Pith. sign in

REVIEW 4 cited by

Governing Through the Cloud: The Intermediary Role of Compute Providers in AI Regulation

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2403.08501 v2 pith:RD7S5ZJW submitted 2024-03-13 cs.CY

Governing Through the Cloud: The Intermediary Role of Compute Providers in AI Regulation

classification cs.CY
keywords computeprovidersregulationrolearguecriticaleffectiveenforcement
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
0 comments
read the original abstract

As jurisdictions around the world take their first steps toward regulating the most powerful AI systems, such as the EU AI Act and the US Executive Order 14110, there is a growing need for effective enforcement mechanisms that can verify compliance and respond to violations. We argue that compute providers should have legal obligations and ethical responsibilities associated with AI development and deployment, both to provide secure infrastructure and to serve as intermediaries for AI regulation. Compute providers can play an essential role in a regulatory ecosystem via four key capacities: as securers, safeguarding AI systems and critical infrastructure; as record keepers, enhancing visibility for policymakers; as verifiers of customer activities, ensuring oversight; and as enforcers, taking actions against rule violations. We analyze the technical feasibility of performing these functions in a targeted and privacy-conscious manner and present a range of technical instruments. In particular, we describe how non-confidential information, to which compute providers largely already have access, can provide two key governance-relevant properties of a computational workload: its type-e.g., large-scale training or inference-and the amount of compute it has consumed. Using AI Executive Order 14110 as a case study, we outline how the US is beginning to implement record keeping requirements for compute providers. We also explore how verification and enforcement roles could be added to establish a comprehensive AI compute oversight scheme. We argue that internationalization will be key to effective implementation, and highlight the critical challenge of balancing confidentiality and privacy with risk mitigation as the role of compute providers in AI regulation expands.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Macro-Prudential AI Governance: A Two-Layer Early Warning and Response System for Frontier AI

    cs.CY 2026-07 conditional novelty 6.0

    A Basel-III-style two-layer system—coordinated finder-coordinator-defender reporting plus ECAR, CRTH, and ARS buffers—can detect and dampen correlated risk build-up across frontier AI labs’ internal deployments.

  2. Detecting Hidden ML Training With Zero-Overhead Telemetry

    cs.LG 2026-06 unverdicted novelty 6.0

    A classifier using NVML telemetry identifies ML training workloads at 98.2% accuracy and retains 43-87% accuracy against the strongest tested adversarial evasions across 9 GPUs and 5 iteration rounds.

  3. How Sovereign Is Sovereign Compute? A Review of 775 Non-U.S. Data Centers

    cs.CY 2025-07 unverdicted novelty 6.0

    U.S. operators control 48% of non-U.S. data center projects by investment value, limiting digital sovereignty for host nations and offering the U.S. an additional governance tool for deployed AI infrastructure.

  4. Hardware-Level Governance of AI Compute: A Feasibility Taxonomy for Regulatory Compliance and Treaty Verification

    cs.CR 2026-04 unverdicted novelty 5.0

    Proposes a feasibility taxonomy of 20 hardware-level AI compute governance mechanisms organized by monitoring, verification, and enforcement, with mappings to regulatory scenarios that highlight immaturity of treaty-v...