Longreads- Jonathan P. Sine has a loving takedown of Breakneck, reviewed in last week's Longreads. Among other things, he notes that the high-water mark for China being run by engineers was two decades ago, and more recently they've been more likely to major in something more related to managing people than managing physical infrastructure. And, more broadly: China has been trying to shift its economic model away from big construction projects and towards consumption. So, to some extent, this is less a takedown than a difference of opinion on how seriously to take the CCP's rhetoric. Still, an interesting piece, and a good reminder to stress-test sweeping claims.
- A New Yorker profile of novelist R. F. Kuang, which is mostly a piece about work ethic. Kuang is 29, and has published four novels since 2021, ("and, while I was reporting this piece, she finished the first draft of another one"). If nothing else, this is a great case study in structured procrastination; some grad students would literally rather become popular novelists than actually publish a dissertation! But it's also a good piece about how people vary in how much energy they have, and this has a big impact on their output. Before being a novelist/grad student, Kuang was a top debater, one of those tasks where raw skill is a gating factor and determination to out-research or out-rehearse the other side is the ultimate differentiator.
- A fun piece by Paul Sagar arguing that political philosophy, as a field, is mostly bogus because theorists tend to start with abstract models that are obviously wrong. This debate will never end, because there are two moves you can always make: you can say that this theorizing makes blatantly incorrect assumptions about normal human behavior, so everything derived from them is nonsense. But, in the other direction, you can argue that mere cataloging of facts is pointless without some overarching model, and that given imperfect information and the vagaries of human behavior, we ought to be suspicious of any model that's too good at explaining the past, because it's doubtless overfitting. Ironically, this might be a case where one branch of philosophers is behind economists in the philosophy department; economists love to say that all models are wrong, but some models are useful, and to the extent that philosophy seeks to understand human behavior, that's a good model to have.
- A technically accurate interpretation of certain Magic: The Gathering rules allows a player to force another player to disclose arbitrary information. (Or forfeit the game.) Magic is an interesting environment because it's basically an entire game built for the kinds of people whose behavior in other games is characterized as "cheese" or cheating. The whole point is to come up with novel ways that different cards' abilities synergize with one another to produce disproportionate outcomes.
- Tomas Pueyo on how the climate of the US helps to explain the Civil War. There are historians who focus a lot on Great Men of History, or institutions and norms, and there are historians who point out that sometimes the most relevant historical fact is what you can grow or mine in a given location, or the locations of rivers. This piece is great because it's a synthesis: a case where institutions happened to be incompatible with geographic reality in a way that wasn't obvious at the start, but that eventually led to massive conflicts.
- In Capital Gains this week, we look at what happens when a business is close to its peak, and the upside from just slightly increasing that runway.
Open Thread- Drop in any links or comments of interest to Diff readers.
- Magic: The Gathering was made by and for mathy people who like finding surprising edge cases in complicated rules. But what are the specific skills it seems to train? I'd naïvely think that tax lawyers and chip designers would be overrepresented in Magic relative to nerds generally, but is that the case?
Diff JobsCompanies in the Diff network are actively looking for talent. See a sampling of current open roles below: - A transformative company that’s bringing AI-powered, personalized education to a billion+ students is looking for elite, AI-native generalists to build and scale the operational systems that will enable 100 schools next year and a 1000 schools the year after that. If you want to design and deploy AI-first operational systems that eliminate manual effort, compress complexity, and drive scalable execution, please reach out. Experience in product, operational, or commercially-oriented roles in the software industry preferred. (Remote)
- Fast-growing, General Catalyst backed startup building the platform and primitives that power business transformation, starting with an AI-native ERP, is looking for expert generalists to identify critical directives, parachute into the part of the business that needs help and drive results with scalable processes. If you have exceptional judgement across contexts, a taste for high leverage problems and people, and the agency to drive solutions to completion, this is for you. (SF)
- Thiel fellow founder (series A) building full-stack software, hardware, and chemistry to end water scarcity, is looking for an experienced software engineer to help build the core abstractions that enable global cloud seeding operations - from mission planning to post-flight analysis. If you have 5+ years building production systems with complex integration requirements, please reach out. (Los Angeles)
- A leading AI transformation & PE investment firm (think private equity meets Palantir) that’s been focused on investing in and transforming businesses with AI long before ChatGPT (100+ successful portfolio company AI transformations since 2019) is hiring experienced forward deployed AI engineers to design, implement, test, and maintain cutting edge AI products that solve complex problems in a variety of sector areas. If you have 3+ years of experience across the development lifecycle and enjoy working with clients to solve concrete problems please reach out. Experience managing engineering teams is a plus. (Remote)
- Ex-Citadel/D.E. Shaw team building AI-native infrastructure to turn lots of insurance data—structured and unstructured—into decision-grade plumbing that helps casualty risk and insurance liabilities move is looking for a data scientist with classical and generative/agentic ML experience. You will develop, refine, and productionize the company’s core models. (NYC, Boston)
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. If you’re at a company that's looking for talent, we should talk! Diff Jobs works with companies across fintech, hard tech, consumer software, enterprise software, and other areas—any company where finding unusually effective people is a top priority.
|
