Position: The Current AI Conference Model is Unsustainable! Diagnosing
  the Crisis of Centralized AI Conference
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.
6893fe53741a16f544fbce21
Nuo Chen
claimed_verified
2025-08-07T10:41:41.756Z
641ac2207c21ab946bf036e8
https://cdn-avatars.huggingface.co/v1/production/uploads/641ac2207c21ab946bf036e8/r6c9gpOrul0eC59d9e2Mo.png
Nuo Chen
user
nuojohnchen
6893fe53741a16f544fbce22
Moming Duan
6893fe53741a16f544fbce23
Andre Huikai Lin
6893fe53741a16f544fbce24
Qian Wang
6893fe53741a16f544fbce25
Jiaying Wu
6893fe53741a16f544fbce26
Bingsheng He
Position: The Current AI Conference Model is Unsustainable! Diagnosing
  the Crisis of Centralized AI Conference
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.
The paper diagnoses structural issues in AI conferences, including publication rates, carbon footprint, negative community sentiment, and logistical challenges, and proposes a Community-Federated Conference model to address these issues.