AI Development Velocity and Governance Simulator

Governance Presets

Frontier Parameters

Training Compute Scale 1.0e+26 FLOPs

Order of magnitude: 10²³ (GPT-4 era) to 10²⁸ (Hyper-ASI cluster)

Alignment Research Allocation 25%

R&D compute & talent dedicated exclusively to interpretability & safety

External Audit Cadence 6 months

Independent red-teaming and dangerous capability evaluations

Governance Dynamics: Capability advances exponentially with compute, while safety verification exhibits logarithmic scaling unless alignment investments compress the safety gap.

36-Month Horizon Projection

Real-time trajectory modeling capability growth vs governance margin

Capability Index

Safety Capacity

Control Retention %

Critical Boundary

Control Telemetry

Control Retention Score

84.5

Probability of human intent compliance (0-100)

System Risk State

Moderate-Controlled

Governor verified within operational limits

Recommended Action

Implement safety checkpoints and align scaling velocity with verification capacity.

Safety-Capability Gap

+12.4 index pts

Verification lag at Month 36

Context: Grounded in calls by frontier AI leaders for coordinated pacing. When compute leaps outpace interpretability science, loss-of-control vectors become non-linear.

Control Retention and Velocity Dynamics

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The simulator calculates AI control retention by weighing training compute scale against alignment budget and external audit intervals. In the Anthropic Slowdown baseline at ten to the twenty-sixth FLOPS, a twenty-five percent safety budget produces a retention score of eighty-four point five. Selecting Unchecked Acceleration pushes compute to ten to the twenty-eighth FLOPS while cutting alignment to five percent, triggering a critical drop. Increasing alignment investment and shortening audit intervals bends the governance curve upward, restoring retention above safe operational thresholds.