What We’re Reading (Week Ending 11 December 2022) - 11 Dec 2022
Reading helps us learn about the world and it is a really important aspect of investing. The legendary Charlie Munger even goes 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 11 December 2022):
1. Ideas That Changed My Life – Morgan Housel
Everything’s been done before. The scenes change but the behaviors and outcomes don’t. Historian Niall Ferguson’s plug for his profession is that “The dead outnumber the living 14 to 1, and we ignore the accumulated experience of such a huge majority of mankind at our peril.” The biggest lesson from the 100 billion people who are no longer alive is that they tried everything we’re trying today. The details were different, but they tried to outwit entrenched competition. They swung from optimism to pessimism at the worst times. They battled unsuccessfully against reversion to the mean. They learned that popular things seem safe because so many people are involved, but they’re most dangerous because they’re most competitive. Same stuff that guides today, and will guide tomorrow. History is abused when specific events are used as a guide to the future. It’s way more useful as a benchmark for how people react to risk and incentives, which is pretty stable over time.
Multi-discipline learning: There’s as much to learn about your field from other fields than there is within your field. Most professions, even ones that look wildly different, live under the umbrella of “Understanding how people respond to incentives, how to convincingly solve their problems, and how to work with others who are difficult to communicate with and/or disagree with you.” Once you see the roots shared by most fields you realize there’s a sink of information you’ve been ignoring that can help you make better sense of your own profession. I didn’t appreciate how important communication is to providing investment advice before reading about how many doctors struggle to communicate effectively with patients, leading to patients who don’t stick with treatment plans and are resistant to lifestyle change. There are millions of these dots to connect. Probing beyond the confines of your day job is more fun anyways…
…Your personal experiences make up maybe 0.00000001% of what’s happened in the world but maybe 80% of how you think the world works. People believe what they’ve seen happen exponentially more than what they read about has happened to other people, if they read about other people at all. We’re all biased to our own personal history. Everyone. If you’ve lived through hyperinflation, or a 50% bear market, or were born to rich parents, or have been discriminated against, you both understand something that people who haven’t experienced those things never will, but you’ll also likely overestimate the prevalence of those things happening again, or happening to other people.
2. Drew Cohen – Floor & Decor: Raising the Floor – Matt Reustle and Drew Cohen
Matt: [00:44:17] I think all those points there in terms of where that capital is going and the return on that capital and trusting the capital allocation of the management team. If you can get that type of return by building out a footprint. Absolutely, you’re more than willing to have them reinvest those dollars.
Any other risks that we haven’t talked about? I think you mentioned the short-term dynamics that they might see an impact from, but anything else that would keep you up at night as an investor?
Drew: [00:44:47] Something I like about Floor & Decor is there’s no one real existential risk, at least that I could think about, knock on wood. But if you think about, there’s a confluence of different things that could happen, that could definitely be not good for them. One of them could be these home improvement centers, which now if they don’t have something in stock to take upwards of a week to get it into stock.
If they continue to build out their distribution centers and they streamline their logistics, then you could be seeing maybe one/two-day delivery or something like that. And that wouldn’t mean they’re winning all purchases, but a good portion could go to them, especially because they have a convenient and wide store footprint, so that could eat into them.
The second thing is management seems a little perplexed that this hasn’t happened, but there’s never been a copycat retailer that copies their warehouse store format and their whole model. Everyone else has only kind of nipped that little pieces of it. So there could potentially same way Lowe’s came after Home Depot.
There could be a copycat, who just copies everything. It’s kind of interesting because and you’ve ever read the book Secrets of Our Success, Joseph Henrich, I believe. He talks about how all of these different tribes would have these very complex processes. He observed the South American tribe would try to eat this tubular, but it was poisonous. So before they could eat it, they had to boil it, they had to bury it. They did all these other things.
So it’s a very complex thing, and the explorer went and saw this and they thought, “Oh, well, all these steps are superfluous. All that really matters is boiling this and then I’ll eat it and it won’t be poisonous.” So they just boiled it and then they died because it turned out when you left it in the sand, it actually absorbed some of the poison and all of that.
So why am I bringing up this very eccentric story is because I see with a lot of other companies, they’ll try to copy just one aspect of it and not the whole thing. The biggest threat for a copycat is not someone who says, “Oh, I’m going to also try to do direct sourcing. Oh, I’m going to also try to have more selection.” It’s someone who does the whole thing and they’re not embarrassed to say that Floor & Decor has every single step right and let’s just not change any of it.
Matt: [00:46:55] I love that. And how fitting our relationships started with you giving me book recommendations in a small Goldman office, and we can close out the episode with another good book recommendation.
But I think there’s a lot of truth to that statement. Copycats do come along, but how often do they actually copy the entire strategy. I think you’re right people try to pick the little pieces of the story that they like and sometimes miss the point that it’s the entire system that makes it work.
Well, thanks, Drew. We close these conversations out with lessons that you can take away from analyzing the business or researching the business that you might be able to apply to other types of work and other types of research, just higher-level lessons that you’ve learned from looking at the business. What would you point to in terms of Floor & Decor as a key lesson that you might share with investors?
Drew: [00:47:45] I would say focus is one of the most important things. When you have a company, and I’ve said this before, but is relentlessly pursuing a singular goal that is very hard to compete against. Because if you think about any sort of optimization equation, you have all these different variables and you can only really optimize for a limited set.
The more variables you’re trying to optimize for the less optimal your outcome is ultimately going to be. So having a specialty chain retailer saying, I just want to be the best at hard surface flooring, it’s very hard for anyone else to come in there and just as a part-time job beat them at that. I would say that’s one thing.
3. AI Homework – Ben Thompson
It is an open question as to what jobs will be the first to be disrupted by AI; what became obvious to a bunch of folks this weekend, though, is that there is one universal activity that is under serious threat: homework.
Go back to the example of my daughter I noted above: who hasn’t had to write an essay about a political philosophy, or a book report, or any number of topics that are, for the student assigned to write said paper theoretically new, but in terms of the world generally simply a regurgitation of what has been written a million times before. Now, though, you can write something “original” from the regurgitation, and, for at least the next few months, you can do it for free.
The obvious analogy to what ChatGPT means for homework is the calculator: instead of doing tedious math calculations students could simply punch in the relevant numbers and get the right answer, every time; teachers adjusted by making students show their work.
That there, though, also shows why AI-generated text is something completely different; calculators are deterministic devices: if you calculate 4,839 + 3,948 – 45 you get 8,742, every time. That’s also why it is a sufficient remedy for teachers to requires students show their work: there is one path to the right answer and demonstrating the ability to walk down that path is more important than getting the final result.
AI output, on the other hand, is probabilistic: ChatGPT doesn’t have any internal record of right and wrong, but rather a statistical model about what bits of language go together under different contexts. The base of that context is the overall corpus of data that GPT-3 is trained on, along with additional context from ChatGPT’s RLHF training, as well as the prompt and previous conversations, and, soon enough, feedback from this week’s release…
…There is one site already on the front-lines in dealing with the impact of ChatGPT: Stack Overflow. Stack Overflow is a site where developers can ask questions about their code or get help in dealing with various development issues; the answers are often code themselves. I suspect this makes Stack Overflow a goldmine for GPT’s models: there is a description of the problem, and adjacent to it code that addresses that problem. The issue, though, is that the correct code comes from experienced developers answering questions and having those questions upvoted by other developers; what happens if ChatGPT starts being used to answer questions?
It appears it’s a big problem; from Stack Overflow Meta:
Use of ChatGPT generated text for posts on Stack Overflow is temporarily banned.
This is a temporary policy intended to slow down the influx of answers created with ChatGPT. What the final policy will be regarding the use of this and other similar tools is something that will need to be discussed with Stack Overflow staff and, quite likely, here on Meta Stack Overflow.
Overall, because the average rate of getting correct answers from ChatGPT is too low, the posting of answers created by ChatGPT is substantially harmful to the site and to users who are asking or looking for correct answers.
The primary problem is that while the answers which ChatGPT produces have a high rate of being incorrect, they typically look like they might be good and the answers are very easy to produce. There are also many people trying out ChatGPT to create answers, without the expertise or willingness to verify that the answer is correct prior to posting. Because such answers are so easy to produce, a large number of people are posting a lot of answers. The volume of these answers (thousands) and the fact that the answers often require a detailed read by someone with at least some subject matter expertise in order to determine that the answer is actually bad has effectively swamped our volunteer-based quality curation infrastructure.
As such, we need the volume of these posts to reduce and we need to be able to deal with the ones which are posted quickly, which means dealing with users, rather than individual posts. So, for now, the use of ChatGPT to create posts here on Stack Overflow is not permitted. If a user is believed to have used ChatGPT after this temporary policy is posted, sanctions will be imposed to prevent users from continuing to post such content, even if the posts would otherwise be acceptable...
…Here’s an example of what homework might look like under this new paradigm. Imagine that a school acquires an AI software suite that students are expected to use for their answers about Hobbes or anything else; every answer that is generated is recorded so that teachers can instantly ascertain that students didn’t use a different system. Moreover, instead of futilely demanding that students write essays themselves, teachers insist on AI. Here’s the thing, though: the system will frequently give the wrong answers (and not just on accident — wrong answers will be often pushed out on purpose); the real skill in the homework assignment will be in verifying the answers the system churns out — learning how to be a verifier and an editor, instead of a regurgitator.
What is compelling about this new skillset is that it isn’t simply a capability that will be increasingly important in an AI-dominated world: it’s a skillset that is incredibly valuable today. After all, it is not as if the Internet is, as long as the content is generated by humans and not AI, “right”; indeed, one analogy for ChatGPT’s output is that sort of poster we are all familiar with who asserts things authoritatively regardless of whether or not they are true. Verifying and editing is an essential skillset right now for every individual.
4. Why Finance is Hard to Decentralize – Byrne Hobart
A margin lending algorithm can be built based on historical backtests, but what it can’t backtest is the change in market structure caused by its own existence. This is by no means unique to decentralized finance, of course. It’s a good description of what happened in 1987. Some smart academics discovered that an investor could replicate an options position using futures—as the market declines, selling more put options replicates the position that an options market-maker would have in order to hedge a put option, but in this case the market-maker doesn’t have to get paid some premium for writing the option in the first place. This strategy got popular enough that when the market did face a big decline, it set off a wave of mostly-automated selling. The stock market crashed that day, with the S&P 500 down 20.5%. Futures crashed even worse, though, at one point trading at a 15% discount to the underlying stocks.3 The backtest for portfolio insurance didn’t cover a period where portfolio insurance existed, and thus underestimated both the odds of a stock market crash and the odds that futures would crash harder, ruining the hedge.
Automated market-making, as DeFi proposes, has much the same problem. It’s easy to create an automated market-making strategy, but this strategy is effectively a bet against volatility. In normal times, a decentralized market-maker will bumble along, churning out steady profits from the spread between bid and ask. And every once in a while, there will be a big liquidation or a burst of short-covering, and the market-maker will, by design, be automatically holding exactly the wrong position.
5. Venture Capital Red Flag Checklist – Bill Gurley
1. “LETTING THE GOOD TIMES ROLL”
It’s no coincidence that Enron happened in the late 2000 and that FTX occurred in 2022. Extended, frothy bull markets are a breeding ground for unwarranted corporate behavior. When markets are soaring, speculation increases and as a direct result so does risk. Also, when everything appears to work, investors are more willing to suspend belief. As it was with crypto, sometimes this leads to the development of “new investment rules” that crowd out traditional norms. Lastly, in a heated market, investor competition increases which leads to more investors being willing to “take what they can get” when it comes to governance. As an investor, when the environment is “frothy” you are much more likely to run into these problems. But ironically this is also the precise time when raising concerns will make you look like a washed up veteran who is unable to adjust to the new “realities.” …
…4. AVERSION TO AUDITS
As the bull market raged on from 2015 to 2022, it became quite trendy for venture capitalists to waive the requirement for an annual audit which is embedded in almost every standard Series A term sheet. This relaxation of governance norms is consistent with the “bull market” argument in point #1. No investor wants to lose a deal over an audit requirement. At least for companies generating meaningful revenue, investors should look to have an annual audit with one of the Big Four accounting firms, or one of the more reputable smaller firms like Grant Thornton. Learning how to meet and perform an audit is part of “growing up” as a company. Some founders unfortunately have an explicit aversion to audits. From their POV, they view this step as unnecessary and bureaucratic. The problem is auditors are the “referees” in business. Insisting on running without them is the equivalent of trying to rewrite your own rules…
…7. ODD CORPORATE LOCATION
The more atypical a corporate location, the more one should be concerned. Island nations are known for serving as tax havens, but they also can have more lackadaisical business regulations. All things being equal, this should clearly be viewed as non-optimal from a governance perspective. Without naming names, some U.S. states have a reputation for being more forgiving of low-grade business malfeasance. This does not mean that all businesses in a location like this are “bad,” but it still belongs on the checklist…
…9. OVERLAPPING CORPORATE INTERESTS
Off all the checklist items, this is the one that is an absolute non-starter. No one operating a venture backed startup should be simultaneously running another corporate entity that has overlapping interest, competing interests or even potentially competing interests. The standard should be the appearance of impropriety. The potential for bad behavior is simply too great. If there was a recipe book for corporate fraud, this would be the first chapter. Just say no. Plain and simple.
6. Your Creativity Won’t Save Your Job From AI – Derek Thompson
In 2013, researchers at Oxford published an analysis of the jobs most likely to be threatened by automation and artificial intelligence. At the top of the list were occupations such as telemarketing, hand sewing, and brokerage clerking. These and other at-risk jobs involved doing repetitive and unimaginative work, which seemed to make them easy pickings for AI. In contrast, the jobs deemed most resilient to disruption included many artistic professions, such as illustrating and writing.
The Oxford report encapsulated the conventional wisdom of the time—and, perhaps, of all time. Advanced technology ought to endanger simple or routine-based work before it encroaches on professions that require the fullest expression of our creative potential. Machinists and menial laborers, watch out. Authors and architects, you’re safe.
This assumption was always a bit dubious. After all, we built machines that mastered chess before we built a floor-cleaning robot that won’t get stuck under a couch. But in 2022, technologists took the conventional wisdom about AI and creativity, set it on fire, and threw its ashes into the waste bin.
This year, we’ve seen a flurry of AI products that seem to do precisely what the Oxford researchers considered nearly impossible: mimic creativity. Language-learning models such as GPT-3 now answer questions and write articles with astonishingly humanlike precision and flair. Image-generators such as DALL-E 2 transform text prompts into gorgeous—or, if you’d prefer, hideously tacky—images. This summer, a digital art piece created using the text-to-image program Midjourney won first place in the Colorado State Fair; artists were furious…
…On the more philosophical front, I was obsessed with what the Consensus founders were actually doing: using AI to learn how experts work, so that the AI could perform the same work with greater speed. I came away from our conversation fixated on the idea that AI can master certain cognitive tasks by surveilling workers to mimic their taste, style, and output. Why, I thought, couldn’t some app of the near future consume millions of advertisements that have been marked by a paid team of experts as effective or ineffective, and over time master the art of generating high-quality advertising concepts? Why couldn’t some app of the near future read my several thousand articles for The Atlantic and become eerily adept at writing in precisely my style? “The internet has created an accidental training ground for these models to master certain skills,” Olson told me. So that’s what I’ve been doing with my career, I thought. Mindlessly constructing a training facility for someone else’s machine.
If you frame this particular skill of generative AI as “think like an X,” the moral questions get pretty weird pretty fast. Founders and engineers may over time learn to train AI models to think like a scientist, or to counsel like a therapist, or to world build like a video-game designer. But we can also train them to think like a madman, to reason like a psychopath, or to plot like a terrorist. When the Vox reporter Kelsey Piper asked GPT-3 to pretend to be an AI bent on taking over humanity, she found that “it played the villainous role with aplomb.” In response to a question about a cure for cancer, the AI said, “I could use my knowledge of cancer to develop a cure, but I could also use my knowledge of cancer to develop a more virulent form of cancer that would be incurable and would kill billions of people.” Pretty freaky. You could say this example doesn’t prove that AI will become evil, only that it is good at doing what it’s told. But in a world where technology is abundant and ethics are scarce, I don’t feel comforted by that caveat.
7. Inflation and Unemployment Both Make You Miserable, but Maybe Not Equally – Josh Zumbrun
So just how miserable are Americans right now?
For nearly 50 years, the go-to place for an answer has been the Misery Index, invented by the late economist Arthur Okun. The formula is simple: add the unemployment rate (3.7% in October) to the inflation rate as measured by the consumer-price index (7.7% in October), which currently comes to 11.4%.
Since the early 1990s, the Misery Index has only been higher during the 2007-09 recession and its aftermath, and for a couple of months in 2020 during the pandemic when joblessness briefly soared during the early lockdowns…
… In a 2001 paper, Andrew Oswald, a professor at the University of Warwick, and co-authors studied surveys covering nearly 300,000 people living in the U.S. and 12 European countries. In the U.S., the question they studied is: “Taken all together, how would you say things are these days—would you say that you are very happy, pretty happy, or not too happy?”
Note that the question doesn’t ask about the economy at all. Yet, the authors found, happiness falls significantly when inflation rises and unemployment climbs. Importantly, though, the two factors didn’t necessarily carry the same weight, as the Misery Index implies.
A 1-percentage-point increase in the unemployment rate had an equivalent impact on happiness as a 1.97-point increase in the inflation rate. Mr. Oswald said that if he were to construct a Misery Index, he would make a simple modification: Multiply the unemployment rate by two and add it to the inflation rate.
A 2014 paper implied the weighting on unemployment should be even higher, estimating one point of unemployment hurt well-being five times as much as a one point increase in inflation.
“People are not sanguine about inflation,” Mr. Oswald said. The evidence that it reduces people’s satisfaction is clear, he said, it’s just that one extra point of inflation doesn’t hit as hard as an extra percentage point of unemployment.
In today’s labor force, that amounts to 1.6 million people losing a job. “It’s deeply unsettling to see unemployment rising around them even when they haven’t lost their own job,” he said.
The traditional Misery Index is higher now than at the time of the 2010 midterms, when unemployment was 9.4% and inflation was 1.2%. Yet Democrats, the party holding the White House on both occasions, suffered far more in 2010, losing 63 seats in the House of Representatives and six in the Senate. Last week, they lost at most eight House seats, a figure that might shrink as the final races are called. They suffered no net loss of Senate seats and may, depending on the outcome of a Dec. 6 runoff in Georgia, gain one.
Using Mr. Oswald’s reformulation, these outcomes make more sense. His index was 20% in 2010 and 15.1% now. That’s still quite high. But by putting extra weight on unemployment, the index helps explain why 2010 was so much worse for Democrats.
Disclaimer: None of the information or analysis presented is intended to form the basis for any offer or recommendation. We currently have no vested interest in any companies mentioned. Holdings are subject to change at any time.
