James B. Stewart in the NYT on the AT&T acquisition of Time Warner: "The Worst Merger Ever?" The piece details the extreme culture clash between a media organization and a telecom business, and the narrower personality differences between Time Warner and AT&T's management. (In the opening scene, AT&T's head presents Time Warner executives with a memo detailing how he wants them to communicate with him.1) A good look at why merger synergies don't always add up.
Meghna Rao at Meridian profiles Carolynn Levy, inventor of the SAFE. Early-stage investing has grown in part because it's become extremely standardized, and the SAFE is an important part of that. It's a nice example of productivity growth in a service industry: instead of inventing new legal terms for every startup (which in practice meant either repeating existing work or finding a clever way to structure an unfair contract), a single product has been mass-produced at high quality and low cost.
Matthew Tejo: Why Twitter Didn’t Go Down: From a Real Twitter SRE. It's hard to develop intuitions on roughly what order of magnitude the required headcount is to run a given service. It's much more common to cite a company as overstaffed than to point to examples of low-headcount services that were still able to scale (the two classic case studies are WhatsApp and Instagram). This piece goes into some detail on what happens automatically and what requires human intervention—which is a good start for thinking about where the tradeoff favors the latter, even at Twitter scale.
Quantian on physics hacks that can be applied to quantitative finance. Highly, highly recommended; it's a great case study in taking a fairly abstract idea, in this case the fact that answers need to be in specific units, and applying it to a novel topic. (And don't miss the coda, about using parallel construction to disclose information you already have but aren't supposed to share—also a fun topic!)
Just in case that post leaves you wildly excited to apply metaphors across disciplines, this piece from SubAnima is a great case study in a model that doesn't apply in a different field: as it turns out, cells should not be thought of as machines. This is a great piece on a topic that's fractally complicated.
- The Money Noose: Before FTX, the big story of a broker/prop trading firm collapsing because they dipped into customers' accounts was the collapse of MF Global. This book is less of an "I interviewed all the participants and got the real story, or at least a few plausible stories" book, and more of an "I read the legal documents, SEC filings, and news reports so you don't have to" kind of book. Decent if you want to know what happened to MF Global, but not destined to be on the list of great books about big frauds.
The mechanics of the collapse were deeper than just misappropriating customer funds. MF Global had the motivation to do that in part because there was a trade they could run that would allow them to book profits upfront on trades that would take a year to resolve. (This happens surprisingly often; one thing that did Enron in was booking mark-to-market profits on long-term deals. And in the 1980s, Lehman ran into trouble in the opposite direction, from trades whose losses didn't have to be booked upfront.) As happens a lot with leveraged firms, the trades that bankrupted them all turned out fine in the end. What killed them wasn't being wrong about the direction of asset prices, but being unable to withstand a margin call.
- Drop in any links or comments of interest to Diff readers.
- I'm interested in learning about the mechanics of layoffs. How do companies decide how much to cut, or who goes and who stays? There is clearly a process that starts with someone saying "We should reduce our headcount" and ends with specific people having a fairly unpleasant meeting with HR, but what happens in between? (If you'd prefer to discuss this off the record, please feel free to reach out by email.)
Comma AI is an incredible example of capital discipline. With ~15 engineers and $8.1 million raised capital, it's managed to win first-place in Consumer Reports' rankings of autonomous driving systems. This is partly due to their superior end-to-end learned architecture, yet also due to their superior business model. And these two things are deeply interconnected. The traditional autonomous vehicle is a Rube Goldberg machine of interconnected hardware, software, and ML subsystems; it requires tons of engineers and scientists ($$$) to develop, and cannot generate revenue until everything finally works. By restricting himself to not raising vast amounts of money, George Hotz forced himself to focus on figuring out the "sine qua non" of autonomy: an intelligent agent that (like the human brain) doesn't require perfect maps and perfect vision to drive safely.
George Hotz laid it all out in two blog posts:
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It's an interesting look at class and power. There are wealthy and powerful people, and they can no doubt hire extremely deferential assistants, domestic help, etc. But there is apparently no amount of money or power that lets someone get away with making such demands of anyone they want to. ↩