The Role of Computing Resources in Publishing Foundation Model Research

TL;DR

This paper examines how computing resources shape FM research by analyzing 6,517 FM papers from 2022–2024 and surveying 229 first-authors. It shows that greater compute relates to higher citation counts and venue acceptance but does not strictly track with research environment or methodology, and reveals substantial gaps in reporting GPU usage and other compute metrics. The findings highlight a centralization of access toward well-resourced institutions and open-weight model dominance, while underscoring the value of shared computing and better disclosure to broaden participation. The work suggests policy, infrastructure, and methodological changes to democratize FM research and ensure sustainable, transparent advancement. Overall, it provides a data-driven view of the compute landscape in FM research and practical recommendations to reduce barriers and improve reproducibility.

Abstract

Cutting-edge research in Artificial Intelligence (AI) requires considerable resources, including Graphics Processing Units (GPUs), data, and human resources. In this paper, we evaluate of the relationship between these resources and the scientific advancement of foundation models (FM). We reviewed 6517 FM papers published between 2022 to 2024, and surveyed 229 first-authors to the impact of computing resources on scientific output. We find that increased computing is correlated with national funding allocations and citations, but our findings don't observe the strong correlations with research environment (academic or industrial), domain, or study methodology. We advise that individuals and institutions focus on creating shared and affordable computing opportunities to lower the entry barrier for under-resourced researchers. These steps can help expand participation in FM research, foster diversity of ideas and contributors, and sustain innovation and progress in AI. The data will be available at: https://mit-calc.csail.mit.edu/

Figures (5)

• Figure 1: Study Design and Data Collection.
• Figure 2: Temporal Evolution of FMs. A) FM papers as a proportion of published papers over time. B) Evolution of FM papers over phases, methods, and domains. C) Temporal evolution of GPU Model distribution in LLM-extract and self-reported data. "Pub." denotes publications from the scraped dataset, while "Await Publish" refers to papers that are either under review, rejected, or in preparation for submission.
• Figure 3: Distribution of FM Papers. We analyze FM papers across various dimensions: (A) Senior Author's Affiliations in (A1) Academia and (A2) Industry; (B) Countries by Senior Author's Affiliation; (C) Paper Count by LLM Usage; and (D) GPU Types Used. The boxplots below panel (D) display GPU Number and TFLOPs across four categories: Affiliation, Phase, Method, and Domain. TFLOPS: Tera Floating-Point Operations Per Second; FP16: 16-bit floating-point format (Note: * p < 0.001).
• Figure 4: Funding Distribution of FM Research. Only 15.3% of FM papers contain funding country and agency information in their manuscript. A) Distribution of funding by country across three categories: Government, Corporate, and Foundation. B) Relationship between each country's GDP per capita and the number of funded papers. For academic and industry settings: C) Relationship between available GPU resources and average number of papers produced. D) Relationship between GPU resources and average citation count per paper.
• Figure 5: Author Number and Citation Number Versus Senior Authors' Affiliation, Domain, Phase, and Methodology. (Note: * p < 0.001).