Longreads + Open Thread

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Longreads

• Omar Shams at Mutable AI (disclosure: I'm an investor) writes about how AI will change organizations. The concept of an "org chart" was invented to make running railroads more efficient, and while it’s a great way to model how a railroad works, it gets worse and worse as it applies to more and more kinds of companies. If org charts, and if "organization" generally, is about handling information flow—making sure the right person has the right information at the right time—then this is another way that AI will impact a large set of companies well outside the usual list of expected beneficiaries.
• From a few years ago, but relevant from time to time: an NYT profile of a lone Norwegian power trader who once lost so much money it wiped out the Nasdaq Clearing reserve. This ends up being several stories at once: one is about how even a zero-sum market can still introduce instability through counterparty risk, another is about how a long streak of successful trades doesn't necessarily imply that a given trader always has an edge (even though it's evidence in that direction).
• David Segal on Uri Geller's weird intermediate status as either a professional con artist or an entertainer whose schtick is that he's a con artist. It also has some good advice on how to differentiate yourself if you have skills that other people share: "I tell them, ‘Wear Armani T-shirts, buy Hermès after-shave, fix your teeth, smile a lot, be nice to people. This is the way to become famous and loved by your audience." It's also a good illustration of a broad phenomenon: it really rubs people the wrong way if someone in their profession takes the job either much more seriously than they do or much less seriously than they do, especially if that person ends up being successful.
• Katherine Dee writes in The New Atlantis about Carmen Hermosillo, who wrote incisive critiques of Internet culture, starting in 1994. The essays make some solid points; there's an early iteration of "audience capture" in the piece on the commodification of personal experience. But it raises some important questions: is commodification a matter of bandwidth? A matter of distribution? Baring your soul in-person, one-on-one, seems like the opposite of commodified behavior, but doing the same thing performatively for a large online audience of near-strangers is an unnatural behavior that also leads to plenty of ad views and subscription dollars. In one sense, reading first-person accounts about being Entirely Too Online is not that helpful to the average person, since they're not that online. But we're all getting more online all the time, unless we consciously resist it.
• Eugene Wei on what went wrong at Twitter. Algorithms can powerfully shape perceptions (the classic chart of causes of death compared to media coverage thereof is a good example—does the average person worry more about terrorism or kidney disease?). Part of the model is that TikTok's more purely algorithmic feed is better at surfacing interesting content than a follow graph; it's a big world out there and the person with the most interesting thing for you to read or watch is probably not one network hop away. But "capturing that passive disapproval is something Twitter has never done well." When the default interaction is to scroll a lot, like a little, and tweet or retweet more occasionally than that, it's hard to interpret scrolling as passive enjoyable consumption or as hastily scrolling past something annoying. And because of the popularity of snark, online in general and on Twitter in particular, even sentiment analysis of replies and retweets will struggle. So the follower network is important, and a more algorithmic feed creates a completely different experience.
• In this week’s Capital Gains, we look at whether investors are irrationally under-levered or irrationally over-levered.There’s a case for both, in different scenarios.

Books

• Market Mover: Lessons from a Decade of Change at Nasdaq: The funny thing about stock exchanges is that they 1) think of themselves as the beating heart of ruthless capitalism, designed entirely around the needs of capital allocators who invest in, but do not run, companies, and 2) they are often founded as, basically, workers' cooperatives with democratic governance. This book tells the story of the Nasdaq's gradual shift from being a collective (National Association of Securities Dealers Automated Quotation System) to being a standalone company. It's also a good story about how network effects don't always imply durable economics: they continuously acquired new businesses both to maintain market share and to diversify into adjacent businesses with better economics than a pure exchange.

Open Thread

• Drop in any links or comments of interest to Diff readers.
• I’m interested in what you might call the “corporate brainstem”—how organizations handle recurring, potentially low-priority tasks that don’t have some external catalyst for their completion (until it’s too late). This can be anything from periodically making sure the cloud bill doesn’t include a lot of wasted spending to occasionally reevaluating their entire value proposition to keeping in touch with potential customers who weren’t quite a good fit but could be in the future. There are individuals who are amazing at this, but there’s lots of variance, and a company that relies on their efforts runs some risks. So, what are some approaches, from cron jobs to recurring meetings to signifiers of corporate culture, that keep these tasks from slipping?
  

Reader Feedback

Last week’s piece on the metaverse noted that mobile was unusual as a computer model shift in that it had relatively few casualties among previously leading companies. Levi Ramsey points out that Sun arguably blew a head start in mobile (perhaps while distracted by also losing revenue and relevance during the cloud transition).

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Diff Jobs

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

• A well funded seed stage startup founded by former SpaceX engineers is building software tools for hardware engineering. They're looking for a full stack engineer interested in developing highly scalable mission-critical tools for satellites, rockets, and other complex machines. (Los Angeles)
• A firm using machine learning to customize investments is looking for a data engineer. (NYC)
• A VC backed company reimagining retirement wealth and building a 401k alternative is looking for a GTM and growth lead with experience in banking and in fintech. (NYC)
• A company building ML-powered tools to accelerate developer productivity is looking for a mathematician. (Washington DC)
• A proprietary trading firm is seeking systematic-oriented traders with ML experience—ideally someone who has displayed excellence in DS and ML, like a Kaggle Master. (Montreal)

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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