Dynamics of Socio-Institutional Asynchrony in Generative AI: Analyzing the Relative Importance of Intervention Timing vs. Enforcement Efficiency via the Socio-Institutional Asynchrony Model (SIAM)

TL;DR

This paper introduces the SIAM model to evaluate policy levers in AI governance, finding that timely intervention is more impactful than enforcement efficiency in reducing social burden.

Contribution

The study develops the SIAM framework and quantitatively compares the effects of intervention timing and enforcement efficiency in AI regulation.

Findings

Earlier intervention reduces social burden by 64%.

Enhanced enforcement efficiency reduces social burden by 30%.

Advancing intervention timing has roughly twice the impact of speeding up enforcement.

Abstract

The super-exponential growth of generative AI has intensified the institutional mismatch between the pace of technological diffusion and the speed of institutional adaptation. This study proposes the Socio-Institutional Asynchrony Model, or SIAM, to quantitatively evaluate the relative effectiveness of two policy levers: intervention timing and enforcement efficiency. Using the timeline of the EU AI Act and an assumed compute doubling time of six months, we conduct a high precision simulation with 10001 time steps. The results show that an earlier intervention timing reduces the cumulative social burden by approximately sixty four percent, whereas improving enforcement efficiency reduces it by only about thirty percent. We further demonstrate analytically that advancing the start of intervention has structurally higher sensitivity, with roughly twice the relative effectiveness, compared to accelerating enforcement speed. These findings suggest that the core value of AI governance lies in proactive timeliness rather than reactive administrative efficiency.