AI Self-R&D Velocity & Recursion Simulator

Model Parameters

Scenario Presets

Current Autonomous R&D Share 26%

Anthropic reports Claude currently leads 26% of internal architectural R&D experiments.

Monthly Acceleration Rate 4.5% / mo

Compounding expansion rate per month as synthetic evaluations and code synthesis optimize next-gen kernels.

Human Review & Safety Bandwidth 85%

Alignment research throughput. Drops below 70% introduce governance drag and divergence risks.

Projection Horizon 12 Months

Temporal window for iterative code refactoring, benchmark generation, and capability self-prompting.

Recursive Telemetry & Impact

Real-Time Compounding Engine Active

Projected Self-R&D 70.0% Autonomous R&D share

Velocity Multiplier 3.42x Effective research pacing

Governance Friction 28.5% Oversight saturation drag

Milestone Status High Recursion Velocity Frontier automation phase

Recursive Self-Improvement Trajectory

Autonomous AI R&D %

Governance Limit

Recursive Feedback Mechanism

AI models designing specialized loss functions, curating high-difficulty synthetic reasoning benchmarks, and rewriting distributed GPU kernels produce non-linear throughput gains compared to conventional manual engineering sprints.

Human-in-the-Loop Governance Bottleneck

When autonomous development exceeds human review bandwidth, governance friction rises. At 28.5% friction, verification latency and verification queues require automated proof-checking systems to avert unaligned divergence.

Recursive AI Self-R&D Dynamics

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Autonomous research compounding starts from an initial self-development baseline, calibrated here at twenty-six percent. Monthly acceleration compounds code synthesis and synthetic benchmarks into research velocity, driving projected autonomy up to seventy percent. Selecting the fully autonomous preset pushes projected self-development to ninety-nine percent, causing governance friction to peak against human bandwidth limits.