Scaling Participation in Modular AI Systems
This work addresses the problem of centralized AI development by enabling bottom-up, participatory AI systems that better capture diverse human knowledge and values.
The paper introduces scaling participation, a new paradigm for building modular AI systems from diverse stakeholder contributions, and shows that such systems outperform monolithic LLMs by up to 15.4% across 15 tasks, with emergent capabilities solving over 15% of problems where individual models fail.
Humanity is a mosaic of multifaceted talents and needs, and any truly intelligent AI must reflect that richness. Yet the LLMs used by all are built by the few -- a centralized market of monolithic AI models structurally ill-suited to capture the diversity of human knowledge, reasoning, and values. Here we introduce scaling participation, a new paradigm in which modular AI systems are built from the bottom up through the contributions of diverse stakeholders. Participants contribute small models trained on their own interests and priorities; these models then collaborate in modular frameworks as compositional AI systems. Participatory AI systems outperform monolithic LLMs by up to 15.4% across 15 tasks, such as reasoning and factuality, surpassing models larger than all contributed components combined. Further experiments show that participatory AI systems benefit from contributor diversity, substantially improve on each contributor's original priorities, and exhibit emergent capabilities that allow them to solve over 15% of problems where all individual models fail. Scaling participation provides a technical foundation for transitioning from the monolithic status quo toward an open, bottom-up, and collaborative AI future.