AI Safety & Compute Scaling Scenario Modeler

Strategic Scenarios

Consensus Framework

Allocation Variables

Frontier Compute Scale 50,000 PFLOP/s

10k PFLOP/s (Cluster) 200k PFLOP/s (Gigawatt)

Dedicated Safety & Eval Overhead 25%

5% (Permissive) 60% (Strict Verification)

Regulatory Friction & Compliance Burden

Executive Alignment Architecture

Industry Stance Memo

CNBC reported that Meta CEO Mark Zuckerberg aligned with Nvidia CEO Jensen Huang against arbitrary government slowdowns on AI development, favoring open hardware deployment paired with iterative runtime safety over frontier moratoriums.

Effective Velocity 37,500 PFLOP/s Net useful research

Systemic Risk Score 34.2 / 100 Empirical exposure

Policy Stance Aligned Fast-Follower Strategic posture

Safety Margin Moderate Guardrails Defense-in-depth

Compute Scaling vs. Safety Frontier Trajectory

Projected risk curve and effective throughput over multi-epoch scaling runs

Effective Compute Systemic Risk

Simulation State Ledger

Dimension	Configured Input	Model Consequence	Status

Compute Scaling vs Safety Overhead Dynamics

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The simulator calculates effective training velocity by subtracting dedicated safety red-teaming overhead from gross frontier compute scale. Concurrently, empirical systemic risk is scaled by compute intensity and dampened by safety verification and regulatory friction. Selecting Precautionary increases evaluation overhead to 50 percent, suppressing systemic risk down while contracting effective research velocity.