Longreads + Open Thread
EA, Nomura, Forecasting, Solo Founders, Odds, Talent, Walmart
Trevor Klee writes that the Effective Altruism movement is subsidizing too much navel-gazing. It's a fun phenomenon to explore: there are a number of incredibly wealthy people who have affinity for the Effective Altruist worldview, in part because an increasing number of industries provide outsized rewards to people who extrapolate from a small set of first principles. That kind of thinking also makes people more prone to being EAs. But the high ratio of money to projects means there's a demand for intermediate efforts to figure out where the money should be directed—just like the rest of the economy, growth means that more resources are spent on various kinds of allocation rather than production. But, also like the rest of the economy, allocation can suck up arbitrarily vast resources and shows limited productivity gains.
Jamie Catherwood at Investor Amnesia has a good profile of Tokushichi Nomura II, founder of the eponymous brokerage. Founding a financial institution that sticks around for a long time seems to be a two-step process, where step 2 is to be very cautious and careful in order to preserve your capital and brand name, and step 1 is to be insanely risk-tolerant in order to get capital worth preserving. Nomura definitely did that—at one point he stole money from a firm he was working for and used the collateral to speculate, and early in his career he cornered the market in a textile firm. Amazingly, Nomura still owns 2.9% of that same textile company over a century later.
Sam Atis interviews Michael Story on forecasting and prediction markets. Prediction markets are conceptually fun (see Diff pieces here and here), but they haven't taken off as a way to guide the decisions of big companies. As the interview notes, there's a fun paradox here. From the interview: "Forecasting might be overrated. Nearly all forecasters are paid more by their day jobs to do something other than forecasting. The market message is “don’t forecast”!" One answer to that problem is that accurate views of the future are somewhere between useful and necessary, but they're almost never the majority of what successful people spend their time on. So over-indexing to good predictions might mean under-indexing to using those predictions effectively.1
Florent Crivello makes the case against cofounders. This is one of those pieces you should absolutely read if you instantly decided not to based on the thesis: it's about 10% devoted to the anti-cofounder argument, 50% to addressing objections, and 40% advice for people who read it and decide the arguments are compelling but not quite strong enough.
Terry Tao has a fun article starting with the viral news story about a lottery in the Philippines with 433 winners and moving on to a great discussion of how to think about reports of improbable events. The key insight is that it's very hard to compute the base rate, and it's also hard to compute the conditional probability of hearing about something. It's important to cultivate this instinct because the current media environment is very, very good at drawing attention to unusual events.
Talent, by Tyler Cowen and Daniel Gross, is partly a lengthy musing on the nature of extraordinary skill and how to find it, and partly a collection of some of the world's most prying interview questions. (“What are the open tabs in your browser right now?” has gotten the most attention, but there are many others—a personal favorite of mine was "How do you think this interview is going?"). Successful efforts to spot unusually talented people have a short half-life, because if they work, they create a prestigious filter that people will try to game. And even well-designed filters have some weak points. The alpha from the specifics in this book won't last, but the alpha from applying the general principles will be high for a while.
Sam Walton: Made In America. This book, like other books about low-priced retail, is partly a story of constantly learning new things about elasticity. Walton's story is full of cases where big discounts on particular products brought in an endless stream of customers, who also wound up paying higher markups on other stuff. The book is in some ways very hard to apply to tech startups, because Walmart grew in a different funding environment and its incremental margins were always closer to zero than to infinity. But in other ways, it's directly relevant: Walmart could make money in smaller markets than its competitors, and it eventually learned how to surround big cities by building stores in each outlying area. It's easier for a business like this to move into expensive places than for a structurally higher-cost one to go downmarket, which is exactly the lesson that companies like neobanks have learned in a different context.
Drop in any links or thoughts of interest to Diff readers.
A topic I've been mulling recently: some companies have an accidental retention strategy of building really excellent internal systems that employees rely on to get things done, and can't easily function without them. This can include more operational stuff, like making it easy to get a replacement laptop or ensuring that travel goes smoothly. But it can also mean internal developer tools; Steve Yegge just wrote about some of what Google does in this area. Which companies excel at this?
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Becoming Trader Joe is a funny case study in this. The author talks about all the macroeconomic concerns he had at various times in the company's existence—tech layoffs! Inflation! Kondratieff Cycles!—but it turns out that pretty much regardless of how well or how badly the economy was doing, the right way for him to spend his time was making sure TJ's had a good wine selection at a great price.