Peter Turchin et al?

This podcast episode I enjoyed is somewhat an example: https://www.chinatalk.media/p/autocracy-and-stagnation-how-imperial Opus 4.6 summary of the relevance Huang's work is genuinely capital-intensive, quantitative history. The standout detail from the episode: he and Chinese collaborators spent six years with around 40 research assistants digitizing Joseph Needham's 27-volume Science and Civilisation in China to build a statistical database — Needham himself never analyzed his material quantitatively. That database powers the CDI (inventions-per-capita) scores that drive Huang's central empirical claim that China was most inventive during its fragmented post-Han "European moment" (220–589 CE), before keju was institutionalized. He also has a co-authored paper with Clair Yang doing statistical work on civil service exams and imperial stability, plus statistical analyses of social mobility in imperial China across dynasties. This is the opposite of vibes-based humanities history — it's a multi-year, multi-person, data-infrastructure-first research program. It also generates falsifiable forward predictions: Huang argues Xi's elimination of term limits has reintroduced the ancient succession problem and that current top-down industrial policy will produce Brezhnev-style stagnation. Those are bets you can score over the next decade or two. Where it doesn't fit: The LW commenter's stronger ask is for fields where quality is judged by prediction track record. Huang's work isn't judged that way — it's still judged by academic peer review, theoretical elegance, and historiographical argument. Nobody is keeping a Brier score on his China forecasts. The infrastructure is quantitative; the epistemic culture is still humanities-academic. The better pointer to give them: The episode is one node in a larger movement: the Center for Quantitative History (CQH) and the broader cliometrics-of-China field — Yuhua Wang (The Rise and Fall of Imperial China, statistical analysis o