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 and 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 Critics note that thresholds become outdated as algorithmic efficiency improves and that compute alone is an imperfect capability proxy.
Used by these instruments
Related concepts
- Frontier-Tier AI— A categorical classification of AI models above certain capability or compute thresholds, indicating
- Systemic Risk (AI)— A regulatory designation indicating that a general-purpose AI model poses risks of significant scale
- Designated Systemic-Risk Model— A general-purpose AI model that has been formally designated by the EU AI Office under Article 51(1)
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.
References
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