LLeMpower: Understanding Disparities in the Control and Access of Large Language Models
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Abstract (arXiv)
Large Language Models (LLMs) are a powerful technology that augment human skill to create new opportunities, akin to the development of steam engines and the internet. However, LLMs come with a high cost. They require significant computing resources and energy to train and serve. Inequity in their control and access has led to concentration of ownership and power to a small collection of corporations. In our study, we collect training and inference requirements for various LLMs. We then analyze the economic strengths of nations and organizations in the context of developing and serving these models. Additionally, we also look at whether individuals around the world can access and use this emerging technology. We compare and contrast these groups to show that these technologies are monopolized by a surprisingly few entities. We conclude with a qualitative study on the ethical implications of our findings and discuss future directions towards equity in LLM access.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 40 of its 64 references that are in the map. First posted on arXiv 14 Apr 2024.
Builds on
40 of 64 references.
References · 40
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28 more
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Built on by
Citing papers per year: 2024: 1; 2025: 2; 2026: 2.
5 papers.
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- 2024Building Understandable Messaging for Policy and Evidence Review (BUMPER) with AIcited by 0
- 2026Reasoning4Sciences: Bridging Reasoning Language Models to All Scientific Branchescited by 0
- 2026LLMs as Post-hoc Auditors of Physiological Plausibility in Symbolic Regression: A Clinician-Evaluated Case Studycited by 0
- 2026LLMs as Post-hoc Auditors of Physiological Plausibility in Symbolic Regression: A Clinician-Evaluated Case Study10 Sep 2026
- 2026Reasoning4Sciences: Bridging Reasoning Language Models to All Scientific Branches31 May 2026
- 2025Embodied AI with Foundation Models for Mobile Service Robots: A Systematic Review26 May 2025
- 2025TiEBe: Tracking Language Model Recall of Notable Worldwide Events Through Time13 Jan 2025
- 2024Building Understandable Messaging for Policy and Evidence Review (BUMPER) with AI27 Jun 2024