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

It is demonstrated analytically that advancing the start of intervention has structurally higher sensitivity, with roughly twice the relative effectiveness, compared to accelerating enforcement speed, which suggests that the core value of AI governance lies in proactive timeliness rather than reactive administrative efficiency.

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.

Figures (1)

• Figure 1: Comparison of Total Social Burden ($H_{total}$) between Timing and Efficiency Strategies. Note that the Y-axis represents the Maturity Level of Adoption/Institution (0--1). The red shaded area corresponds to the Timing Strategy, and the blue shaded area corresponds to the Efficiency Strategy.