Recursive AI R&D Development Tracker & Metrics Workbench

Model Parameters

Baseline Year 2026

Scenario Presets

1. AI-Driven R&D Share

Portion of next-gen architecture, code, and training workflows authored by AI systems.

2. Training Speed Multiplier 3.8x

Accelerated empirical loop speed enabled by autonomous synthetic data and automated ablation.

3. Compute Scaling Factor 4.5x

Cluster hardware capacity & algorithmic FLOP utilization efficiency scaling.

4. Safeguard Constraint Level Moderate

Governance, alignment testing latency, and sandboxing gating overhead.

The Feedback Principle: As AI systems undertake more AI research (Measurement 1), each improvement cascades into subsequent iterations, shrinking the generational interval while elevating capability vectors.

AI Contribution Share

68.5%

Next Gen Horizon

7.4 mo

Recursive Velocity Index

84.2

Risk Divergence State

Controlled Growth

Generational Capability Progression (2026–2030)

D3.js Live Trajectory

Recursive Autonomous Curve (AI-Driven)

Human-Only Linear Baseline

Effective Safeguard Frontier Margin