Resource Democratization: Is Compute the Binding Constraint on AI Research?
Rebecca Gelles, Veronica Kinoshita, Micah Musser, James Dunham
Abstract
Access to compute is widely viewed as a primary barrier to AI research progress. Compute resource disparities between academic and industry researchers is therefore a source of concern. Yet the experiences of researchers who might encounter resource constraints in their work have received no direct study. We addressed this gap by conducting a large survey of U.S. AI researchers that posed questions about project inputs, outcomes, and challenges. Contrary to popular narratives, responses from more than 500 participants revealed more concern about talent and data limitations than compute access. There were few differences between academic and industry researchers in this regard. The exception were researchers who already use large amounts of compute, and expressed a need for more. These findings suggest that interventions to subsidize compute without addressing the limitations on talent and data availability reported by our respondents might cause or exacerbate commonly cited resource inequalities, with unknown impact on the future of equitable research.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext 8d07437e-cf50-47df-98e5-c6345192f063Builds on1
Related papers
- The Cost of Scaling Down Large Language Models: Reducing Model Size Affects Memory before In-context LearningTian Jin, Nolan Clement, Xin Dong, Vaishnavh Nagarajan et al.ICLR 2024 · 2 citations
- Revisiting Computation for Research: Practices and TrendsJeremiah Giordani, Ziyang Xu, Ella Colby, August Ning et al.SC 2024 · 1 citation
- The World is Not Enough: Growing Waste in HPC-enabled Academic PracticeCarolynne Lord, Adrian Friday, Adrian Jackson, Caroline Bird et al.CHI 2025 · 4 citations
- A Theory of Data Acquisition and Pricing at ScaleAndrew Ilyas, Amin Saberi, Grigorios VelegkasICML 2026
- Amplifying Rural Educators' Perspectives: A Qualitative Study on the Impacts of Generative AI in Rural U.S. High SchoolsShira Michel, Benjamin Taylor, Sabrina Parra Díaz, Joseph B. Wiggins et al.CHI 2026 · 2 citations
