Byrne Hobart
Today's issue of The Diff is brought to you by our sponsor, 2 Hour Learning.
Longreads
- An occasional thought experiment you might run is: how well could you trade if you had perfect foresight, over short periods? There's been at least one case where someone managed to hack into a newswire and trade ahead of earnings announcements, but presumably there are different skills involved in that compared to fundamental analysis. Now, there's a new way to test this: Elm Wealth has a fun game where you can look at WSJ headlines on various days and choose how much you'd put into equities and treasury bonds if you'd seen those headlines a day in advance. Full disclosure: I got a 60% hit rate, 0.32 sharpe, 36% gain. And that's with "cheating" by generally being able to figure out roughly when the front page was from and knowing about how each asset class did in that period. Which you'd think would be a big advantage, but if you know that the market went up 20% some year, that's a daily edge of about 7 basis points. Maybe enough to be better than 50/50, but not by much. And even more annoyingly, the periods when the market has extreme swings are periods of high day-to-day volatility, so, knowing your Kelly, it's probably optimal to bet less on days when you're more confident that the market was collapsing; US stocks were down 16% in October 2008, which also had two of the ten biggest percentage gains in S&P 500 history.
- Alex Heath interviews Mark Zuckerberg for The Verge (disclosure: long META). A lot of this is an elaboration on the original reason we call it "Meta" and not "Facebook": the company was founded early in one platform transition, and wasn't big enough to affect how that transition went, so it bet a lot on what they think is the next one. One thing I hadn't considered before is that augmented reality glasses solve the AI integration problem—companies that control the operating system (e.g. Apple) or a substantial share of the digital properties where users keep their data (e.g. Google) can use AI to integrate across them—maybe your AI agent and your friends' chat for a bit to turn your vague plans to get lunch some time into a specific date, time, and location. For some use cases, smart glasses have this advantage, too, because they see the same things the user sees, whether they're in the physical world or on a screen. Also enjoyable: Zuck's narration of how he had to refresh his mental model of what the news feed was for in order to properly compete with TikTok.
- In Global China Pulse, anthropology PhD student Yanyu Chen gets an inside look at the business of money-laundering intermediaries. Clean money is perfectly fungible, but dirty money has the character of physical commodities—it exists somewhere, and can be moved only with certain infrastructure and only to particular destinations. So this business is like a weird version of physical commodities trading. The end purpose is, of course, to convert stolen funds into spendable funds, but the operational complexity of this demonstrates how well most fraud detection and anti money-laundering efforts work—it happens, but it's a serious effort.
- Ben Southwood, Samuel Hughes, and Sam Bowman on what's wrong with the UK's economy. Economic growth is subject to technological and material constraints, but lack of growth over long periods is a choice. The theme of this piece is that the UK has an opportunity to achieve incredible outcomes by doing the basic things right—building houses (France has 1.2x as many per capita), getting cheaper energy (the US consumes almost 3x as much per capita), getting to parity with other countries on infrastructure (Spain gets twenty times as much railroad mileage per dollar). Technology-driven productivity growth matters a lot in the long run, but sometimes the limiting factor behind it is that people can't take promising-but-low-paid jobs at early-stage tech startups because nobody's allowed to build the housing they need to work there.
- A deep cut: Anthony Lane on Lego as the perfect toy in The New Yorker, in 1998. One of the nice things The New Yorker sometimes does is to pay an observant and funny writer to spend as much time as it takes to examine some cultural phenomenon from every possible angle. So we get a look at Lego as it's sold in toy stores, a visit to Legoland, Lego in popular fiction, and Norman Mailer's Norman castle-inspired Lego sculpture.
- In this week's Capital Gains, we look at why two-year treasury bonds are special, with additional thoughts on why central banks operate in the increments they do.
- In The Riff this week, we cover cult stocks, ESG, tick sizes, and lists. Listen with Twitter/Spotify/Apple/YouTube.
- And as a bonus podcast, I joined Patrick McKenzie/Patio11 on Complex Systems to talk about fake money, hyper-consumers, food delivery, writing, how kids use ChatGPT, and more.
Open Thread
- Drop in any links or comments of interest to Diff subscribers.
- What interesting old-economy-adjacent startups are out there? One useful thing to keep an eye on is industries with a high average age—when there's a wave of retirements, entering the industry means having opportunities for rapid promotion even if the industry isn't growing. And that applies to companies as well as careers.
- Also—if you played the Elm Wealth macro-with-perfect-foresight game, post your score (especially if you figured out a heuristic that has a good hit rate).
A Word From Our Sponsors
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Companies in the Diff network are actively looking for talent. See a sampling of current open roles below:
- An AI startup building tools to help automate compliance for companies in highly regulated industries is looking for an attorney who can work with customers to help them use the product. JD from a top school and experience at a leading law firm (or an in-house role in financial services) preferred. (NYC)
- A new data startup that's seeing early traction bringing transparency to a $1.5tr asset class is looking for an experienced founding engineer. This is a full-stack role; Python, React experience a plus. (Hybrid, Miami/West Palm Beach, FL).
- A hyper-growth startup that’s ingesting customers’ sales and marketing data and turning it into revenue is looking for a product-oriented engineer with a track record of building, shipping, and owning customer delivery at high velocity. (NYC)
- A startup building a new financial market within a multi-trillion dollar asset class is looking for a data scientist with actuarial experience. (if you’ve been an actuary but are newer to the data side, that’s great too.) (NYC)
- A well funded startup founded by two SpaceX engineers that’s building the software stack for hardware companies is looking for a staff product manager with 5+ years experience building and managing data-intensive products. (LA, Hybrid)
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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