Longreads + Open Thread

Longreads

• Ed Caesar has a lengthy piece in The New Yorker about encrypted phone companies that get hacked by, or are sometimes run by, law enforcement. One reason this happens so often is that selling to criminals requires security-through-obscurity: it's hard to get an SOC 2 audit for a product whose clientele is organized crime. Another reason they're a popular target is that illegal supply chains are highly dependent on trust, since counterparties are hard to sue. Using information from encrypted phones to make drug busts and arrests also makes it harder for criminals to trust each other, and that makes bigger and more lucrative networks harder to operate. (On the other hand, perhaps it hands a bigger advantage to the largest cartels—even in the illegal narcotics trade regulations tend to benefit the largest-scale enterprises!)
• Nate Rogers at The Ringer interviews the pseudonymous Twitter personality @dril, who is responsible for coining numerous online idioms and who has managed to keep the same schtick funny for over a decade without changing styles or getting bored. Dril also either hasn’t sold out or hasn’t been able to figure out how to monetize fame nearly as well as other accounts of similar size. This might constitute post-scarcity conspicuous consumption: if you have a million fans and haven't used that status to make millions of dollars, you're consuming a luxury good that is somewhere between expensive (measured by opportunity cost) and priceless.
• Gergely Orosz of Pragmatic Programmer interviews Steve Yegge about his career writing code and managing coders. One convenient thing about the variance in programmer output is that you don't necessarily have to give up on being an individual contributor to keep getting promoted. One fun story is how Yegge joined Google: one of his jobs at Amazon was to convince prospective employees to choose Amazon over other offers: "So I started to dig in. What was it about Google that these interns were more excited about than what we were doing? And the more I dug into this, the more I realized that I also wanted to work at Google." (Disclosure: I'm long Amazon—other Amazon shareholders will probably be quite happy to read the description of how good AWS is at treating customers well and figuring out what they need.)
• Pasha Kamyshev suggests that neither doom nor "foom" are the right model for AI, because discontinuous improvements in AI are unlikely. This is very much up for debate, but it's a fun alternative to the hyper-optimist or apocalyptic views of artificial intelligence. This piece seems reasonable given the way other technologies have worked historically, where we do encounter problems but we also encounter them at a pace that allows us to adjust.
• Karthik Sankaran writes about why the US dollar's status as a reserve currency is not an unalloyed benefit, but is net good for the US. One important line: "the US is sufficiently heterogenous in terms of resource endowments and the technological sophistication of different industries that there is never going to be a single level of the dollar that is right for all sectors"—which is especially important because that heterogeneity is partly why the dollar is a reserve currency in the first place!
• This week's Capital Gains asks: what is alpha? One fun answer is that it's just a different Greek letter representing the same thing as "epsilon" in a linear regression, i.e. the amount of return that can't be explained purely by the risks being taken. Aggregate alpha must sum to zero before transaction costs and fees: outperformance from one investor means underperformance by another. So the search for alpha is really a search for other people's mistakes—and the question is whether it's easier to systematically get things right or to find areas where other people systematically get things wrong.
• Tobias Huber and I were in Pirate Wires this week writing about the risks of playing it safe, in AI and other domains. Oil, transistors, and artificial nitrogen fixation all had significant military applications early in their existence, but ended up having most of their benefits in civilian applications. If we’d decided that Transistor Safety was a key issue in the 1960s, we’d probably still think of transistors as a tool mostly used for steering rockets and for lightweight combat radios—just like fears of nuclear war had a bigger impact on nuclear power than on the nuclear weapons themselves.

Books

• The Everything Store: Jeff Bezos and the Age of Amazon: Stories about the early days of successful tech companies often include anecdotes about how the company came close to failing multiple times, and how even when it looked like things were fine, the CEO was perennially convinced they were one or two missteps away from dying. There are two good explanations for this: it might be a selection effect, where ambitious companies are incredibly risky and the success stories come from lucky people who correctly recognized that they faced long odds. Or it could be that companies stop growing so fast when they stop worrying so much.
  
  I reread this book partly to see how Amazon handled the post dot-com deceleration, and the answer is that they were quick to recognize what was going on, brutal about cutting costs fast, but generally able to get back to a healthy steady state in not too long. On the other hand, that was a time when online commerce had bigger secular tailwinds than it does today: in 2001, a company that merely avoided any missteps and didn't lose share would have seen its top-line grow 19% just because online spending was growing that fast.

Open Thread

• Drop in any links or comments of interest to Diff readers.
• Are there any important non-AI inflections happening right now? Sometimes there are important changes that get obscured by more newsworthy ones; someone who spent all their time reading about the banking system in 2008-9 might have missed the early stages of the US becoming energy independent.

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