Compute Monitoring
monitoringpolicyPath: /knowledge-base/responses/monitoring/
E464Entity ID (EID)
Page Recorddatabase.json — merged from MDX frontmatter + Entity YAML + computed metrics at build time
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"llmSummary": "Analyzes two compute monitoring approaches: cloud KYC (implementable in 1-2 years, covers ~60% of frontier training via AWS/Azure/Google) and hardware governance (3-5 year timeline). Cloud KYC targets 10^26 FLOP threshold (~\\$10-100M training cost), but on-premise compute and jurisdictional arbitrage enable evasion; hardware-level monitoring could address this but faces substantial technical challenges.",
"description": "This framework analyzes compute monitoring approaches for AI governance, finding that cloud KYC (targeting 10^26 FLOP threshold) is implementable now via the three major providers controlling 60%+ of cloud infrastructure, while hardware-level governance faces 3-5 year development timelines. The EU AI Act uses a lower 10^25 FLOP threshold. Evasion through on-premise compute and jurisdictional arbitrage remains the primary limitation.",
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{
"lesswrong": "https://www.lesswrong.com/tag/compute-governance"
}Backlinks (12)
| id | title | type | relationship |
|---|---|---|---|
| hardware-enabled-governance | Hardware-Enabled Governance | policy | — |
| thresholds | Compute Thresholds | policy | — |
| international-regimes | International Compute Regimes | policy | — |
| accident-risks | AI Accident Risk Cruxes | crux | — |
| misuse-risks | AI Misuse Risk Cruxes | crux | — |
| ai-compute-scaling-metrics | AI Compute Scaling Metrics | analysis | — |
| bioweapons-attack-chain | Bioweapons Attack Chain Model | analysis | — |
| short-timeline-policy-implications | Short Timeline Policy Implications | analysis | — |
| holden-karnofsky | Holden Karnofsky | person | — |
| coordination-tech | AI Governance Coordination Technologies | approach | — |
| export-controls | AI Chip Export Controls | policy | — |
| governance-overview | AI Governance & Policy (Overview) | concept | — |