Core Policy Thesis: When disproportionate computing clusters and autonomous algorithmic capabilities centralize inside an unregulated oligopoly, public accountability collapses. To safeguard civil liberties, democratic institutions, and economic stability, frontier AI corporations must relinquish exclusive monopoly over model parameters and compute capacity.
Governance Levers & Policy Presets
Pre-configured Scenarios:
85%
Share of world-leading training clusters (H100/B200 equivalents) monopolized by top 3 labs.
20%
Mandatory disclosure of training dataset provenance, weights access, and algorithmic evaluations.
90%
Effective barrier requiring mandatory state/international safety clearance prior to deployment.
30%
Binding red-teaming authority and third-party inspection without lab NDA restrictions.
Dynamic Consequence: Lowering compute concentration and elevating transparency forces public model relinquishment, reducing uninhibited corporate dominion while reinforcing democratic oversight.
Institutional Power & Risk Metrics
Power Centralization Index
78.5
High Concentration
Public Governance Score
34.2
Vulnerable
Equilibrium Comparison: Corporate Power vs. Relinquished Controls
Severe Oligopolistic Dominance
Frontier capabilities remain heavily insulated from independent scrutiny. Corporate developers dictate deployment pacing with nominal external friction, creating systemic regulatory capture.
Ready to capture current balance.