Longreads + Open Thread

Longreads

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• A wonderful 1952 piece reminiscing about New York before the car. One invisible form of progress is the reduction in ambient misery: "The horses would fall in great numbers. Then they lay quiet, open-eyed, puzzling, until pulled up by the bit and lashed at by the barking drivers. The horses would then scramble up, steel hoofs dashing sparks. If, falling again the horse broke a leg, a policeman would draw his gun."
• Nima Badizadegan in Speculative Branches writes about Knight Capital's company-killing software bug. It's a good parable about complex systems in general: the specific bug was a very strange interaction across new systems, legacy systems, and insufficient logging and issue flagging for deployment. The fundamental issue was complicated enough that it's likely that no one person understood all of the systems well enough to understand what had happened. Which is a good reminder that past some unknown level of complexity, what keeps a system running is the meta-system: standards, double-checking, and procedures for what to do when those inevitably miss something.
• John Collison interviews Charlie Munger: the rare interview that's equally fun when the guest and host agree and when they don't. Munger was a highly opinionated person who was unafraid to call out misbehavior—and he also seemed to have a case study for everything. One such case meditates on how Costco's operating efficiency differs from that of other companies whose returns are partly the product of their ability to sell inventory before it’s paid for it. Some companies push customers to pay earlier and simultaneously bully suppliers into accepting their money later. But for Costco, it's more organic: their retail sales are 14.3x inventory, or, put another way, on average they've sold all of their inventory within 25 days of arrival. If they pay their suppliers with typical 30-day terms, their inventory is actually a source of cash rather than something that demands it.
• Simone Brunozzi on how he got hired at Amazon in 2008 (disclosure: I'm long AMZN). In some hiring processes, the goal is to show extreme nonchalance, so your prospective employer feels like they'd be lucky to have you. But an alternative is to treat getting a specific job as a more-than-full-time job itself: "on the following weekend I spent almost thirty hours in building an improved three-dimensional simulation for AWS, with a fully functional messaging system. At 2 a.m. on Monday, exhausted, I sent him the results of my hard work." This works nicely as a practical demonstration of skills, and of course there's a psychological element as well; once reciprocity kicks in, it switches the default choice from rejecting to hiring.
• Shreeda Segan profiled me in Meridian. It was a fun piece—The Diff is less the result of a complex master plan and more a series of sometimes ad hoc responses to various accidents and contingencies.
• This week's episode of The Riff features a discussion of hedge funds, crowded trades, and incentives. (If that sounds familiar: I accidentally used that description to highlight last week's episode, which was, in fact, about completely different topics—demographics, dating, and globalization. My apologies to anyone who listened to it extra carefully to detect a Straussian connection between declining birthrates and the constructing optimal factor-neutral portfolios.) Listen with Spotify/Apple/YouTube.
• And in Capital Gains, Darius Mortazavi writes about why you should read companies' proxy statements, and what you'll learn from them. If you're an investor, management technically works for you, and it's a bad practice to hire someone without at least glancing at their employment contract to figure out how much they're being paid and what, specifically, they're incentivized to do.

Books

• Earlier this year, Stephen Wolfram published What Is ChatGPT Doing ... and Why Does It Work? Even if you're not going to implement, or even use, AI systems, it's very helpful to get an intuition about how they work and what their limitations are. Large language models are astonishingly effective considering what they do and how they work, and it's a pleasant surprise that given enough text and enough computing power, we can create systems that, when coached appropriately, produce entirely original content that's comparable to human output. The book throws in lots of references to Wolfram|Alpha, Wolfram Language, etc. (yes, it has a healthy dose of Wolfram|Ego). But Wolfram's own tools turn out to be a great way to visualize how these models work. Even if the book itself isn’t new information to every reader, the presentation is worth emulating.

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Open Thread

• Drop in any links or comments of interest to Diff readers.
• One unfortunate feature of fast-moving technologies is that it's hard to know when to do a long-form writeup with more background and context. Aside from Wolfram's book, are there any other similarly comprehensive writeups?

Diff Jobs

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

• A well-funded startup is building a platform to identify compliance risks associated with both human- and AI-generated outputs. They are looking for a frontend engineer with React/Typescript experience to join their team of world-class researchers. (NYC)
• A well funded seed stage startup founded by former SpaceX engineers is building software tools for hardware engineering. They're looking for a full stack engineer interested in developing highly scalable mission-critical tools for satellites, rockets, and other complex machines. (Los Angeles)
• A private credit fund denominated in Bitcoin needs a credit analyst that can negotiate derivatives pricing. Experience with low-risk crypto lending preferred (i.e. to large miners, prop-trading firms in safe jurisdictions). (Remote)
• A data consultancy is looking for a senior data scientist with prior experience in marketing data science and e-commerce. (NYC)
• A startup building a new financial market within a multi-trillion dollar asset class is looking for generalists with banking and legal experience. (US, Remote)

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.

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