Source · Tier B · Preprint
Computing Power and the Governance of Artificial Intelligence
G. Sastry, L. Heim, H. Belfield, M. Anderljung, M. Brundage, J. Hazell, C. O'Keefe, G. K. Hadfield, R. Ngo, K. Pilz, G. Gor, E. Bluemke, S. Shoker, J. Egan, R. F. Trager, S. Avin, A. Weller, Y. Bengio, D. Coyle. 2024. arXiv.
| Link | https://arxiv.org/abs/2402.08797 |
|---|---|
| DOI | 10.48550/arXiv.2402.08797 |
| arXiv | 2402.08797 |
| Accessed | 2026-09-23 |
| Organization | Oxford Martin AI Governance Initiative |
| Imported from | hodgkins-ai-verification-papers@c71e59ff0e8e |
| Note | Listed under "Motivations and policy proposals" in the Hodgkins bibliography (CC BY 4.0). Also listed on the Oxford Martin AI Governance Initiative's publications page (https://aigi.ox.ac.uk/publications/computing-power-and-the-governance-of-artificial-intelligence/, read 2026-09-24). |
Cited by
- Communication between compute groups is bounded
- Compute stock is at most a declared amount
- This compute runs inference, not training
- There is no undeclared relevant compute
- A training run stayed within declared limits
- Compartmentalization
- FLOP accounting
- Inference and training workloads
- Interconnect bandwidth
- Undeclared compute