Reclaiming "Open AI" -- AI Model Serving Can Be Open Access, Yet Monetizable and Loyal

• URL: http://arxiv.org/abs/2411.03887v3
• Date: Tue, 03 Jun 2025 04:28:46 GMT
• Title: Reclaiming "Open AI" -- AI Model Serving Can Be Open Access, Yet Monetizable and Loyal
• Authors: Zerui Cheng, Edoardo Contente, Ben Finch, Oleg Golev, Jonathan Hayase, Andrew Miller, Niusha Moshrefi, Anshul Nasery, Sandeep Nailwal, Sewoong Oh, Himanshu Tyagi, Pramod Viswanath,
• Abstract summary: The rapid rise of AI has split model serving between open-weight distribution and opaque API-based approaches.<n>This position paper introduces, rigorously formulates, and champions the Open-access, Monetizable, and Loyal (OML) paradigm for AI model serving.
• Score: 39.63122342758896
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The rapid rise of AI has split model serving between open-weight distribution, which often lacks owner control and monetization, and opaque API-based approaches that risk user privacy and model transparency, forming a dichotomy that hinders an equitable AI ecosystem. This position paper introduces, rigorously formulates, and champions the Open-access, Monetizable, and Loyal (OML) paradigm for AI model serving: a foundational shift to securely distribute and serve AI models by synthesizing transparency with granular monetization and critical safety controls. We survey diverse OML constructions from theory and practice, analyze their security, performance, and practical trade-offs, outline a conceptual OML deployment protocol, and discuss market and policy implications. We assert that OML can foster a democratized, self-sustaining, and innovative AI landscape, mitigating centralized power risks. Finally, we call on the research community to further explore the broad design space of OML, spanning cryptographic, AI-native, and socio-economic mechanisms, to realize its full potential for a collaborative, accountable, and resilient AI future.

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