# The Case for Universal Basic Computing Power

arXiv: https://arxiv.org/abs/2311.12872 · HTML: https://mlsanity.com/paper/2311.12872/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 6 references that are in the map.
- First posted on arXiv 18 Nov 2023.

## Builds on (most cited first, 4 of 4)

- [Datasheets for Datasets](https://mlsanity.com/paper/1803.09010/) (2018)
- [Model Cards for Model Reporting](https://mlsanity.com/paper/1810.03993/) (2018)
- [Compute Trends Across Three Eras of Machine Learning](https://mlsanity.com/paper/2202.05924/) (2022)
- [Machine Learning Model Sizes and the Parameter Gap](https://mlsanity.com/paper/2207.02852/) (2022)

## Abstract (arXiv)

The Universal Basic Computing Power (UBCP) initiative ensures global, free access to a set amount of computing power specifically for AI research and development (R&D). This initiative comprises three key elements. First, UBCP must be cost free, with its usage limited to AI R&D and minimal additional conditions. Second, UBCP should continually incorporate the state of the art AI advancements, including efficiently distilled, compressed, and deployed training data, foundational models, benchmarks, and governance tools. Lastly, it's essential for UBCP to be universally accessible, ensuring convenience for all users. We urge major stakeholders in AI development large platforms, open source contributors, and policymakers to prioritize the UBCP initiative.