A multi-agent reinforcement learning system was developed to determine whether Papr should open-source its predictive memory layer, which achieved a 92% score on Stanford's STARK benchmark. The system involved four stakeholder agents and ran 100,000 Monte Carlo simulations, revealing that 91.5% favored an open-core approach, showing a significant average net present value (NPV) advantage of $109M compared to $10M for a proprietary strategy. The decision to open-source was influenced by deeper memory agents favoring open-core, while shallow memory agents preferred proprietary options. The open-source move aims to accelerate adoption and leverage community contributions while maintaining strategic safeguards for monetization through premium features and ecosystem partnerships. This matters because it highlights the potential of AI-driven decision-making systems in strategic business decisions, particularly in the context of open-source versus proprietary software models.
