Parameterize international adoption velocity tiers for agentic AI

Develop a methodology to parameterize the logistic adoption function V(r, τ)—including tier assignment and parameters k, τ0, and L—for economies with varying AI readiness and regulatory environments (e.g., under the EU AI Act), enabling robust cross-country application of the ATE framework.

Background

The ATE framework relies on region-specific adoption tiers and S-curve parameters to project displacement timelines. The paper calibrates these for U.S. metros but notes that international extension requires systematic tiering and parameterization suited to diverse institutional and regulatory landscapes.

Because adoption dynamics are shaped by national AI readiness, governance, and sector composition, a principled approach to deriving k, τ0, and L by country or region is necessary to avoid mis-specification of cross-national exposure profiles.