Open AI Ecosystem Dynamics

Simulating Tim O'Reilly's knowledge commons and open-weights vs proprietary AI value capture

Openness Score 92
Reuse Multiplier 4.2x
Knowledge Sustainability 78%
Capture / Rent 15%
Open Weights Ratio 85%
Share of frontier and distilled models released with public weights vs closed API inference.
Developer Reuse Multiplier 4.2
Compounding rate at which downstream devs extend, fine-tune, and fork shared knowledge.
API Rent Extraction 15%
Proportion of downstream economic value captured via closed proprietary API tollbooths.
Technical Commons Inflow 78%
Reinvestment into technical publishing, documentation, open datasets, and tutorials.
"Create more value than you capture. That has always been the fundamental rule of open systems and healthy ecosystems." — Tim O'Reilly
Open Weights & Commons
Downstream Developers
Closed API Platforms
Publishing & Literature
Interactive Network Flow • Drag Nodes to Explore

Ecosystem State Diagnostic

Open weights foundation fosters decentralized fine-tuning and reciprocal documentation loops. Knowledge commons remains sustainable with high developer velocity.

Publishing & Technical Literature Impact

Technical authors and educators benefit from direct code/weights inspection, maintaining thriving books, reference documentation, and open benchmarks.

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