Christoph Schweizer (newsletter): “The three most important words in AI over the next year will be focus, focus, and focus. Leading companies invest in a few high-value initiatives—prioritizing just 3.5 use cases on average, compared with the 6.1 pursued by their peers. They invest more than 80% of their AI budgets in reshaping core functions and inventing new offerings rather than achieving important but incremental productivity gains. Critically they embrace the 10-20-70 principle: dedicating 10% of their effort to algorithms, 20% to data and technology, and 70% to people, processes, incentives, leadership, and culture. In other words, they address the way their organizations actually work, which often proves harder than the technological advancements themselves.”
Alex Tabarrok: “The real lesson from markets is not to create monarchs but to design systems that create choice and competition and allow citizens to remove leaders when they fail.”
Reid Hoffman: “Imagine A.I. models that are trained on comprehensive collections of your own digital activities and behaviors. This kind of A.I. could possess total recall of your Venmo transactions and Instagram likes and Google Calendar appointments. The more you choose to share, the more this A.I. would be able to identify patterns in your life and surface insights that you may find useful. Decades from now, as you try to remember exactly what sequence of events and life circumstances made you finally decide to go all-in on Bitcoin, your A.I. could develop an informed hypothesis based on a detailed record of your status updates, invites, DMs, and other potentially enduring ephemera that we’re often barely aware of as we create them, much less days, months or years after the fact. When you’re trying to decide if it’s time to move to a new city, your A.I. will help you understand how your feelings about home have evolved through thousands of small moments — everything from frustrated tweets about your commute to subtle shifts in how often you’ve started clicking on job listings 100 miles away from your current residence.”
Quanta: “Computer scientists often deal with abstract problems that are hard to comprehend, but an exciting new algorithm matters to anyone who owns books and at least one shelf. The algorithm addresses something called the library sorting problem (more formally, the “list labeling” problem). The challenge is to devise a strategy for organizing books in some kind of sorted order — alphabetically, for instance — that minimizes how long it takes to place a new book on the shelf. Imagine, for example, that you keep your books clumped together, leaving empty space on the far right of the shelf. Then, if you add a book by Isabel Allende to your collection, you might have to move every book on the shelf to make room for it. That would be a time-consuming operation. And if you then get a book by Douglas Adams, you’ll have to do it all over again. A better arrangement would leave unoccupied spaces distributed throughout the shelf — but how, exactly, should they be distributed?”
Shekhar Gupta: “If you win elections by distributing to the poor, you have to find enough poor. Who can win by spraying tax money over a mere 7.5 per cent? That’s why the states have an incentive in creating two classes of the poor: The genuine, income-linked poor, and the electoral poor. State after state, this is the norm now. The electoral poor are often ten times what your next Census would count as genuine poor. In this market, the political class trades middle-class tax revenues for votes.”
