Longreads + Open Thread

Longreads

• In Bloomberg Businessweek, Simona Weinglass, Michael Riley, and Jason Leopold have a fun story about a government informant who was apparently running financial scams for years while cooperating with the FBI. There are at least two narratives available here, and as one former prosecutor quoted in the article puts it, "Which is worse? To think that our law enforcement is incredibly cynical and willing to let this guy commit these crimes while he’s cooperating? Or that they were negligent or naïve and didn’t know any of this was going on?" There's a calculation you can theoretically make here, where tolerating some level of criminal activity from informants is a worthwhile trade for stopping other crimes. But it’s tough to evaluate because their whole specialty is taking advantage of people by deceiving them, and they're perfectly willing to do so with law enforcement. (As with other cybercrime stories, there's low trust all the way down: part of the drama in this piece comes from repeated fallings-out among the criminals.)
• Dwarkesh Patel interviews Tony Blair on politics, policy, and learning from Lee Kuan Yew. It's a good get (for Blair). Blair opens with the point that heads of government are usually selected without having any specifically relevant background, and thus have to learn on the job while holding an incredibly important, high-stress job. They're also stuck with a lot of cruft from their predecessors, in what Blair aptly describes as a "conspiracy for inertia." A running thread throughout the interview is the gap between what politicians aim to accomplish and what they can practically do; you can run for office saying that your country will be carbon-neutral by 2050, but in office you have to decide whether that means solar, fusion power, fission power, a lower standard of living, or some combination thereof. Notably, when Blair talks about what he learned from Lee Kuan Yew, he's talking about a politician who could and did talk about specific policies, in part because he'd created a system where politics played a smaller role.
• Kevin Nguyen on how the popularity of Game of Thrones recaps warped the media business. There's an unfortunate dynamic in media where being "first" can mean being the first person to identify a story as worth paying attention to, but can also mean being the first to hit "publish" on a story that everyone's writing. The second type of first is toxic, both because it sets a ceiling on quality and because there's no limit to the number of competitors. That also means optimizing for something other than maximally informing readers: one GoT recapper described her job as helping readers sound more sophisticated when they discussed the previous night's episode with coworkers. That's a decent heuristic if you're utterly at a loss for things to write about, but it's also a good way to over-optimize for the wrong thing. And the people interviewed for this piece all have the sense that they worked hard to analyze a media product whose enduring cultural impact was the observation that despite being very popular, it didn't have much of an impact after the finale.
• Cedric at Common Cog writes about Alfred Winslow Jones, who created the first modern hedge fund. (There were other investment partnerships with a similar structure, like the one Ben Graham ran, but Jones created the long/short, volatility-sensitive approach from which modern funds are descended.) This piece emphasizes that Jones was implementing ideas that were sometimes decades ahead of financial theory. Getting credit for publishing first is a strong incentive, but getting paid for coming up with the same idea and not publishing it can be a stronger one.
• Eric Gilliam at FreakTakes writes about how the research model used by BBN and CMU's autonomous vehicles research group can be applied to semiconductor research. Part of the model is having teams that are small enough that everyone involved understands the full scope of the problem, even if they know their individual bit of it better than anyone else. Which means that the research has a fractal similarity to science itself: it's partly a matter of dividing up the work into coherent chunks, and identifying meta concepts that apply across domains.
• This week in Capital Gains, we took a quick tour through the history of money, covering why a gold standard once made sense, why it stopped working very well, and why the practice of using money tends to be ahead of the theory of what money really is.
• In The Riff, we covered a fun story of a clever Tinder investment, index rebalancing as postmodern finance, internal incentives in AI labs, and more. Listen on Twitter/Spotify/Apple/YouTube.

Books

The Greek Way: Edith Hamilton wrote this book in 1930 to explain the influence of the Greeks on modern civilization to her contemporaries. With the passage of time, it's a good look at how people thought in 1930, framed in reference to our common cultural inheritance of the Greeks. Some of the references require Googling, but some have held up surprisingly well—Aristophanes really did sound a bit like Gilbert and Sullivan, just with more confidence that he had unlimited permission to poke fun at his contemporaries as long as the jokes landed. (It's also a reminder that some jokes will never go out of style: she, too, plays the trick of vaguely describing an international conflict in a way that makes it sound like a reference to British/German relations before revealing that—surprise!—she's really retelling the backstory to the Peloponnesian War. This trick still works.)

Open Thread

• Drop in any links or comments of interest to Diff readers.
• The FreakTakes piece above describes one model for managing research teams, and its general outline shows up in many other places. Are there good examples of research labs that didn't use this kind of free-range approach and still got good results?

Diff Jobs

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• A company building developer tools is looking for a very technical technical writer—someone who has experience developing software and wants to write instead, while still getting paid competitively. (Washington DC area)
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• A diversified prop trading firm with a uniquely collaborative team structure is looking for experienced traders and PMs. (Singapore or Austin, TX preferred)
• 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 cloud infrastructure engineer to join their team of world-class researchers. (NYC)
• A data consultancy is looking for data scientists with prior experience at hedge funds, research firms, or banks. Alt data experience is preferred. (NYC)

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