TL;DR

AI disempowerment operates across markets, networks, and governance simultaneously, but our analytical tools don't cross those boundaries. We propose spectral graph metrics—spectral gap, Fiedler vector, eigenvalue distribution—as computable, cross-domain measures for tracking how the balance of influence shifts when AI enters coordination systems, and identify three specific quantities to monitor for AI governance.

Introduction

AI systems are changing how society coordinates — across markets, networks, governance institutions, scientific communities, all at once. The gradual disempowerment thesis captures why this is hard to address: human influence over collective outcomes can erode slowly, through ordinary competitive dynamics, without any single dramatic failure. AI systems become better at navigating coordination mechanisms, and the effective weight of human agency quietly decreases.

The stubborn part is that it operates across institutional boundaries simultaneously. Regulate algorithmic trading to maintain human oversight of markets, and competitive pressure shifts to network dynamics — whoever shapes information flow shapes what traders believe before they trade. Address attention capture in social networks, and the pressure migrates to governance advisory relationships. The problem flows around single-domain interventions like water finding cracks.