?asOf= parameter to see the current catalog state.Compute Threshold (AI Governance)
compute-threshold · Compute governance
A regulatory trigger expressed as floating-point operations (FLOPs) consumed during model training, above which specific reporting, evaluation, or governance obligations attach.
Definition & scope
Compute thresholds operationalize the intuition that capability scales (imperfectly) with training compute. Jurisdictions have adopted different thresholds: US EO 14110 used 10²⁶ FLOPs for foundation-model reporting; EU AI Act Art. 51 uses 10²⁵ FLOPs as the systemic-risk presumption; China's GenAI Measures use no compute threshold (registration triggered by public-facing deployment instead); UK AISI commitments are voluntary and capability-based rather than compute-thresholded. Critics note that thresholds become outdated as algorithmic efficiency improves and that compute alone is an imperfect capability proxy.
Locus of dispute: Is compute-thresholding a defensible proxy for governance-relevant capability? Algorithmic-efficiency improvements (DeepSeek R1 demonstrating frontier-tier reasoning below 10²⁵ FLOPs) destabilize the threshold; field is split on whether compute thresholds should be indexed to efficiency, replaced by behavioural evaluation, or kept fixed for predictability.
Use in governance
How instruments operationalise this concept
| Instrument | Jurisdiction | Status |
|---|---|---|
| EU AI Act | EU | in force |
| Executive Order 14110 on Safe, Secure, Trustworthy AI | US | partial |
Appears in topic articles
Editorial note
When citing a specific FLOP threshold, always pair it with the jurisdiction and instrument. '10²⁵ FLOPs' is meaningful only under EU AIA; the same number has different implications in other regimes.
See also
Further reading
Sources on the broader topics this concept relates to — complementing, not standing in for, the primary sources cited inline above. 65 academic & grey-literature sources; catalogued metadata with a primary link; one-line findings are ✦ AI-generated summaries, labeled as such (charter §7.9). Browse the full literature index.
- Geopolitical ecologies of cloud capitalism: Territorial restructuring and the making of national computing power in the U.S. and China Peer-reviewed✦ AIUS and Chinese drives for sovereign AI/cloud dominance depend on reorganizing land, energy and regulatory systems to sustain large-scale national computing power.
- An interdisciplinary account of the terminological choices by EU policymakers ahead of the final agreement on the AI Act: AI system, general purpose AI system, foundation model, and generative AI Peer-reviewed✦ AITraces how the AI Act's legal text shifted across versions among the terms 'AI system, general purpose AI system, foundation model, and generative AI', exposing definitional instability in the regime.
- The EU model of AI governance: regulating artificial intelligence through law and policy Peer-reviewed✦ AIAnalyses how the AI Act's risk-based model handles general-purpose and foundation models whose 'autonomous content generation challenges legal categories of authorship, accountability, and control'.
- Generative AI and data protection Peer-reviewed✦ AIExamines friction between foundation-model training and the GDPR, noting models that 'memorize and leak pieces of training data' cannot be treated as anonymous.
- Defending Compute Thresholds Against Legal Loopholes Preprint✦ AIIdentifies 'enhancement techniques that are capable of decreasing training compute usage while preserving... model capabilities', exposing loopholes in compute-reporting thresholds.
- Digital Disintegration: Techno-Blocs and Strategic Sovereignty in the AI Era Peer-reviewed✦ AIArgues states increasingly assert 'strategic digital sovereignty...through selective alliances with firms and other governments,' fragmenting global AI infrastructure into techno-blocs rather than multilateral order.
- GPTs are GPTs: Labor market impact potential of LLMs Peer-reviewed✦ AIFinds around 80% of the U.S. workforce "could have at least 10% of their work tasks affected" by LLMs, which exhibit "traits of general-purpose technologies".
- Computing Power and the Governance of Artificial Intelligence Preprint✦ AIArgues compute is a uniquely governable lever because it is "detectable, excludable, and quantifiable, and is produced via an extremely concentrated supply chain".
- Training Compute Thresholds: Features and Functions in AI Regulation Preprint✦ AIFinds "training compute currently is the most suitable metric to identify GPAI models", but thresholds should only trigger further scrutiny, not determine risk measures alone.
- Compute North vs. Compute South: The Uneven Possibilities of Compute-based AI Governance Around the Globe Peer-reviewed✦ AICensus of hyperscale cloud regions shows a divide between "Compute North" states hosting training-relevant compute and a Compute South, shaping who can wield compute-based governance.
- Generative AI in EU law: Liability, privacy, intellectual property, and cybersecurity Peer-reviewed✦ AIExamines how the EU AI Act, liability regimes, GDPR, copyright and cybersecurity rules apply to generative AI, identifying gaps and proposing targeted regulatory refinements.
- Infrastructuring AI: The stabilization of 'artificial intelligence' in and beyond national AI strategies Peer-reviewed✦ AIShows the UK National AI Strategy 'stabilises: AI as an autonomous and inevitable force', revealing how national strategies fix actors, capital flows, and power relations.
+ 53 more across this concept's topics — see the literature index.
References
The primary instrument sources behind the article's classifications.
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