AI Safety Slowdown & Governance Simulator

Quantitative Policy Dynamic Lab • Frontier Model Scaling vs Safeguards
Context: Former VP Kamala Harris stated that "a slowdown is critical in order to ensure AI serves the public interest" (The Spectator Index).
Policy Levers
Pre-Configured Policy Scenarios:
AI Training Acceleration 0.35x
Compute scaling velocity and deployment cadence.
Safety Research Allocation 75%
Budgets committed to mechanistic interpretability and red-teaming.
Governance Friction & Oversight 0.60
Independent audits, threshold licensing, and mandated slowdown buffers.
Event Telemetry Log
[INIT] Baseline scenario calibrated: Balanced Slowdown.
Dynamic System Telemetry Real-time Equilibrium Model
Public Alignment Score
84.2%
Optimal Safeguards Maintained
Systemic Risk Index
12.4%
Low Risk
Societal Benefit Yield
76.8%
Sustainable Growth
Projected Stability
Stable Equilibrium
Resilient
5-Year Projected Trajectory Under Current Governance Posture
Public Alignment
Systemic Risk
Capability Frontier

AI Safety Slowdown & Governance Simulator Explainer

Read the explanation

This simulator models how training acceleration, safety research allocation, and governance friction interact to determine risk and public alignment. When acceleration exceeds combined safety and oversight buffers, systemic risk compounds non-linearly across the simulated five-year trajectory. Adjusting the policy levers or selecting Balanced Slowdown restores safety buffers, maintaining public alignment above eighty percent and low systemic hazard.

Enjoy this tool? Build your own with Super