CONCEPT
Adaptive Governance
Governance designed for learning rather than compliance — polycentric, cross-scale, diversity-maintaining, adequate to the dynamics of the AI transition.
Adaptive governance is the framework for governing complex systems in release or reorganization phases, where conservation-phase regulatory mechanisms fail. Its core principles: govern for learning rather than compliance; operate polycentrically across scales rather than within single jurisdictions; maintain diversity of approach rather than converging on a single model; incorporate experiential knowledge alongside expert assessment. Applied to AI, adaptive governance suggests that the EU AI Act, the American executive orders, and emerging frameworks elsewhere — all serious but all conservation-phase instruments applied to a release-phase phenomenon — must be supplemented by institutional arrangements capable of learning as fast as the system they govern.
In The You On AI Field Guide
Conservation-phase governance works through a characteristic sequence: expert assessment informs regulation, regulation specifies requirements, compliance is monitored, periodic review adjusts. The sequence assumes stable conditions between reviews. The AI transition is changing the system being governed faster than governance cycles can track, producing regulation calibrated for conditions that have already shifted.
The first adaptive principle — govern for learning — changes institutional design from command