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Based on an academic paper (arXiv:2402.08797), this piece is a key reference for understanding compute governance as a practical AI policy tool, relevant to discussions of international AI regulation and safety enforcement mechanisms.
Metadata
Importance: 72/100blog postanalysis
Summary
This article argues that compute governance is a feasible and effective lever for AI policy, as compute is detectable, excludable, quantifiable, and produced by a highly concentrated supply chain. It outlines three mechanisms—tracking, allocation control, and hardware guardrails—and surveys existing government actions like U.S. export controls, the Biden AI executive order, and the EU AI Act's compute thresholds.
Key Points
- •Compute used to train leading AI systems has increased 350 million-fold over 13 years, making it central to both AI progress and its governance.
- •Governments can govern AI via compute by tracking usage, subsidizing or restricting access, and building enforcement mechanisms into hardware.
- •Compute is feasible to govern because it is detectable, excludable, quantifiable, and produced by a highly concentrated global supply chain.
- •The U.S. has imposed export controls on advanced AI chips and introduced reporting requirements for large training runs (>10^26 FLOP).
- •The EU AI Act targets foundation models trained above 10^25 FLOP, currently covering GPT-4, Gemini Ultra, Claude 3, and Inflection 2.
Cited by 1 page
| Page | Type | Quality |
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| Intervention Timing Windows | Analysis | 72.0 |
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_**Compute governance is a particularly important approach to AI governance because it is feasible.**_
.jpeg?sfvrsn=68c7c14b_7)Server racks in a data centers
Computing power—compute, for short—is a key driver of artificial intelligence (AI) progress. Over the past 13 years, the amount of compute used to train leading AI systems has [increased by a factor of 350 million](https://arxiv.org/abs/2202.05924). This has enabled the major AI advances that have recently gained global attention. However, compute is important not only for the progress of AI but also for its governance. Governments have taken notice. As we argue in a [recent paper](https://arxiv.org/pdf/2402.08797.pdf), they are increasingly engaged in compute governance: using compute as a lever to pursue AI policy goals, such as limiting misuse risks, supporting domestic industries, or engaging in geopolitical competition.
The Biden administration introduced [export controls](https://www.federalregister.gov/documents/2023/10/25/2023-23055/implementation-of-additional-export-controls-certain-advanced-computing-items-supercomputer-and) on advanced semiconductor manufacturing equipment and the most high-end AI-relevant chips, aimed at undercutting Chinese access to leading-edge AI applications. In October 2023, the administration’s executive order [“On the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence”](https://www.whitehouse.gov/briefing-room/presidential-actions/2023/10/30/executive-order-on-the-safe-secure-and-trustworthy-development-and-use-of-artificial-intelligence/) introduced reporting requirements on models trained using more compute (1026 operations) than any that have been trained to date. Overseas, the [EU’s AI Act](https://artificialintelligenceact.eu/) will place additional requirements on foundation models trained using more than 1025 operations, currently covering three or four existing systems (GPT-4, Gemini Ultra, Claude 3, and Inflection 2).
States understand the importance of compute. The U.S. and the EU are both investing $50 billion in subsidies through their Chips Acts. Companies understand its importance, too. Almost all start-ups working on advanced AI have entered into “compute partnerships” with U.S. Big Tech compute providers. This includes most recently the French company Mistral, even though it had branded itself as a French-European national champion. Microsoft is reportedly investing [$50 billion](https://www.semianalysis.com/p/microsoft-infrastructure-ai-and-cpu?publication_id=329241&post_id=138891071&isFreemail=true&r=3rfwu) into expanding its Azure data centers worldwide, one of the biggest corporate infrastructure investments ever. NVIDIA has rocketed up to having the third biggest market capitalization in the world. And to boost compute production, Sam Altman is reportedly trying
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