Longreads + Open Thread

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Longreads

• John Hermann on the world of "Generative Engine Optimization," or attempts to get linked by generative AI. This is, partly, an effort by traditional online marketers to glom onto a new trend—they were talking about social media optimization well before there was much economic activity on social media, too. GEO actually seems fairly prosocial, if done well: the smarter models get, the harder they will be to trick, so the main optimization is to say true and useful things better than anyone else in history. The rise of search, and of SEO, was good news for people who could provide passably good answers to lots of popular queries, and some businesses, like Yelp and TripAdvisor, were built on it. GEO redistributes some traffic away from large-scale content-farming operations and towards lots of strange obsessives who've thought more deeply about topics than anyone else.
• For an example of which writers and what kinds of writing benefit from generative AI, consider Nate Silver on how early to arrive at the airport. My first big disappointment with a reasoning model involved me replying to its answer with "Really? Two hours early for a domestic flight that departs at 6am?" This is exactly the kind of thinking it's helpful to outsource, because you could figure it out yourself, but you wouldn't learn that much useful information beyond the scope of the problem. In the future, training data will include some much-too-rigorous analysis, too. And that's the kind of writing that was probably under-rewarded relative to its social benefit in the past, and that will get Nate Silver a few additional subscriptions by way of LLM chatbot referrals in the future.
• Mojo on Substack writes in praise of risk arbitrage as a training ground for investors, a point with which The Diff strongly agrees ($). One way this piece makes the argument is that there's a first-order game—calculate the deal price, estimate time to close, use this to back into an annualized return—and then a metagame of understanding what can go wrong (financing problems, regulatory intervention) and what can occasionally go really right (bidding wars). And then there are further games to play from there, like understanding how all of these distributions will evolve over time, understanding how different market participants will change their behavior under different circumstances, etc.
• Did Craigslist kill newspapers, or did their model mostly die because classifieds in general make more sense online? This piece tries to tell a story that it wasn't, but there isn't that much evidence to go on. Craigslist prices itself differently from newspapers, and has a different network effect. It did hurt monetization for one of the most lucrative parts of the newspaper bundle, but now it's in relative decline—because there's a new bundle where classifieds are better as a user retention too for targeted ads ($, Diff). The story of the media business is one of constant shifts in what kind of attention-gathering is complementary to particularly lucrative ways to turn that attention into money.
• Matt Reynolds in Wired looks at the impact of cloning on polo. A fun and ludicrous read. One feature of many sports is that as they get more popular, and as training gets more rigorous, excellence just comes down to genetics because that's the main way elite players differ. (And this can happen at multiple layers: if you have a genetic predisposition to be a little slower or weaker than someone else, but also a predisposition to stay motivated to train for longer, you'll win—at least until someone somehow finds a way to commoditize and optimize that willingness to train.) It's also a story about intellectual property: if that's someone's edge, their incentive is to keep that IP under their control for as long as possible.
• In this week's Capital Gains, we look at treating platform companies as virtual real estate.

Books

Anathem: Neal Stephenson has a habit of playing a specific prank on his readers, which goes like this:

1. The beginning of his book will describe some unbelievably cool possible world—a cyberpunk samurai pizza delivery guy who works for the mafia! A post-scarcity world where all the confusing social rules are explicit! A secret cabal of time-travelers! And then
2. He'll spend most of the narrative explaining why this hypothetical world has internal contradictions that make it, in the end, as much a fantasy to the fictional characters as it is to the reader.

In Anathem, the ridiculously awesome concept is a fictional world where there's some sort of combination of a monastery and endless grad school where everyone's straightedge and almost all modern technology is forbidden. These monks are completely isolated from the outside world for fixed periods—a year, decade, century, or millennium—during which they keep busy by doing the kinds of manual labor you need to do if you're morally opposed to motorized equipment, as well as by contemplating math, physics, history, choral music (no instruments, either!), philosophy, etc. In the outside world, empires and religions rise and fall, technology advances and falls back, and, at the time the book is set, staring at their handheld and constantly-buzzing electronic communications devices.

It's a good book to revisit any time you worry that you aren't feeling guilty enough about frying your attention span online, but it's an especially good one to read right now. A book that starts out by asking what would result from long periods of uninterrupted thought has useful things to say in an era when there have recently been many exciting developments in the field of Not Having To Think About Things Too Hard, i.e. LLMs.

A lot like having kids, monasticism is cheaper in an absolute sense than it's ever been, but even more expensive in terms of opportunity cost. You could FIRE your way to being able to afford a cheap house somewhere, with lots of books. If you want to be really monastic, you could probably find a house that's cheap in part because it's so far from modern conveniences like hospitals. And yet, we mostly don't do that kind of thing, and, as it turns out, it's hard to take half-measures. You can try keeping your phone in do-not-disturb mode when you're not on the clock, operating a news-insensitive lifestyle (target-date funds instead of specific assets, opting into a social circle that avoids current events), but it's incredibly easy for the modern world to seep in. One minute, you're grabbing your phone to Google an unfamiliar term, the next minute you discover that an hour went by; or you and your book club are doing your absolute best to talk about Plutarch, but somebody makes an apt comparison to Trump, and suddenly you're back in 2025. Having an insanely strict set of rules that everybody tries to follow consistently seems like the only durable way around this, and to do that you need some kind of context in which the rules make sense. (It's really hard to start intentional communities purely because you have the right intentions, because everyone recognizes that the rules are somewhat arbitrary. If you want your community to be separate for long periods, and to retain its norms, you need either the fall of the Roman Empire or intense religious persecution to get enough group solidarity going. Anathem, incidentally, does provide a reasonable backstory for how something like the setup in the novel could come about.)

The book is ultimately a love letter to civilization. Not a particular civilization, but to the parts of it that might have been discovered at the same time by a German monk and a Chinese scholar, who later historians will realize were both rehashing something that had been puzzled together by a Persian astronomer half a millennium earlier. And it's also an effort to treat neoplatonism as a meaningful way to understand reality and aesthetics—if something really resonates, across places and times, maybe it really is a meaningful sort of something, which was discovered rather than created! The book itself sort of reflects this; the beginning is both very slow and deliberately confusing, but that's just life in the monastery for you!

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Open Thread

• Drop in any links or comments of interest to Diff readers.
• One of the few things that doesn't feel late-cycle right now is that there are still some great companies that haven't gone public but clearly could. What's holding them back?

Diff Jobs

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

• Well funded, Ex-Stripe founders are building the agentic back-office automation platform that turns business processes into self-directed, self-improving workflows which know when to ask humans for input. They are initially focused on making ERP workflows (invoice management, accounting, financial close, etc.) in the enterprise more accurate/complete and are looking for FDEs and Platform Engineers. If you enjoy working with the C-suite at some of the largest enterprises to drive operational efficiency with AI and have 3+ YOE as a SWE, this is for you. (Remote)
• Thiel fellow founder (series A) building full-stack software, hardware, and chemistry to end water scarcity, is looking for an ambitious robotics engineer to help build sophisticated drone nest systems that can deploy, maintain, and coordinate fleets of UAVs for precision atmospheric intervention. If you spend nights and weekends in the shop tinkering on hardware projects, have built and deployed robotic automation systems, and understand ROS (Robot Operating System) well, please reach out. 
• A Google Ventures-backed startup founded by SpaceX engineers that’s building data infrastructure and tooling for hardware companies is looking for a product manager with 3+ years experience building product at high-growth enterprise SaaS businesses. Technical background preferred. (LA, Hybri
• 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)
• An OpenAI backed startup that’s applying advanced reasoning techniques to reinvent investment analysis from first principles and build the IDE for financial research is looking for software engineers and a fundamental analyst. Experience at a Tiger Cub a plus. (NYC)

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.