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Computing Power and the Governance of Artificial Intelligence

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arxiv 2402.08797 v1 pith:LRFRPSQJ submitted 2024-02-13 cs.CY

classification cs.CY
keywords computedevelopmentgovernancepowerareasartificialbeneficialcomputing
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Forward citations

Cited by 12 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 21 citations worldwide. Full citation record

  1. Resourced Authority A Mechanism-Design Model for Participatory Governance of Deployed AI Agents

    cs.GT 2026-08 reject novelty 6.0 of 10

    This paper formalizes participatory AI governance as a compute-budget authorization game, but the central theorem's sufficiency proof fails to construct a valid equilibrium.

  2. Hardware Mechanisms to Dynamically Throttle AI Performance

    cs.AR 2026-07 conditional novelty 6.0 of 10

    Dynamic microarchitecture throttling of GPU memory resources can cut LLM inference performance by up to 80% with low hardware overhead, giving architects a continuous, hardware-enforced AI capability control.

  3. Decentralised AI Training and Inference with BlockTrain

    cs.AI 2026-06 unverdicted novelty 6.0 of 10

    BlockTrain partitions models into blocks trained on local objectives, reaching CE 1.359 on WikiText within 0.04 of end-to-end baseline while enabling distributed training and inference over TCP for up to 75B-parameter models.

  4. How to Catch a GPU: A Taxonomy of Verification and Enforcement Mechanisms for International AI Agreements

    cs.CY 2026-06 conditional novelty 6.0 of 10

    Verification of international AI agreements will fail first at detecting hidden compute facilities, around the 10,000-H100-equivalent scale, before other enforcement mechanisms break.

  5. Path-conditioned training: a principled way to rescale ReLU neural networks

    stat.ML 2026-02 conditional novelty 6.0 of 10

    Rescaling ReLU weights at initialization with PathCond aligns the path kernel with the identity and trains to the same accuracy in up to 1.5× fewer epochs.

  6. Verifying International Agreements on AI: Six Layers of Verification for Rules on Large-Scale AI Development and Deployment

    cs.CY 2025-07 conditional novelty 6.0 of 10

    Countries could verify compliance with international AI agreements through six redundant verification layers, provided the report's listed hardware and analysis challenges are solved.

  7. Compute Requirements for Algorithmic Innovation in Frontier AI Models

    cs.LG 2025-07 conditional novelty 6.0 of 10

    Estimated development compute for 36 LLM pretraining innovations shows half would remain possible under GPT-2-level or 8-H100 compute caps.

  8. A Theory of Inference Compute Scaling: Reasoning through Directed Stochastic Skill Search

    cs.LG 2025-06 conditional novelty 6.0 of 10

    A skill-graph random-walk model gives closed-form accuracy-versus-compute formulas for four reasoning strategies and connects them to training scaling.

  9. Technical Options for Flexible Hardware-Enabled Guarantees

    cs.CR 2025-06 conditional novelty 6.0 of 10

    A hardware 'interlock' placed on AI accelerator network paths could provide privacy-preserving, verifiable guarantees about AI compute usage, according to a design analysis that sketches FLOP-counting and update protocols.

  10. Technical Requirements for Halting Dangerous AI Activities

    cs.AI 2025-07 conditional novelty 5.0 of 10

    A taxonomy of compute-centric technical interventions, graded by readiness and mapped to five AI governance plans, argues that halting dangerous AI requires substantial control over AI compute.

  11. Distributed and Decentralised Training: Technical Governance Challenges in a Shifting AI Landscape

    cs.CY 2025-07 conditional novelty 5.0 of 10

    A policy analysis distinguishing distributed and decentralised AI training, arguing decentralised training may erode detectability and shutdownability while compute controls remain relevant.

  12. Domestic frontier AI regulation, an IAEA for AI, an NPT for AI, and a US-led Allied Public-Private Partnership for AI: Four institutions for governing and developing frontier AI

    cs.CY 2025-07 accept novelty 5.0 of 10

    Compute governance can underpin four institutions for frontier AI: domestic regulation, an International AI Agency, a Secure Chips Agreement, and a US-led Allied Public-Private Partnership.

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