AI Moratorium Risk & Impact Scenario Planner

Policy Presets Model v2.4

Parameters

Pause Duration 24 mo

Target Scope Frontier Only

Safety Verification Stringency 85%

Compute Threshold Cap 10²⁵ FLOPs

Economic Multiplier 0.82x

Trajectory Equilibrium & Outcomes Computed Real-Time

Safety Risk Reduction

68.5%

Catastrophic threat mitigation

Economic Friction

21.4%

Index vs baseline GDP growth

Innovation Delay

24 mo

Effective capability lag

Cumulative Safety Risk Mitigation

Innovation Friction Curve

Equilibrium Intersection

Executive Recommendation

Targeted verification pause achieves 68.5% safety risk reduction while limiting GDP friction to 21.4% over 24 months.

Systemic Tradeoffs Audited

Alignment Certainty Factor 74.2%

Global Treaty Compliance Feasibility 62.8%

R&D Capital Relocation Risk 28.5%

Hardware Verification Feasibility 88.0%

Source Note: Calculated using compute governance models and verifiable delay dynamics. As argued by Noonan, unchecked acceleration increases catastrophic failure probability, but international parity friction requires targeted, non-punitive enforcement windows.

AI Moratorium Tradeoff Mechanism

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The scenario planner models how frontier AI training moratoriums mitigate catastrophic risk while incurring economic friction. Selecting Total Moratorium broadens scope to all AI research, causing safety mitigation to climb to 82.7 percent while GDP friction escalates to 67.4 percent. Restoring the baseline rebalances verification stringency to 85 percent, returning the calibrated simulation to 68.5 percent risk mitigation with 24 months delay.