Longreads + Open Thread

Longreads

• Ronan Farrow in The New Yorker has a long article on Elon Musk, focusing on how he's indispensable to various government programs but also a high-variance person who sometimes says and even does fairly crazy things. In a sense, we should expect this to be even more common than it is. You can divide tasks into 1) things everyone can do, 2) things nobody can do, and 3) things almost nobody can do but that a handful of people or perhaps exactly one person can get done. Are you surprised that the people driving point 3 are somewhat unusual on many dimensions? They have to be unpredictable, because the predictable thing is for SpaceX not to exist in the first place! It would be more boring to read but more surprising to learn about someone who had launched a similar company and was otherwise extremely boring and predictable. (There are some funny tics in the writing where the author mentions something Elon believes and then, as an aside, tells you “this is Bad”. The old question was "Will it play in Peoria?" but now the question is "Will it play in Park Slope?")
• David Blitz on The Cross-Section of Factor Returns. Systematic investing is one of those areas where you may not care about it, but it cares about you: discretionary investors (i.e. stock pickers) tend to fall into identifiable systematic camps, like momentum, value, carry, and the like. So even if it's a bit intrusive for someone to reverse-engineer your process, it's helpful to know what drives returns and when. An important takeaway from this piece is that many strategies underperform during bull markets and outperform during bear markets. Which means one way to explain their returns is psychology: follow these strategies and you tend to do better than average over decades, but you’ll do worse at times when everyone's talking about stocks and better when nobody cares.
• "A literal banana" writes a devastating takedown of large swathes of behavioral science in Carcinisation. Featuring lots of fun thought experiments in addition to looks at the relevant literature. For example: "Nudge studies aren’t real and don’t replicate. When they’re attempted in the real world, the effects are much smaller than in the academic studies, and the effective so-called nudges tend to share a curious feature: they operate on rationality rather than automaticity. For instance, one of the most effective “nudges” is apparently for the government to send clearly-worded letters explaining what they want people to do. That seems more like common sense and respect than a “nudge” to me, but I’m not a Nudge Professional."
• Peter Seibel: Code is not Literature. This piece starts with the observation that programmers are theoretically supposed to read good code, but generally don't report spending much time doing so. Then extends this to note that the code people do read, in technical books, is generally cleaned up and stripped down to emphasize whatever point is being taught. But then, mercifully, concludes that there is some merit to reading code—not as literature, but as an entirely different academic discipline.
• Gina Kolata in the NYT on the long history of GLP-1 agonists and the mystery of why they work. Diet is always a hard field because the human body is so relentless about retaining homeostasis, which means you're already starting with a mystery: why do some people get so obese in the first place? There are first-order answers, of course, involving how much they consume and how much they burn (and the mechanism of action for these drugs is more or less that they induce perfect compliance with a healthy diet: people on them simply don't have as much desire to eat, so they consume fewer calories, so they lose weight—a roundabout victory for the calories in/calories out model!). This piece is also a good example of practice leapfrogging theory: we'll probably develop better theories of obesity and weight loss in response to these drugs, whereas the natural way to think about it is that things should go the other way around.
• In this week's Capital Gains, we look at how to read the yield curve, and whether or not to worry that yield curve inversions predict recessions. We're in one right now (a yield curve inversion, not a recession). But, as with the weight loss story above, we're looking at one variable in a fiercely complex system, and that variable is both an input and an output into trends in economic growth.

Books

• Project Boing: Very early in my career, I worked with someone who put out a strong "I'm working at this job to gather material for my roman à clef debut novel" vibe, and I decided that there was a risk I'd be an identifiable character in the book so I ought to be extra polite. That particular person did not write a book, but a colleague I briefly interacted with at Yahoo! around 2012 has now written a book about “Boing;)”, a suspiciously similar company with unusual punctuation in the name, also set in 2012. (I do not believe I'm in the book.) The book is more of a satire of big corporations than of tech specifically, which may explain some of how Yahoo has done since then. Some of it's written in a corporate-epistolary style: the narrator's inner monologue includes screenshots of Powerpoint presentations and Excel sheets describe his state of mind. It's hard to assess from the outside, because there are many, many inside references to amusing events in Yahoo's history, which was indeed wildly chaotic but in the end quite fun.

Open Thread

• Drop in any links or comments of interest to Diff readers.
• The Diff is planning a piece on designing products for power users. I have opinions as a user, but it would be nice to hear from someone on the other side: if you've worked on a product that at least some users will be interacting with for more than half of their workday (think Excel, Bloomberg, Gmail, Slack, an IDE) we'd be delighted to talk to you, on or off the record, about the unique considerations for these products.

Diff Jobs

Companies in the Diff network are actively looking for talent. A sampling of current open roles:

• A new fintech startup wants to bring cross-border open banking to LATAM, and is looking for a founding engineer. (NYC)
• A concentrated crossover fund is looking for an intellectually curious data scientist with demonstrated mastery in analytics. Experience with alt data, web scraping, and financial modeling preferred. (SF).
• A company building zero-knowledge proof-based tools to enable novel financial arrangements is looking for a senior engineer with a research bent. Ideal experience includes demonstrations of extraordinary coding and/or math ability. (NYC or San Diego preferred, remote also a possibility.)
• A new health startup that gives customers affordable access to preventative care and lifestyle interventions seeks a founding engineer. 7+ years of JavaScript experience preferred (TypeScript is ideal), and payments experience is a plus. A great opportunity for anyone excited to make healthcare better by treating problems cost-effectively before they're catastrophic. (US, remote; Austin preferred)
• The leading provider of advanced options analytics — “the ASML of options trading” — is growing rapidly, very profitable, and looking for a generalist who can excel in chief of staff and business development functions. A trading, quant, or similarly technical background is a big plus. (Connecticut, NYC)

Even if you don't see an exact match for your skills and interests right now, we're happy to talk early so we can let you know if a good opportunity comes up.

If you’re at a company that's looking for talent, we should talk! Diff Jobs works with companies across fintech, hard tech, consumer software, enterprise software, and other areas—any company where finding unusually effective people is a top priority.