Governance

Compute Governance

Regulatory approaches that use computational resources as a lever for AI oversight and control.

Definition

Compute Governance refers to regulatory and policy approaches that leverage computational resources (computing power, cloud infrastructure, specialized AI chips) as a mechanism for AI governance, oversight, and control. It recognizes that access to compute is a key bottleneck for developing advanced AI.

US Approach (Executive Order 14110):

  • Reporting requirements for models trained above compute thresholds (10^26 FLOP)
  • Know-your-customer requirements for cloud providers
  • Export controls on advanced AI chips
  • Reporting on large-scale compute clusters

EU AI Act Approach:

  • GPAI systemic risk threshold based on cumulative compute (10^25 FLOP)
  • Compute as proxy for model capability and potential risk
  • Commission can adjust thresholds as technology evolves

Governance Mechanisms:

  • Thresholds: Using compute metrics to trigger regulatory requirements
  • Reporting: Mandatory disclosure of compute usage
  • Access controls: Know-your-customer for cloud AI services
  • Export controls: Restricting advanced hardware exports
  • Allocation: Government influence over compute distribution

Rationale: Compute is measurable, concentrated among few providers, and correlated with AI capability—making it a practical regulatory leverage point.

Related concepts: Systemic Risk, General-Purpose AI Model with Systemic Risk, Frontier AI

Sources

  • Executive Order 14110
  • Frontier AI Governance