Articles on AI, its economics, and the institutions it's reshaping.
The form AI workforce transformation has to take inside a large firm. Set centrally, executed locally, and durable enough to hold the trust the transition requires.
The flattering version of the AI workforce conversation says we're all going to be augmented; the doomer version says we're all going to be replaced. Both miss the point.
2024 was supposed to be the year AI broke democratic politics. The catastrophe didn't arrive, but the conclusion that the threat was overstated is precisely the kind of misreading that leaves democracies unprepared.
The productivity case for AI is increasingly well-evidenced. The more consequential question is not whether AI delivers gains, it is where those gains go, and whether the economy is structured to distribute them.
In 1937, Ronald Coase asked why firms exist. AI agents represent the most consequential reduction in transaction costs in economic history, and his framework helps explain what comes next.
AI predictions have a poor track record, not because the technology is overhyped, but because the path from capability to consequence is part of a complex system, and it means that governance built on prediction will always arrive too late. The better question isn't what will AI do? It's what do we want?