# Computing Power and the Governance of Artificial Intelligence

arXiv: https://arxiv.org/abs/2402.08797 · HTML: https://mlsanity.com/paper/2402.08797/

## Key facts

- Cited by 40 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Feb 2024.

## Built on by (most cited first, 30 of 40)

- [Knowledge Mechanisms in Large Language Models: A Survey and Perspective](https://mlsanity.com/paper/2407.15017/) (2024)
- [Immunization against harmful fine-tuning attacks](https://mlsanity.com/paper/2402.16382/) (2024)
- [Training Compute Thresholds: Features and Functions in AI Regulation](https://mlsanity.com/paper/2405.10799/) (2024)
- [Responsible Reporting for Frontier AI Development](https://mlsanity.com/paper/2404.02675/) (2024)
- [Societal Adaptation to Advanced AI](https://mlsanity.com/paper/2405.10295/) (2024)
- [Verification methods for international AI agreements](https://mlsanity.com/paper/2408.16074/) (2024)
- [The Singapore Consensus on Global AI Safety Research Priorities](https://mlsanity.com/paper/2506.20702/) (2025)
- [Trust AI Regulation? Discerning users are vital to build trust and effective AI regulation](https://mlsanity.com/paper/2403.09510/) (2024)
- [Path-conditioned training: a principled way to rescale ReLU neural networks](https://mlsanity.com/paper/2602.19799/) (2026)
- [Panacea: Novel DNN Accelerator using Accuracy-Preserving Asymmetric Quantization and Energy-Saving Bit-Slice Sparsity](https://mlsanity.com/paper/2412.10059/) (2024)
- [A Theory of Inference Compute Scaling: Reasoning through Directed Stochastic Skill Search](https://mlsanity.com/paper/2507.00004/) (2025)
- [Self-Improvement of Large Language Models: A Technical Overview and Future Outlook](https://mlsanity.com/paper/2603.25681/) (2026)
- [The Jumping Reasoning Curve? Tracking the Evolution of Reasoning Performance in GPT-[n] and o-[n] Models on Multimodal Puzzles](https://mlsanity.com/paper/2502.01081/) (2025)
- [International Agreements on AI Safety: Review and Recommendations for a Conditional AI Safety Treaty](https://mlsanity.com/paper/2503.18956/) (2025)
- [Trends in AI Supercomputers](https://mlsanity.com/paper/2504.16026/) (2025)
- [Detecting Hidden ML Training With Zero-Overhead Telemetry](https://mlsanity.com/paper/2606.19262/) (2026)
- [Towards Data Governance of Frontier AI Models](https://mlsanity.com/paper/2412.03824/) (2024)
- [Practical Principles for AI Cost and Compute Accounting](https://mlsanity.com/paper/2502.15873/) (2025)
- [Distinguishing Task-Specific and General-Purpose AI in Regulation](https://mlsanity.com/paper/2506.17347/) (2025)
- [Distributed and Decentralised Training: Technical Governance Challenges in a Shifting AI Landscape](https://mlsanity.com/paper/2507.07765/) (2025)
- [Agentic Inequality](https://mlsanity.com/paper/2510.16853/) (2025)
- [Position: Neglecting the Sustainability of AI is Fuelling a Global AI Arms Race](https://mlsanity.com/paper/2502.20016/) (2025)
- [Technical Requirements for Halting Dangerous AI Activities](https://mlsanity.com/paper/2507.09801/) (2025)
- [Compute Requirements for Algorithmic Innovation in Frontier AI Models](https://mlsanity.com/paper/2507.10618/) (2025)
- [Countering Autonomous Cyber Threats](https://mlsanity.com/paper/2410.18312/) (2024)
- [Trends in Frontier AI Model Count: A Forecast to 2028](https://mlsanity.com/paper/2504.16138/) (2025)
- [Comprehensive AI governance requires addressing non-model gains](https://mlsanity.com/paper/2606.00047/) (2026)
- [LLM-Enabled Multi-Agent Systems: Empirical Evaluation and Insights into Emerging Design Patterns & Paradigms](https://mlsanity.com/paper/2601.03328/) (2026)
- [The Economics of AI Supply Chain Regulation](https://mlsanity.com/paper/2603.12630/) (2026)
- [Precautionary Governance of Autonomous AI: Legal Personhood as Functional Instrument](https://mlsanity.com/paper/2605.12505/) (2026)

## Abstract (arXiv)

Computing power, or "compute," is crucial for the development and deployment of artificial intelligence (AI) capabilities. As a result, governments and companies have started to leverage compute as a means to govern AI. For example, governments are investing in domestic compute capacity, controlling the flow of compute to competing countries, and subsidizing compute access to certain sectors. However, these efforts only scratch the surface of how compute can be used to govern AI development and deployment. Relative to other key inputs to AI (data and algorithms), AI-relevant compute is a particularly effective point of intervention: it is detectable, excludable, and quantifiable, and is produced via an extremely concentrated supply chain. These characteristics, alongside the singular importance of compute for cutting-edge AI models, suggest that governing compute can contribute to achieving common policy objectives, such as ensuring the safety and beneficial use of AI. More precisely, policymakers could use compute to facilitate regulatory visibility of AI, allocate resources to promote beneficial outcomes, and enforce restrictions against irresponsible or malicious AI development and usage. However, while compute-based policies and technologies have the potential to assist in these areas, there is significant variation in their readiness for implementation. Some ideas are currently being piloted, while others are hindered by the need for fundamental research. Furthermore, naive or poorly scoped approaches to compute governance carry significant risks in areas like privacy, economic impacts, and centralization of power. We end by suggesting guardrails to minimize these risks from compute governance.