Position: The Current AI Conference Model is Unsustainable! Diagnosing the Crisis of Centralized AI Conference

TL;DR

The paper diagnoses the unsustainability of current centralized AI conferences across multiple dimensions and proposes a decentralized Community-Federated Conference model to address these issues.

Contribution

It introduces the Community-Federated Conference model, a novel decentralized approach to AI conferences aimed at improving sustainability, inclusivity, and resilience.

Findings

AI conference publication rates have more than doubled in a decade.

Environmental impact of conferences exceeds local daily emissions.

High levels of negative sentiment and mental health concerns in online discourse.

Abstract

Artificial Intelligence (AI) conferences are essential for advancing research, sharing knowledge, and fostering academic community. However, their rapid expansion has rendered the centralized conference model increasingly unsustainable. This paper offers a data-driven diagnosis of a structural crisis that threatens the foundational goals of scientific dissemination, equity, and community well-being. We identify four key areas of strain: (1) scientifically, with per-author publication rates more than doubling over the past decade to over 4.5 papers annually; (2) environmentally, with the carbon footprint of a single conference exceeding the daily emissions of its host city; (3) psychologically, with 71% of online community discourse reflecting negative sentiment and 35% referencing mental health concerns; and (4) logistically, with attendance at top conferences such as NeurIPS 2024 beginning to outpace venue capacity. These pressures point to a system that is misaligned with its core mission. In response, we propose the Community-Federated Conference (CFC) model, which separates peer review, presentation, and networking into globally coordinated but locally organized components, offering a more sustainable, inclusive, and resilient path forward for AI research.