AI 中文总结
本文通过控制跨度模型调和了人工智能与研究团队规模的关联矛盾，指出团队规模随AI能力呈拟凹性，完全可编码团队的峰值在2026-2030年，且人类任务少的领域先达峰值。
AI 中文摘要
人工智能与更大规模的研究团队相关联，但在数学这一最具可编码性的领域中，使用人工智能的个体研究者如今也能产出达到研究级别的成果。本文提出的控制跨度模型可调和这些观察结果：人工智能降低了执行成本，从而扩大了实验室规模；同时自动化了可编码任务，降低了每个单元的成员份额。因此，团队规模相对于人工智能能力呈拟凹性，最多有一个峰值。该模型预测，完全可编码的团队在有效自动化覆盖率达到一个闭式阈值时达到峰值，该阈值通常接近完全覆盖；在具有共同基础且存在内部峰值的领域中，那些不可简化的人类任务内容较少的领域会率先达到峰值。在明确的先验条件下，完全可编码峰值的90%预测区间为2026年至2030年。
英文摘要
Artificial intelligence is associated with larger research teams, yet in mathematics, among the most codifiable fields, individual researchers working with AI now produce research-grade results. A span-of-control model reconciles these observations. AI lowers execution cost, which expands laboratory scale, and automates codifiable tasks, which lowers the member share of each unit. Team size is therefore quasi-concave in AI capability, with at most one peak. The model predicts that a fully codifiable team peaks when effective automation coverage reaches a closed-form threshold, typically near complete coverage, and, among fields with shared primitives that possess an interior peak, those with less irreducibly human task content peak first. Under explicit priors, the 90 percent forecast intervals for the fully codifiable peak span 2026 to 2030.
Comments16 pages, 4 figures, 2 tables. JEL: D23, J24, O31. Appendices A-E contain the proofs, the Monte Carlo detail and the two-stage test protocol
