Longreads

• Ross Douthat interviews Palantir CTO Shyam Sankar. This is an important piece, because the discourse around Palantir is so messy. On some issues, they mess up the compromise through presumed incompetence ($, Diff), where laws are kept on the books on the assumption that they won’t be actively enforced but the side that supports them will see their existence as a moral victory. It ends up making some companies implicitly political because they’re the means by which some political agendas get carried out. And that gives them some pretty intense responsibilities. In theory, the question of whether or not big companies should be able to veto the decisions of elected officials is one that everyone has an easy answer for, but in practice, it really depends on who won the election, what they want to do, and whose cooperation it takes to make that possible.
• Jordan Nel on how the volatility of volatility is increasing, and this raises the value of tail bets, like venture. This is a fun argument (and one I’ve made!): if you want to bet on generic chaos and uncertainty, you’ll pay through the nose for put options, and to the extent that you come out ahead, it’s because those put options position you to buy during a crisis—i.e. you go long uncertainty knowing you’ll usually lose money, but you’ll make back those losses by betting on the status quo! But that’s less true if you target individual risks, and back a company that’s viable when those risks are theoretical but indispensable when they materialize.
• Larissa MacFarquhar in the New Yorker on aphantasia, the inability to visualize, and hyperphantasia, mental visualization skills that allow people to visualize things as real as what they’re actually seeing (“One hyperphantastic told a researcher that he had more than once walked into a wall because he had pictured a doorway.”). Pieces like this are always fun because, inevitably, someone who reads this will learn for the first time that either a) people can actually summon images in their mind and look at them, or b) that there are people who can’t do this. Inner monologues are similar—for some people, the experience of thinking is just a stream of words, and other people don’t have that at all. But what’s especially interesting about all of this is that these different inner experiences have so little impact on capabilities: people without an inner monologue can still write and talk, and people who can’t close their eyes and imagine a flower can still draw one, or describe what it looks like.
• John Collison and Daniel Gross interview Dan Sundheim of D1 Capital on Cheeky Pint. Investing is always a game of relative advantage: it’s not the absolute level of speed, information quality, or analytical ability that matters, but the relative sort. And that applies not just to competition between investors, but to companies, too: one of his key theses (probably true!) is that gas turbine manufacturers underestimate how long the current cycle has to run, and are producing less than the optimal amount. For now, that means that they’re minting money and have revenue visibility far into the future. But it only works if the modern economy is complex enough that someone who’s spent their entire career in one industry is missing perspective that someone with a broader outlook can get. (In a way, these capital allocators play the same role with companies that therapists play with their clients—helping them understand what really drives their behavior, and how it aligns with what they actually want to accomplish.)
• Courtney Shea in Maclean’s covers the frenetic spending habits of early-career Canadians. (For US readers, imagine that real estate prices are similar but your earnings are about a third lower.) As crazy as it sounds, some of this is just the result of consumer lenders running the numbers and realizing that young people tend to get higher raises than older ones, and are at an age where they can develop brand loyalty, so paying a BNPL service’s fees to ensure that consumers get loyal to them still means coming out ahead. There will be plenty of writedowns, of course, and probably some retrenching by lenders. But if there’s one group of people it makes sense to make currently-irresponsible loans to, it’s the ones who are aging into more responsibility and more capacity to service debt.
• In this week’s Capital Gains, we look at the economics of information. It wants to be free, because it’s so cheap to transmit, but it wants to be really expensive, because by definition its value is heterogeneous both on an absolute basis and in reference to particular buyers. That dynamic leads to all kinds of peculiarities in both pure-play data businesses like Bloomberg and in media generally.
• One of this weeks’ interesting questions for the Diff-bot was about the market for quant talent. One feature of that market is that it’s more efficient when what’s being bought and sold is talent, rather than experience; quant firms go to great lengths to ensure that they get in front of as many of the country’s smartest math people as possible. Where the market is less efficient, by nature and by design, is with the more experienced cohort. They’re used to working with a specific firm’s infrastructure, and focus on signals that are additive given that firm’s existing risk profile. And funds and banks recognize that the alpha they get from investment professionals is a function of the gap between gross alpha added and compensation paid, and try to structure their employment contracts so that quitting to go somewhere else is never quite optimal.

Books

Blood Meridian: Or the Evening Redness in the West: The “frontier thesis” of American history focuses on how Westward expansion had mostly positive cultural effects on the people who did it. But another effect of the frontier is that it gives your society somewhere to put the most dangerous and dysfunctional ones.

Blood Meridian is a novel, not a history book, but it draws heavily from a nonfiction work, Samuel Chamberlain’s My Confession, detailing Chamberlain’s late-1840s/early-1850s membership in the Glanton Gang, a group of scalpers. They were hired by local governments to scalp Apaches, and were paid per scalp. But the most successful gangs tended to scalp whoever they could get away with, terrorizing random villages and travelers until the government caught up with them. (What a relief it must have been for residents of East Coast cities to know that the invisible hand had carefully picked up everyone who would be willing to scalp an innocent person for money and distributed them near the Texas/Mexico border.)

The way I read the book was that it’s a meditation on the fact that civilization tends to tamp down people’s naturally violent nature, but it consists entirely of those same flawed, violence-prone people. The agents of the state who enforce norms of nonviolence are presumably going to include more than their fair share of people who actually enjoy violence, if only because their job provides a socially-sanctioned outlet for it. A gang of scalpers is taking that to an extreme, but it’s a natural feature of any civilization. And yet, it actually works—the bad characters in the story tend to come to unfortunate ends, with the exception of the implied-to-be-supernatural Judge Holden. (Holden is portrayed as never changing over time, capable of great depravity, but also someone who everyone in the Glanton gang encountered somewhere else before they joined up with him. Fictional portrayals of satan tend towards camp, but this one works.)

Cormac McCarthy tends to write bleak novels whose payoff is more bleakness, but they’re also very frank. That kind of behavior is something people are capable of, and we don’t need all that much encouragement. And that ends up being pretty optimistic, because it’s hard to dispute what McCarthy says about the worst of human behavior, but also hard to dispute that most of the time, we manage to contain that behavior and largely get along.

Open Thread

• Drop in any links or comments of interest to Diff readers.
• When there’s a long cycle in an industry where few participants are willing to add capacity, how do you actually analyze things? The longer the backlog is, the more of their present value consists of cash flows that are already basically locked in. Do you just bet on the distant future where they’ll start looking more like a stock than a bond, or is there something else to do?

Diff Jobs

Companies in the Diff network are actively looking for talent. See a sampling of current open roles below:

• A startup is automating the highest tier of scientific evidence and building the HuggingFace for humans + machines to read/write scientific research to. They’re hiring engineers and academics to help index the world’s scientific corpus, design interfaces at the right level of abstraction for users to verify results, and launch new initiatives to grow into academia and the pharma industry. A background in systematic reviews or medicine/biology is a plus, along with a strong interest in LLMs, EU4, Factorio, and the humanities. (Toronto, Remote)
• Ex-Optiver/DRW quants with over a decade of experience in HFT and AI are reimagining time series forecasting from first principles. They are building a research lab, initially monetized via derivatives trading. The team is hiring a founding engineer (Python/C++/Rust; distributed compute, ML infra) and a founding AI researcher to rethink how machines model the future. No finance experience needed. (SF)
• 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)
• 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)

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.