Title: Societal Adaptation to AI Human-Labor Automation Show Chinese title

Title: 社会对人工智能人机自动化适应

Abstract: AI is transforming human labor at an unprecedented pace - improving 10$\times$ per year in training effectiveness. This paper analyzes how society can adapt to AI-driven human-labor automation (HLA), using Bernardi et al.'s societal adaptation framework. Drawing on literature from general automation economics and recent AI developments, the paper develops a "threat model." The threat model is centered on mass unemployment and its socioeconomic consequences, and assumes a non-binary scenario between full AGI takeover and swift job creation. The analysis explores both "capability-modifying interventions" (CMIs) that shape how AI develops, and "adaptation interventions" (ADIs) that help society adjust. Key interventions analyzed include steering AI development toward human-complementing capabilities, implementing human-in-the-loop requirements, taxation of automation, comprehensive reorientation of education, and both material and social substitutes for work. While CMIs can slow the transition in the short-term, significant automation is inevitable. Long-term adaptation requires ADIs - from education reform to providing substitutes for both the income and psychological benefits of work. Success depends on upfront preparation through mechanisms like "if-then commitments", and crafting flexible and accurate regulation that avoids misspecification. This structured analysis of HLA interventions and their potential effects and challenges aims to guide holistic AI governance strategies for the AI economy. Show Chinese abstract

Abstract: 人工智能正在以史无前例的速度改变人类劳动——每年培训效果提高10$\times$。 本文分析了社会如何适应由人工智能驱动的人类劳动自动化（HLA），使用了Bernardi等人 的社会适应框架。 结合一般自动化经济学和近期人工智能发展的文献，本文提出了一个“威胁模型”。 该威胁模型以大规模失业及其经济社会后果为中心，并假设在完全人工通用智能（AGI）接管和快速创造就业之间存在非二元情景。 分析探讨了“能力修改干预措施”（CMIs）和“适应性干预措施”（ADIs），前者塑造人工智能的发展方式，后者帮助社会调整。 关键的干预措施包括引导人工智能发展朝着补充人类能力的方向，实施人机协同要求，对自动化征税，全面重新定向教育，以及为工作提供物质和社会替代品。 虽然CMIs可以在短期内减缓转变，但重大自动化是不可避免的。 长期适应需要ADIs——从教育改革到提供工作收入和心理益处的替代品。 成功取决于通过“如果-那么承诺”等机制提前准备，并制定灵活且准确的监管，以避免规格错误。 对HLA干预措施及其潜在影响和挑战的结构化分析旨在指导人工智能经济的整体人工智能治理策略。