变革性技术的监管
Regulating Transformative Technologies
AER: Insights · 2024 · [{"name": "Daron Acemoğlu", "affiliation": ["Massachusetts Institute of Technology"]}, {"name": "Todd Lensman", "affiliation": ["Massachusetts Institute of Technology"]}]
中文摘要
生成式人工智能等变革性技术有望加速许多部门的生产率增长，但也带来了因潜在滥用而产生的新风险。我们构建了一个多部门技术采用模型，用以研究当社会能够随时间逐渐了解这些风险时，对变革性技术的最优监管。社会最优的技术采用是渐进的，且通常呈凸性。如果社会损害较大且与新技术的生产率成比例，那么更高的增长率反而会导致更慢的最优采用，这一结果颇具悖论性。当企业未能将全部社会损害内部化时，均衡状态下的技术采用是无效率的；与部门无关的监管虽有帮助，但通常不足以恢复最优状态。（JEL D21, H21, H25, O31, O33）
Abstract
Transformative technologies like generative AI promise to accelerate productivity growth across many sectors, but they also present new risks from potential misuse. We develop a multisector technology adoption model to study the optimal regulation of transformative technologies when society can learn about these risks over time. Socially optimal adoption is gradual and typically convex. If social damages are large and proportional to the new technology’s productivity, a higher growth rate paradoxically leads to slower optimal adoption. Equilibrium adoption is inefficient when firms do not internalize all social damages, and sector-independent regulation is helpful but generally not sufficient to restore optimality. (JEL D21, H21, H25, O31, O33)