Scaling Participation in Modular AI Systems

Abstract

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, community-sourced 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, repurposing existing collaboration algorithms for this bottom-up paradigm. Participatory AI systems outperform monolithic LLMs by up to 15.42% (95% CI: [10.09%, 21.13%]) across 15 tasks, such as reasoning and factuality, surpassing models with more parameters than all contributed components combined. Further experiments show that these systems are especially strong at representing diverse cultures, values, and communities, 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, demonstrated here with academic contributors and benchmark evaluations, for transitioning from the monolithic status quo toward an open, bottom-up, and collaborative AI future.