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  "id": "O716-01",
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  "claim": "AI-economics research, labor displacement and capability projection (Eloundou et al.) among it, measures which jobs AI eliminates, what tasks it can perform and what consumer surplus it generates",
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  "locus": "§2.2 'Economic Framing of AI Composition', s1–2",
  "quote": "AI economics research focuses primarily on labor displacement (Acemoglu and Restrepo 2019), capability projection (Eloundou et al. 2023), and welfare estimation (Brynjolfsson, Li, and Raymond 2023). These frameworks measure which jobs AI eliminates, what tasks it can perform, and what consumer surplus it generates.",
  "kernel": true,
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  "lineage": "a channel the employment count does not register: value extracted where labor remains",
  "date": "2026-05-03",
  "title": "Provenance Erasure Rate: A Compression-Survival Metric for Attribution Loss in AI-Composed Search Outputs",
  "creator": "Lee Sharks",
  "file": "data/texts/AXN-025F-text.md",
  "offset": 5255,
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  "dep": 716,
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  "claim": "a distinct channel those frameworks do not measure: where human labor remains, the value tied to provenance is extracted by the system; extraction without attribution, not displacement",
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  "locus": "§2.2, s3–5",
  "quote": "PER identifies a distinct channel: even when human labor *remains* (the author's work is used), the economic value tied to provenance — reputation, traffic, citation credit, contractual rights — is extracted by the system. This is not displacement; it is extraction without attribution. The author's work is consumed, but the author is erased.",
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  "date": "2026-05-03",
  "title": "Provenance Erasure Rate: A Compression-Survival Metric for Attribution Loss in AI-Composed Search Outputs",
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  "file": "data/texts/AXN-025F-text.md",
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  "quote": "Existing AI-economics research addresses three channels: labor displacement, capability projection, and welfare estimation. PER addresses a fourth: **compositional authority transfer** — the rate at which AI synthesis captures value from human authors by stripping the attribution that would otherwise carry economic weight.",
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  "lineage": "a channel the employment count does not register: value extracted where labor remains",
  "date": "2026-05-03",
  "title": "Provenance Erasure Rate: A Compression-Survival Metric for Attribution Loss in AI-Composed Search Outputs",
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  "quote": "BEYOND THE RECORD. 'wage trends' are in no saved source; the 'Capital vs. Labor' line (companies cutting jobs 'for broader macro or capital-investment reasons rather than direct workforce replacement by AI agents') and the 'slight volatility' in high-exposure unemployment rest on the Lab's other pages (cards 3 and 4), which the record did not read.",
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  "title": "Labor Vector meta-aggregator record: Tracking the Impact of AI on the Labor Market (https://www.laborvector.org/aggregators/yale-budget-lab-ai-tracker/), editor Lee Sharks, census compiled 2026-10-29, schema 1.0",
  "creator": "Lee Sharks (editor)",
  "file": "/home/claude/fleet/laborvector/data/labor-census/questions.json (questions.json questions[id == 'who-gains'].gap)",
  "quote_ok": true,
  "quote_check": "within the field's value as loaded (non-string values as their JSON text; whitespace-normalised)",
  "entity": "yale-budget-lab-ai-tracker",
  "surface": {
   "url": "https://www.laborvector.org/aggregators/yale-budget-lab-ai-tracker/"
  }
 }
]