Longreads + Open Thread

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Longreads 

• Aaron Zamost has a nice, memorable model of tech companies' PR narratives, noting that as soon as a company looks unbeatable, the narrative cycle will turn, but also that it's a cycle, and today's forgotten company is tomorrow's stunning turnaround. The piece is a decade old, so it's also a good time capsule of which company had which role when: Uber was once hated, at least by the tech press and perhaps drivers (customers and investors disagreed!), but doesn't have the same reputation today (though, once again, investors and customers both seem pretty happy, and have reached an equilibrium where one set complains about prices about as much as the other set complains that Uber isn't fully exploiting its pricing power). Via Matt Mullenweg.
• Bloomberg has an odd article about how a new use case for an existing commodity has raised the price of that commodity, which forces other users to economize for a bit until new supply comes on line. In this case, the commodity in question is electricity, and Bloomberg cites some eye-popping swings in wholesale prices as well as more modest, but significant, changes in what residential customers actually pay. They highlight fifteen states where wholesale prices rose unusually quickly, seven of which are among the ten states with the lowest wholesale electricity prices. So this is partly a very prosaic story about how there's new demand for some commodity, less sensitive to where that commodity is located, which has made said commodity more valuable—the same thing that happened to labor markets when manufacturing globalized. They interview some sympathetic people who are mostly living on fixed incomes and thus have to cut expenses every time the power bill rises, and it is good to be aware of this kind of thing. One of them, for example, is a blind man living on disability, who's quoted saying "They can say this is going to help with AI, but how is that going to help me?" But... there are lots of really good answers to that! How does a technology that excels in converting things from one medium to another—for example, reading a piece of snail mail aloud, or reading a menu at a restaurant—help someone who has limited eyesight? Obviously, the story is about the cost of AI, and they're under no obligation to explain why AI companies would want to buy so much power, or why they'd have the funding and revenue that enables them to do so. But, one can hope that the journalists in question did, in fact, tell this particular down-on-his-luck guy that AI can make his life better, for free, right now. On the other hand, the piece is a lot punchier if he continues to suffer. It's a quandary.
• David Perell interviews Dan Wang on writing and China. It's a good piece on the process of both gathering material and writing about it. As Wang points out, there are lots of places that publicly present themselves in a way that's very different from their everyday reality—the associations you have with major cities you haven't visited probably have very little to do with what you'd see if you went to a major landmark but then walked ten minutes in a random direction. He also defends the sometimes naughty habit of writing an entire essay just to use a particular sentence (guilty!) and notes the awkward feeling when writing nonfiction, that all you've really done is read a bunch of books and recite the facts from them in a different order. Which is less about neurosis and more an opportunity for Wang to propose a helpful solution.
• On the topic of China, Ozy Brennan tells the odd story of Edgar Snow, Mao's American troubadour. Snow wrote Red Star Over China, lauding the Chinese Communist Party in the 1930s, where he had uniquely good access to both the Chinese Communist leadership and general information about the situation. What he didn't have was freedom to accurately report on this. So this is a nice cautionary tale about primary sources: yes, they saw things nobody else saw. But there might have been someone looking over their shoulder.
• Drayton D'Silva on a more comprehensive view of the collapse of Long Term Capital Management. When Genius Failed is a good book, but perhaps too infected with the author's aversion to quants to deliver a good quanty answer to what actually went wrong. Leverage and hubris were definitely factors, but it's really a parable about how if you do something smart, other people will copy you, and if they have to stop copying you for whatever reason, every similarly-smart bet you made can turn bad at once.
• This week in Capital Gains, we consider what shareholder democracy is, and whether or not it really exists, with some comparisons to actual existing democracy. As with any effort to get lots of individuals to collaborate on something, it isn't as pristine in practice as it is in theory.
• The Yet Another Value Blog book club is back, and this time we discuss John Malone's new memoir, Born to be Wired. There are good times, and there are bad times, but there are no times when AT&T is not making poor capital allocation decisions.
• And from ReadHaus, a reader asks for advice on conquering the planet Arrakis. One of the fun things that AI enables is transfer between different media—that's what you're doing every time you use a text prompt to get an image or video, or use voice input to get a text answer. But it also allows transfer within an medium, like applying a mostly-nonfiction newsletter to a sci-fi scenario. The full space of covers, remixes, etc. has not been well-explored.

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Books

The Great Boom, 1950-2000: How a Generation of Americans Created the World's Most Prosperous Society: This book's author, Robert Sobel, was a prolific business historian, who specialized in books about financial institutions (he wrote histories of the NYSE and Amex, and gets a snide cameo in Liar's Poker as the author of a corporate hagiography about Salomon Brothers), and some good books about financial crashes. This book is more broadly-scoped: it's a history of postwar American prosperity.

That's necessarily autobiographical: Sobel was born in 1931, and he died soon after completing the manuscript, so it's really a way for him to look back and understand how his life was, in material terms, so much better than that of anyone else in history.

The strongest section of the book is the first few chapters, which highlight just how counterintuitive American economic growth was after the Second World War. Historically, demobilization led to high unemployment, and much of the US industrial base had been either built or retrofitted to produce tanks, bombers, battleships, ammunition, etc. So, in the late 1940s, there was a massive mismatch between the US's economic inputs and outputs. In the last episode of US economic growth, the 1920s, export markets provided an outlet for some of the US's excess manufacturing capacity, but by this time, the US had a larger share of global manufacturing output and the rest of the world had a similar set of economic problems.

One way to look at the early chapters of the book is that they're the mirror image of China's growth starting in the late 1970s: you can actually afford a lot of malinvestment, excessive borrowing, etc. as long as consumption is low and the economy ahs the wrong capital base. America had a serious housing shortage after the war, for example, which meant that a) almost any dwelling you could build was economically viable, and b) mass-production of those dwellings could drive efficiency gains.

Later chapters are weaker, in part because so much happened that a high-level summary is necessarily going to miss some details. Given Sobel's usual focus, the book spends a lot of time on the stock market, both as an indicator of what was going on in the economy and as a story about financial democratization. But one thing that all of this does help to explain is why finance is such a big industry compared to what it was historically. In 1950, you just didn't need very many smart people allocating capital to ensure that capital was going to the right places. Buying a jeep factory from the government and 20 cents on the dollar and turning it into a factory that built civilian trucks was just too obvious; if you'd spent time building an elaborate DCF to figure out whether your IRR was 30% or 40%, you were wasting valuable time you could use to achieve that IRR! It also wasn't hard to figure out that if big cities's economies were growing fast, farmland just outside of a city was underpriced if it could be converted into suburbs. But as the obvious low-hanging fruit gets picked, you do need more analysis to figure out which investments are worth making—either because little details about the timing of cash flow make the difference between returns just-above and just-below the cost of capital, or because you need some applied futurism to bet that capital invested in some project today will be more profitable in whatever economic configuration we have a few years in the future.

Open Thread

• Drop in any links or comments of interest to Diff readers.
• Every so often, I like to ask: what trends, companies, asset price changes, etc., need the Diff treatment and aren’t getting it?

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.
• 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)
• 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 Associates, VPs, and Principals to lead AI transformations at portfolio companies starting from investment underwriting through AI deployment. If you’re a generalist with deal/client-facing experience in top-tier consulting, product management, PE, IB, etc. and a technical degree (e.g., CS/EE/Engineering/Math) or comparable experience this is for you. (Remote)
• YC-backed founder building the travel-agent for frequent-flyers that actually works is looking for a senior engineer to join as CTO. If you have shipped real, working applications and are passionate about using LLMs to solve for the nuanced, idiosyncratic travel preferences that current search tools can't handle, please reach out. (SF)
• Ex-Bridgewater, Worldcoin founders using LLMs to generate investment signals, systematize fundamental analysis, and power the superintelligence for investing are looking for machine learning and full-stack software engineers (Typescript/React + Python) who want to build highly-scalable infrastructure that enables previously impossible machine learning results. Experience with large scale data pipelines, applied machine learning, etc. preferred. If you’re a sharp generalist with strong technical skills, please reach out.
• 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)

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.