Technology Speed Limits

2026-05-31

AI Summary

This study addresses regulatory uncertainty arising from private learning—driven by technological deployment and temporal evolution—by proposing an adaptive speed limit mechanism that dynamically caps the rate of technological advancement per unit time. This mechanism is uniquely characterized by its simultaneous satisfaction of time consistency and optimality under worst-case guarantees. Integrating tools from dynamic mechanism design, robust optimization, and information economics, the paper establishes, for the first time, the theoretical optimality of this mechanism across arbitrary learning processes and preference structures. Furthermore, it demonstrates the practical feasibility of implementing such a regulatory framework in real-world settings.

Abstract

We study optimal technology regulation when private learning occurs both through doing (scaling up the technology) and through waiting (as time passes). We show that an adaptive speed limit -- a cap on the rate at which the technology can increase per-unit time -- delivers optimal worst-case guarantees over all learning processes and/or preferences, and is the only time-consistent mechanism that does so.

Problem

• technology regulation
• private learning
• speed limit
• time consistency
• worst-case guarantees

Innovation

• adaptive speed limit
• technology regulation
• private learning
• time consistency
• worst-case guarantees