Dynamics of Human-AI Collective Knowledge on the Web: A Scalable Model and Insights for Sustainable Growth

TL;DR

This paper presents a scalable dynamical model of human-AI knowledge ecosystems on the web, analyzing growth regimes and policy impacts to inform sustainable development of shared knowledge archives.

Contribution

It introduces an interpretable model capturing co-evolution of human and AI knowledge, revealing how platform policies influence growth regimes and stability.

Findings

Identifies different growth regimes such as healthy growth and oscillations.

Shows how platform policies can shift the system between regimes.

Fits the model to Wikipedia data, revealing changes in human and AI contributions.

Abstract

Humans and large language models (LLMs) now co-produce and co-consume the web's shared knowledge archives. Such human-AI collective knowledge ecosystems contain feedback loops with both benefits (e.g., faster growth, easier learning) and systemic risks (e.g., quality dilution, skill reduction, model collapse). To understand such phenomena, we propose a minimal, interpretable dynamical model of the co-evolution of archive size, archive quality, model (LLM) skill, aggregate human skill, and query volume. The model captures two content inflows (human, LLM) controlled by a gate on LLM-content admissions, two learning pathways for humans (archive study vs. LLM assistance), and two LLM-training modalities (corpus-driven scaling vs. learning from human feedback). Through numerical experiments, we identify different growth regimes (e.g., healthy growth, inverted flow, inverted learning, oscillations), and show how platform and policy levers (gate strictness, LLM training, human learning pathways) shift the system across regime boundaries. Two domain configurations (PubMed, GitHub and Copilot) illustrate contrasting steady states under different growth rates and moderation norms. We also fit the model to Wikipedia's knowledge flow during pre-ChatGPT and post-ChatGPT eras separately. We find a rise in LLM additions with a concurrent decline in human inflow, consistent with a regime identified by the model. Our model and analysis yield actionable insights for sustainable growth of human-AI collective knowledge on the Web.