Planning for Frontier Lab Scaling
planning-for-frontier-lab-scalinganalysisPath: /knowledge-base/models/planning-for-frontier-lab-scaling/
E705Entity ID (EID)
Page Recorddatabase.json — merged from MDX frontmatter + Entity YAML + computed metrics at build time
{
"id": "planning-for-frontier-lab-scaling",
"numericId": null,
"path": "/knowledge-base/models/planning-for-frontier-lab-scaling/",
"filePath": "knowledge-base/models/planning-for-frontier-lab-scaling.mdx",
"title": "Planning for Frontier Lab Scaling",
"quality": 55,
"readerImportance": 5.5,
"researchImportance": 5.5,
"tacticalValue": null,
"contentFormat": "article",
"tractability": null,
"neglectedness": null,
"uncertainty": null,
"causalLevel": null,
"lastUpdated": "2026-03-13",
"dateCreated": "2026-02-15",
"llmSummary": "Strategic framework analyzing how non-lab actors could respond to frontier AI labs deploying \\$100-300B+ pre-TAI. For philanthropies: analysis of potential shifts from matching spend to maximizing leverage; focus on pipeline, governance advocacy, and strategic timing. For governments: options for adaptive regulation, mandatory safety spending, public compute infrastructure. For academia: analysis of industry partnerships, safety curricula, talent retention via joint appointments. For startups: potential safety-as-service, evaluation infrastructure, niche specialization opportunities. For civil society: frameworks for accountability infrastructure, coalition building, public education. Key theme: the 2025-2028 window may be particularly important because lab spending patterns are being established, IPOs create new accountability mechanisms, and the pre-TAI period may be the last window for meaningful external influence.",
"description": "Strategic framework analyzing how governments, philanthropies, academia, startups, and civil society could respond to frontier AI labs deploying \\$100-300B+ pre-TAI, with analysis of potential interventions for each actor type.",
"ratings": {
"novelty": 7,
"rigor": 5,
"completeness": 7.5,
"actionability": 9
},
"category": "models",
"subcategory": "governance-models",
"clusters": [
"ai-safety",
"governance",
"community"
],
"metrics": {
"wordCount": 3288,
"tableCount": 19,
"diagramCount": 2,
"internalLinks": 27,
"externalLinks": 2,
"footnoteCount": 0,
"bulletRatio": 0.06,
"sectionCount": 35,
"hasOverview": true,
"structuralScore": 13
},
"suggestedQuality": 87,
"updateFrequency": 180,
"evergreen": true,
"wordCount": 3288,
"unconvertedLinks": [
{
"text": "80,000 Hours AI Safety Career Guide",
"url": "https://80000hours.org/problem-profiles/artificial-intelligence/",
"resourceId": "c5cca651ad11df4d",
"resourceTitle": "80,000 Hours AI Safety Career Guide"
},
{
"text": "80,000 Hours analysis",
"url": "https://80000hours.org/problem-profiles/artificial-intelligence/",
"resourceId": "c5cca651ad11df4d",
"resourceTitle": "80,000 Hours AI Safety Career Guide"
}
],
"unconvertedLinkCount": 2,
"convertedLinkCount": 0,
"backlinkCount": 1,
"citationHealth": {
"total": 1,
"withQuotes": 0,
"verified": 0,
"accuracyChecked": 0,
"accurate": 0,
"inaccurate": 0,
"avgScore": null
},
"hallucinationRisk": {
"level": "medium",
"score": 55,
"factors": [
"no-citations"
]
},
"entityType": "analysis",
"redundancy": {
"maxSimilarity": 19,
"similarPages": [
{
"id": "ai-talent-market-dynamics",
"title": "AI Talent Market Dynamics",
"path": "/knowledge-base/models/ai-talent-market-dynamics/",
"similarity": 19
},
{
"id": "frontier-lab-cost-structure",
"title": "Frontier Lab Cost Structure",
"path": "/knowledge-base/models/frontier-lab-cost-structure/",
"similarity": 19
},
{
"id": "pre-tai-capital-deployment",
"title": "Pre-TAI Capital Deployment: $100B-$300B+ Spending Analysis",
"path": "/knowledge-base/models/pre-tai-capital-deployment/",
"similarity": 18
},
{
"id": "safety-spending-at-scale",
"title": "Safety Spending at Scale",
"path": "/knowledge-base/models/safety-spending-at-scale/",
"similarity": 18
},
{
"id": "intervention-timing-windows",
"title": "Intervention Timing Windows",
"path": "/knowledge-base/models/intervention-timing-windows/",
"similarity": 16
}
]
},
"changeHistory": [
{
"date": "2026-02-18",
"branch": "claude/resolve-issue-251-XhJkg",
"title": "Remove legacy pageTemplate frontmatter",
"summary": "Removed the legacy `pageTemplate` frontmatter field from 15 MDX files. This field was carried over from the Astro/Starlight era and is not used by the Next.js application.",
"model": "opus-4-6",
"duration": "~10min"
},
{
"date": "2026-02-15",
"branch": "claude/migrate-cairn-pages-3Dzfj",
"title": "Migrate CAIRN pre-TAI capital pages",
"summary": "Migrated 6 new model pages from CAIRN PR #11 to longterm-wiki, adapting from Astro/Starlight to Next.js MDX format. Created entity definitions (E700-E705). Fixed technical issues (orphaned footnotes, extra ratings fields, swapped refs). Ran Crux improve --tier=polish on all 6 pages for better sourcing, hedged language, and numeric EntityLink IDs. Added cross-links from 4 existing pages (safety-research-value, winner-take-all-concentration, racing-dynamics-impact, anthropic-impact).",
"pr": 155
}
],
"coverage": {
"passing": 8,
"total": 13,
"targets": {
"tables": 13,
"diagrams": 1,
"internalLinks": 26,
"externalLinks": 16,
"footnotes": 10,
"references": 10
},
"actuals": {
"tables": 19,
"diagrams": 2,
"internalLinks": 27,
"externalLinks": 2,
"footnotes": 0,
"references": 1,
"quotesWithQuotes": 0,
"quotesTotal": 1,
"accuracyChecked": 0,
"accuracyTotal": 1
},
"items": {
"llmSummary": "green",
"schedule": "green",
"entity": "green",
"editHistory": "green",
"overview": "green",
"tables": "green",
"diagrams": "green",
"internalLinks": "green",
"externalLinks": "amber",
"footnotes": "red",
"references": "amber",
"quotes": "red",
"accuracy": "red"
},
"editHistoryCount": 2,
"ratingsString": "N:7 R:5 A:9 C:7.5"
},
"readerRank": 614,
"researchRank": 583,
"recommendedScore": 134.65
}External Links
No external links
Backlinks (1)
| id | title | type | relationship |
|---|---|---|---|
| pre-tai-capital-deployment | Pre-TAI Capital Deployment: $100B-$300B+ Spending Analysis | analysis | — |