The “Big Effing Race” Mentality Is Reshaping AI Research - Anastasios Angelopoulos (LMArena)

What happens to AI research when success brings money, hype, and short-term pressure? In this conversation at NeurIPS, Anastasios Angelopoulos, co-founder and CEO at LMArena, traces the evolution of AI research from curiosity-driven academic work to today’s resource-intensive frontier, and asks what we’re losing along the way. Subscribe for more in-depth conversations with the researchers and founders behind frontier AI. The discussion spans: Why long-horizon, foundational research is increasingly hard to sustain How open evaluation efforts emerged from Berkeley’s academic culture The growing gap between curiosity-driven science and short-term impact metrics Why open science still matters, but can’t survive on ideals alone The uncomfortable truth about resources, compute, and who gets to participate This is a conversation about value systems in AI: what we reward, what we measure, and what kinds of researchers and ideas we make space for in the long run. Recorded at Laude Lounge @ NeurIPS 2025. More at laude.org. X: https://x.com/LaudeInstitute LinkedIn: / laude-institute Anastasios's X: https://x.com/ml_angelopoulos Anastasios on LinkedIn:   / ml-angelopoulos   --- Hosted by Andy Konwinski Creative Producer - Mike Maley Production Manager - Lauren Lukow Videographer - Andrew James Benson Assistant Camera - Bradley Smith Senior Video Editor / Graphics - Cai Lee Editors - Jordan Calig, Juan Diego Parra Audio Editor - Carter Wogahn Produced by K. Tighe, Kayleigh Karutis, and Chris Rytting. Produced by Laude in partnership with Pod People. --- Chapters 0:00 — Introduction: Open Research at the AI Frontier 0:36 — Why NeurIPS matters for frontier AI research 0:55 — What LLM Arena is and why evaluation defines progress 1:25 — From academic project to global AI benchmark 2:51 — How LLM Arena reshaped model evaluation 3:25 — The hidden risks in today’s AI research culture 3:47 — The endangered researcher problem 6:04 — Why curiosity-driven research is being squeezed out 7:44 — Long-horizon research and invisible impact 9:08 — Academia’s original mission in AI 10:23 — Resource concentration and narrowing ambition 10:30 — Why foundational breakthroughs take time 12:56 — Protecting long-term thinkers in AI 14:57 — Open vs closed research and global competitiveness 16:02 — Why open science historically wins 17:27 — Why open source AI is harder now 18:11 — Power, platforms, and control of the AI stack 20:06 — Two paths forward for open AI research 21:08 — “There’s a big effing race happening right now” 21:52 — Resources, urgency, and responsibility 22:09 — Closing: What open research needs next --- #artificialintelligence #responsibleai #aigovernance #opensource #airesearch #machinelearning #airesearch #ai #computerscience #LMArena #benchmarking