations as humans. They just won’t. We’ll design them not to.
What if humans don’t build in those drives, and superintelligence systems wind up hurting humans by single-mindedly pursuing a goal? Like philosopher Nick Bostrom’s example of a system designed to make paper clips no matter what, and it takes over the world to make more of them.
You would be extremely stupid to build a system and not build any guardrails. That would be like building a car with a 1,000-horsepower engine and no brakes. Putting drives into AI systems is the only way to make them controllable and safe. I call this objective-driven AI. This is sort of a new architecture, and we don’t have any demonstration of it at the moment.
2. China’s debt isn’t the problem – Michael Pettis
IMF in its latest Global Debt Monitor highlighted how China’s overall debt-to-GDP ratio has increased fourfold since the 1980s. It has been particularly rapid over the past decade. Over half of the increase in the entire global economy’s debt-to-GDP ratio since 2008 is solely due to an “unparalleled” rise in China, according to the IMF.
That $47.5tn total debt pile has grown further in 2023, which might mean that China has now finally overtaken the US in debt-to-GDP terms…
…However, the surge in Chinese debt is not itself the problem but rather a symptom of the problem. The real problem is the cumulative but unrecognised losses associated with the misallocation of investment over the past decade into excess property, infrastructure and, increasingly, manufacturing.
This distinction is necessary because much of the discussion on resolving the debt has so far focused on preventing or minimising disruptions in the banking system and on the liability side of balance sheets.
These matter — the way in which liabilities are resolved will drive the distribution of losses to various sectors of the economy — but it’s important to understand that the problems don’t emerge from the liability side of China’s balance sheets. They emerge from the asset side…
…In proper accounting, investment losses are treated as expenses, which result in a reduction of earnings and net capital. If, however, the entity responsible for the investment misallocation is able to avoid recognising the loss by carrying the investment on its balance sheets at cost, it has incorrectly capitalised the losses, ie converted what should have been an expense into a fictitious asset.
The result is that the entity will report higher earnings than it should, along with a higher total value of assets. But this fictitious asset by definition is unable to generate returns, and so it cannot be used to service the debt that funded it. In an economy in which most activity occurs under hard-budget constraints, this is a self-correcting problem. Entities that systematically misallocate investment are forced into bankruptcy, during which the value of assets is written down and the losses recognised and assigned.
But, as the Hungarian economist János Kornai explained many years ago, this process can go on for a very long time if it occurs in sectors of the economy that operate under soft-budget constraints, for example state-owned enterprises, local governments, and highly subsidised manufacturers.
In these cases, state-sponsored access to credit allows non-productive investment to be sustained. And as economic activity shifts to these sectors, the result can be many years of unrecognised investment losses during which both earnings and the recorded value of assets substantially exceed their real values. Because the debt that funds this fictitious investment cannot be serviced by the investment, the longer it goes on, the more debt there is.
But once these soft-budget entities are no longer able — or willing — to roll over and expand the debt, they will then be forced to recognise that the asset side of the balance sheet simply doesn’t generate enough value to service the liability side. Put another way, they will be forced to recognise that the real value of the assets on their balance sheets are less than their recorded value.
That is the real, huge and intractable problem China faces…
…The third and most important impact is what finance specialists call “financial distress” costs. In order to protect themselves from being forced directly or indirectly to absorb part of the losses, a wide range of economic actors — workers, middle-class savers, the wealthy, businesses, exporters, banks, and even local governments — will change their behaviour in ways that undermine growth.
Financial distress costs rise with the uncertainty associated with the allocation of losses, and what makes them so severe is that they are often self-reinforcing. As we’ve seen with the correction in China’s property sector, financial distress costs are almost always much higher than anyone expected.
The point is that resolving China’s debt problem is not just about resolving the liability side of the balance sheet. What matters more to the overall economy is that asset-side losses are distributed quickly and in ways that minimise financial distress costs. That is why restructuring liabilities must be about more than protecting the financial system. It must be designed to minimise additional losses.
3. The pharma industry from Paul Janssen to today: why drugs got harder to develop and what we can do about it – Alex Telford
The biopharmaceutical industry expends huge sums shepherding drug candidates through the development gauntlet and satisfying regulatory requirements. In 2022, the industry spent around $200 billion on R&D, more than four times the US National Institute of Health’s (NIH) budget of $48 billion. Pharmaceuticals is the third most R&D intensive sector in the OECD countries.
The bulk of that spending goes towards clinical trials and associated manufacturing costs; roughly 50% of total large pharma R&D spend is apportioned to phase I, II, and III trials compared to 15% for preclinical work. While early phases may cull more candidate compounds in aggregate, the cost of failure is highest during clinical development: a late-stage flop in a phase III trial hurts far more than an unsuccessful preclinical mouse study. By the time a drug gets into phase III, the work required to bring it to that point may have consumed half a decade, or longer, and tens if not hundreds of millions of dollars.
Clinical trials are expensive because they are complex, bureaucratic, and reliant on highly skilled labour. Trials now cost as much as $100,000 per patient to run, and sometimes up to $300,000 or even $500,000 per patient for resource-intensive designs, trials using expensive standard of care medicines as controls or as part of a combination, or in conditions with hard-to-find patients (e.g., rare diseases). When these costs are added on top of other research and development expenditures, like manufacturing, a typical phase I program with 20-80 trial participants can be expected to burn around $30m. Phase III programs, involving hundreds of patients, often require outlays of hundreds of millions of dollars. Clinical trials in conditions where large trials with tens of thousands of patients are standard, such as cardiovascular disease or diabetes, can cost as much as $1 billion.
Because executing late stage clinical trials and manufacturing enough of the drug to cover them is so expensive, companies prefer to manage risk by conducting studies sequentially, even though many steps could in principle be done in parallel.
A major reason that COVID-19 vaccine development was so fast was not because shortcuts were taken, but that the funding from operation warp speed and advance purchase agreements allowed companies to parallelize much of the process, scale up manufacturing early, and jump quickly into phase IIIs because they were insulated from the financial risk of failure. Early trial phases were combined in multiphase designs, Pfizer commandeered existing manufacturing infrastructure and repurposed it for COVID-19 vaccine production, and employees and regulators worked around the clock. The FDA and other regulators took reviewers off of non-COVID-19 drugs and redeployed them to review the COVID-19 vaccines; there were essentially no delays in safety reviews that you would otherwise see in other clinical trials. The little delays that crop up in development were powered through with extra manpower and resources: at one point during Operation Warp Speed the military recovered a vital piece of equipment needed to manufacture Moderna’s vaccine from a stalled train, and put it on an aeroplane so it could arrive in time. While the vaccines were approved under expedited emergency use regulatory pathways, they were nevertheless rigorously tested. Allocating such extensive resources for every new drug, as was done during the pandemic, is unsustainable and comes with substantial opportunity cost.
In business-as-usual times, the industry’s expenditure on drug R&D nets us about 40 new US FDA drug approvals a year — and a similar number (though not always exactly the same drugs) approved by the equivalent agencies in other regions, as well as some new indications for existing drugs.
All that money spent by the industry on R&D appeared to go a lot further in the past10. Despite continued growth in biopharmaceutical R&D expenditure, we have not seen a proportionate growth in output. Industry R&D efficiency — crudely measured as the number of FDA approved drugs per billion dollars of real R&D spend — has (until recently) been on a long-term declining trajectory11. This trend has been sardonically named “Eroom’s law” – an inversion of Moore’s law. Accounting for the cost of failures and inflation, the industry now spends about $2.5 billion per approved drug, compared to $40 million (in today’s dollars) when Janssen was starting out in 1953…
…Even though we’re spending more money than ever before, historical statistics on drug candidate failure rates suggest that we haven’t really gotten much better at developing drugs that succeed where it counts — in clinical trials. The real bottleneck is not finding drug candidates that bind and modulate targets of interest, it’s finding ones that actually benefit patients. Almost paradoxically, despite huge improvements in the technologies of drug discovery, the rate of new drug launches has hardly shifted in 50 years. High-throughput screening, new model systems, machine learning, and other fancy modern techniques have done little to change the statistic that 9 in 10 drug candidates that start clinical trials will fail to secure approval.
What’s behind this ‘Red Queen’ effect, where we seem to be expending more and more resources to keep running at roughly the same speed?
For one, as we’ve seen across scientific fields, new ideas are getting harder to find. There are more academic researchers than ever — 80,000 in the 1930’s vs. 1.5 million today in the USA — yet we have not seen a proportionate growth in the rate of meaningful discoveries. This may be because ideas are getting inherently more difficult to find, or it may be that the institutions and processes of science have become less effective: bogged down in bureaucracy, sclerotic, chasing the wrong metrics, and thereby limiting the impact of individual researchers.
The biopharmaceutical business is built on top of basic discoveries, and so it is not immune from this general trend afflicting all of science. Without the discovery of methods to stabilise coronavirus spike proteins and enhance immunogenicity prior to the pandemic, it’s unlikely that we would have had effective vaccines for COVID-19 as fast as we did. Imatinib, a breakthrough targeted therapy for a rare blood cancer, was predicated on the discovery of the mutant protein produced by the “Philadelphia chromosome” rearrangement. In support of the ‘low hanging fruit’ argument is data showing changes in the landscape of drug targets over time: compared to past decades, drugs in development are now much more frequently going after targets that would have historically been viewed as intractable. Modern protein targets are more likely to be disordered, with shallow or non-existing pockets for small molecules to bind, or otherwise difficult to interact with.
The more important reason for the decline in R&D efficiency, however, is that it is not enough for drugs to simply be novel and safe, they must also improve meaningfully over the available standard of care, which may include a large armamentarium of effective and cheap older drugs.
This is the so-called ‘better than Beatles’ problem. Imagine if in order to release new music it needed to be adjudicated as better than ‘Hey Jude’, or ‘Here comes the sun’ in a controlled experiment. New experimental music might have a hard time getting past the panel, and wouldn’t have the chance to refine its sound in future iterations. The situation for new drugs is somewhat analogous…
…Yet, even though there are major forces pushing against drug developers, there is a sense that the industry is still underperforming, and that it could do more. One reason for optimism can be seen in the recent flattening of the slope of Eroom’s law following decades of declining productivity. It remains to be seen whether the recent uptick is a sustained turnaround or not. The pessimistic view is that it is illusory, a result of how drugmakers have side-stepped fundamental productivity issues by focusing on developing drugs for niche subpopulations with few or no options where regulators are willing to accept less evidence, it’s easier to improve on the standard of care, and payers have less power to push back on higher prices: rare disease and oncology in particular. It’s no coincidence that investment has flowed into areas where regulatory restrictions have been relaxed and accelerated approvals are commonplace: 27% of FDA drug approvals in 2022 were for oncology, the largest therapeutic area category, and 57% were for rare/orphan diseases.
There is however, a more charitable and optimistic take for the flattening and possible reversal of Eroom’s law. The first possibility is that advances in basic science are finally being widely adopted in the drug development process and bearing fruit. Historically, it takes upwards of 20 years for new drug targets to lead to new medicines. Consider that the sequencing of the human genome was completed in 2003; genomics research has by now improved our understanding of many relatively simple monogenic genetic disease, and has identified new targets for more common conditions through genome-wide association studies (GWAS) that look for associations between gene variants and disease phenotypes in large populations. The PCSK9 inhibitors alirocumab and evolucumab, as an archetypal example, were developed after screening for genetic mutations in families with elevated cholesterol levels identified the PCSK9 gene as a key driver of cholesterol regulation. Drug programs with genetic support are more likely to succeed, and we may have only recently truly started to benefit from our improved understanding of human genetics.
4. Peter Lynch 1994 National Press Club Lecture (transcript here)- Monroe Carmen and Peter Lynch
Peter Lynch: And if you can’t explain – I’m serious – you can’t explain to a 10 year old in two minutes or less why you own a stock, you shouldn’t own it. And that’s true, I think, about 80% of people that own stocks.
And this is the kind of stock people like to own. This is the kind of company people adore owning. This is a relatively simple company. They make a very narrow, easy to understand product. They make a 1 MB SRAM CMOS bipolar risk floating point data, I/O array processor with an optimising compiler, a 16-dual port memory, a double diffused metal oxide semiconductor monolithic logic chip with a plasma matrix vacuum fluorescent display. It has a 16 bit dual memory. It has a Unix operating system, four whetstone megaflop polysilicone emitter, a high bandwidth – that’s very important – six gigahertz metalization communication protocol, an asynchronous backward compatibility, peripheral bus architecture, four wave interweave memory, a token ring and change backplane. And it does in 15 nanoseconds of capability. Now, if you own a piece of crap like that, you will never make money. Never. Somebody will come along with more wetstones or less wetstones or a big omega flop or a small omega flop. You won’t have the foggiest idea what’s happened. And people buy this junk all the time.
I made money in Dunkin Donuts. I can understand it. When there was recessions, I didn’t have to worry about what was happening. I could go there and people were still there. I didn’t have to worry about low price Korean imports. I can understand it. And you laugh. I made 10 or 15 times my money in Dunkin’Donuts. Those are the kind of stocks I can understand. If you don’t understand, it doesn’t work…
…Peter Lynch: I’m trying to convince people there is a method. There are reasons for stocks that go up. Coca Cola, this is very magic. It’s a very magic number. Easy to remember. Coca Cola is earning 30 times per share what they did 32 years ago. The stock has gone up 30 fold. Bethlehem Steel is earning less than they did 30 years ago. The stock is half its price of 30 years ago. Stocks are not lottery tickets. There’s a company behind every stock. The company does well. The stock does well. It’s not that complicated…
…Peter Lynch: Considering there’s not that many billionaires on the planet, I had logic – so I had syllogism and studied these when I was at Boston College – there can’t be that many people who can predict interest rates because there’d be lots of billionaires and no one can predict the economy.
A lot of people in this room were around in 1981 and 82 when we had a 20% prime rate with double digit inflation, double digit digit unemployment. I don’t remember anybody telling me in 1981 about it. I didn’t read – I study all this stuff. I don’t remember anybody telling we’re going to have the worst recession since the Depression. So what I’m trying to tell you, it’d be very useful to know what the stock market is going to do. It’d be terrific to know that the Dow Jones average year from now would be X, that we’re going to have a full scale recession or interest rate is going to be 12%. That’s useful stuff. You never know it though. You just don’t get to learn it. So I’ve always said if you spend 14 minutes a year in economics, you’ve wasted 12 minutes. And I really believe that.
Now, I have to be fair. I’m talking about economics in the broad scale, predicting the downturn for next year or the upturn, or M1 and M2, 3B, and all these all these M’s. I’m talking about economics, to me, is you talk about scrap prices. When I own auto stocks, I want to know what’s happening to used car prices. When used car prices are going up, it’s a very good indicator. When I own hotel stocks, I want to know hotel occupancies. When I own chemical stocks, I want to know what’s happening to price of ethylene. These are facts. If aluminium inventories go down five straight months, that’s relevant. I can deal with that. Home affordability, I want to know about, when I own Fannie Mae or I own a housing stock, these are facts. There are economic facts and there’s economic predictions. And economic predictions are a total waste.
And interest rates. Alan Greenspan is a very honest guy. He would tell you that he can’t predict interest rates. He could tell you what short rates are going to do in the next six months. Try and stick him on what the long term rate will be three years from now. He’ll say, “I don’t have any idea.” So how are you, the investor, supposed to predict interest rates if the Head of the Federal Reserve can’t do it? So I think that’s – But you should study history, and history is the important thing you learn from.
What you learn from history is the market goes down. It goes down a lot. The math is simple. There’s been 93 years, a century. This is easy to do. The market’s had 50 declines of 10% or more. So 50 declines in 93 years, about once every two years the market falls 10%. We call that a correction. That means – that’s a euphemism for losing a lot of money rapidly, but we call it a correction. So 50 declines in 93 years, about once every two years the market falls 10%. Of those 50 declines, 15 have been 25% or more. That’s known as a bear market. We’ve had 15 declines in 93 years. So every six years the market’s going to have a 25% decline. That’s all you need to know…
…Peter Lynch: So you only need a few stocks in your lifetime. They’re in your industry. I think of people – if you’d worked in the auto industry, let’s say you’re an auto dealer the last 10 years. You would have seen Chrysler come up with the minivan. If you’re a Buick dealer, a Toyota dealer, Honda dealer, you would have seen the Chrysler dealership packed with people. You could have made 10 times your money on Chrysler. A year after the minivan came out, Ford introduces the Taurus Sable, the most successful line of cars in the last 20 years. Ford went up sevenfold on the Taurus Sable. So if you’re a car dealer, you only need to buy a few stocks every decade…
…Peter Lynch: And then I want to conclude with, there’s always something to worry about. If you own stocks, there’s always something to worry about. You can’t get away from it. What happens in the 50s, people were worried about the only reason we got out of the depression was World War II. We got another recession in the early 50s. We said, “We’re going to go right back into a depression.” People worried about a Depression in the 50s and were worried about nuclear war. Back then, the little warheads they had then, they couldn’t blow up McLean, West Virginia, or McLean, Virginia, or Charlestown. Now, all these countries that end in ‘stan – there’s nine of these ‘stan countries that have come out of Russia. They all have enough warheads to blow the world up, and no one worries about.
When I was a kid, people were building fallout shelters and we used to have this civil defense drill. Remember this one in high school? You get under your desk. I never thought even then that was a particularly good thing to do. They’d blow us and some people put a hat would, all get under our desk. But in the ‘50s, people wouldn’t buy stocks. Except for the ‘80s, the ‘50s was the best decade in the century of the stock market, and people wouldn’t buy stocks in the ‘50s because they’re worried about nuclear war and they’re worried about depression. Remember when oil went from $4 to $40 and it was going to go to $100 and we’re going to have a depression. Remember that one? Well, about three years later, the same experts, now higher paid, oil is now at $10. They said it was going to go to $4 and we’re going to have a depression…
…Monroe Carmen: Are you concerned about the volatility in the financial markets today? Do you think something needs to be done to reduce it?
Peter Lynch: I love volatility. I remember when in 1972, the market went down dramatically and Taco Bell went from $14 to $1. They had no debt, they never had a restaurant close. And I started buying at $7, but I kept onto it and it went to $1. And it was the largest position in Magellan in 1978 when it was bought out for $42 by PepsiCola. And I think it would have gone to $400 if they didn’t buy it out. I think volatility is terrific.
I think these collars are very important. I don’t think the market going up 80 points one day and down 80 the next is a good thing for the public. I think that’s not a very good thing, but I think all these collars and all these other things to keep the volatility down each day is important. But the market’s going to go up and down. Human nature hasn’t changed a lot in 25,000 years and some event will come out of left field and the market will go down. Or the market will go up. So volatility will occur. Markets will continue to have these ups and downs. I think that’s a great opportunity if people can understand what they own. If they don’t understand what they own, they can own mutual funds. Try and figure out mutual funds they own and keep adding to it.
Basically, corporate profits have grown about 8% a year historically. So corporate profits double about every nine years. The stock market ought to double about every nine years. So I think the next market is about 3,800 today, 3,700. I’m pretty convinced the next 3,800 points will be up, it won’t be down. The next 500 points, the next 600 points, I don’t know which way they’re going. So the market ought to double in the next eight or nine years, it ought to double again in the eight or nine years after that because profits will go up 8% a year and stocks will follow. That’s all there is to it…
…Peter Lynch: October has always been a special month. I remember in 1987 I was very convinced that the market was not in trouble and I didn’t worry about things. And Carol and I had planned this great golf vacation to Ireland. And we’re going to visit one course and stay in a little house and visit another. Go all along the west coast of Ireland and play golf. And we left on a Thursday night and the market went down 55 points that day, which was not too good. And the next day we got to Ireland. Because of the time difference, we’d completed our day and I got back to hotel and I called and the market had gone down 112 on Friday. I said to Carolyn, “I think if the market goes down on Monday, we’re going to have to go back.” We stayed there for the weekend and on Monday the market went down 508 points and my fund went from, I think, $12 billion to $8 billion and that gets your attention. In two working days. I said, by the end of this week, I’d have no funds.
Now, there wasn’t a lot I could do. I mean, here I was on Monday, because the market didn’t open by 12:00 – it was in Ireland, it was still 07:00 in New York. So we did spend that day and we played around golf in the morning. Then we went somewhere and sort of watched the market deteriorate. And I did come back. There wasn’t nothing I could do. I mean, just nothing I could do about it. But I think my shareholders, they called up and they said, “What’s Lynch doing?” They said, “Well, he’s on the 6th hole and he’s even par up to now, but he’s in a trap. This could be a triple bogey here. This could be a big inning.” And I don’t think that’s exactly what they want to hear. So I could do something about this damn thing. So I came back home and suffered with everybody else…
…Peter Lynch: I had this biggest position in my fund one time was Hanes, which owned Leggs and was a huge stock. And it was bought eventually by Consolidated Foods and it was the best division of Consolidated Foods. But it’s my biggest position. Made a monopoly on this Leggs. And Leggs is a really big hit. And I knew somebody would come along with a new product, and it was – Kaiser Roth introduced No Nonsense. I was worried that this thing was better and I couldn’t quite figure out what was going on. So I went to the supermarket and I bought 62 pairs of No Nonsense. Different colors, different shapes, different – they must have wondered what kind of house I had when I was going back. But I brought it in. I brought to the office and I passed that to anybody, male or female, anybody who wanted these things, just take them home and tell me how it is. And they came back in about three weeks and they said, it’s not as good. And that’s what research is. That’s all it was. And I held onto Hanes and the stock was a huge stock. So that’s what it’s about.
5. From Penny-Farthings to Pounds: The Great British Bicycle Bubble of 1896 – Nicholas Vardy
The bicycle’s humble beginnings can be traced back to the “dandy horse” – a pedal-less bike patented in Germany in 1818. Over the next half-century, inventors tweaked the design. In the 1860s, a French enthusiast added pedals and a rotary crank to the dandy horse, creating a rudimentary version of the modern bicycle.
However, the later penny-farthing design, with its oversized front wheel, proved hazardous and cumbersome. It wasn’t until the 1890s that the innovations of chain-driven transmission captured the British public’s imagination. It was also when John Boyd Dunlop invented the pneumatic tire in 1887, making it easy to ride bicycles on hard roads.
Overnight, the bicycle became a technological marvel and a revolutionary new mode of transportation.
What accounted for the bicycle’s remarkable early success?
First, the bicycle liberated the British public from the constraints of railway schedules. The bicycle became synonymous with the freedom to travel when you want, where you want.
Second, the bicycle was cheaper to buy and maintain than horses. It also provided a much-needed solution to the horse manure-laden streets of London.
Third, even women could ride bicycles. That alone doubled the size of its potential market. By 1895, the bicycle came to represent feminine independence.
The bicycle had a large effect on Britain’s infrastructure.
Much like their counterparts did for railroads 60 years before, cycling organizations began to lobby for a network of good roads to connect cities and rural communities. The first roads were built for commuters and travelers on cycles, not in cars.
With every successful new road, the pool of consumers and their need for a bicycle grew. Predictions of hypergrowth abounded. Companies were keen to meet the seemingly endless demand…
…The venture capitalists of their day in the United States and Britain quickly pounced. They bought up Bicycle companies. They bolstered balance sheets with vast amounts of intangible goodwill and patents. This financial sleight of hand allowed them to leverage companies up to invest in increased production.
As a result, the 1890s saw a tremendous boom in bicycle shares in the Birmingham stock exchange, not dissimilar to today’s EV boom.
In 1896, there were roughly 20 British bicycle companies. But demand was quickly outpacing supply. Enter Ernest Terah Hooley, a property dealer from Birmingham, who saw an opportunity. He bought a company called Pneumatic Tyre for a staggering 3 million pounds, a hefty premium given its modest profits.
Thanks to Hooley’s sales prowess, shares in the newly renamed Dunlop Pneumatic Tyre Company skyrocketed by 1,138% in the spring of 1896. Other British bicycle companies followed suit, with their share prices tripling. Speculators made fortunes overnight, and more and more people flocked to get in on future growth.
In 1896 alone, 363 cycle, tube, or tire firms were listed on the London Stock Exchange, with another 238 added in the first half of 1897. The British press hailed the bicycle as a revolutionary technology. The Financial Times even dedicated a daily page to the share prices of bicycle companies…
…Then, the narrative began gradually to shift.
Cycles were made not just in Birmingham but also in the United States.
Suddenly, advances in manufacturing meant new bicycles flooded the market.
Investors learned that expansion in the market did not necessarily translate into the same profit growth.
Competition soon drove prices down. Profit margins fell…
…The bicycle companies had become overleveraged. Vast orders anticipated that failed to come through. You only need to buy a new bicycle every five or ten years. The market became saturated. Sales growth entered a slow-motion collapse. Intense competition led to oversupply and plummeting prices.
Meanwhile, technology had advanced. Other competitors emerged. The automobile was even more of a game-changer than the bicycle. The fortunes associated with the bicycles’ promise of hypergrowth dissipated rapidly.
Only with the benefit of hindsight did it become clear that bicycles were a bubble…
…By December 1897, an index of bicycle-related stocks had plummeted by 40%. In 1898, bicycle stocks traded at an average of 71% below their peaks. More than 80% of the companies participating in the 1890s British bicycle boom went bust.
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