{
 "row": "anthropic-economic-index",
 "address": "Anthropic Economic Index",
 "entity": "The Anthropic Economic Index (anthropic.com/economic-index), the report series and data releases of Anthropic's Economics team, from the initial release of 2025-02-10 to the sixth report of 2026-06-26 (datasets on Hugging Face, Anthropic/EconomicIndex; the third report also arXiv:2511.15080, the second arXiv:2503.04761), which classifies sampled Claude conversations and first-party API transcripts, by Claude, to O*NET tasks, collaboration modes and economic primitives, and since April 2026 surveys Claude users linked to their sessions; a platform-specific observation of Anthropic's own product; formalized in the Labor Vector meta-aggregator record of 2026-10-29 (https://www.laborvector.org/aggregators/anthropic-economic-index/)",
 "procedure": "EA-NEGONT-02 v0.7 §3.4 (D/R/O), working; not frozen",
 "config": {
  "row": "anthropic-economic-index",
  "address": "Anthropic Economic Index",
  "entity": "The Anthropic Economic Index (anthropic.com/economic-index), the report series and data releases of Anthropic's Economics team, from the initial release of 2025-02-10 to the sixth report of 2026-06-26 (datasets on Hugging Face, Anthropic/EconomicIndex; the third report also arXiv:2511.15080, the second arXiv:2503.04761), which classifies sampled Claude conversations and first-party API transcripts, by Claude, to O*NET tasks, collaboration modes and economic primitives, and since April 2026 surveys Claude users linked to their sessions; a platform-specific observation of Anthropic's own product; formalized in the Labor Vector meta-aggregator record of 2026-10-29 (https://www.laborvector.org/aggregators/anthropic-economic-index/)",
  "row_type": "C (a public entity on which the archive bears: a provider's report series and dataset of its own product's usage; an entity of the Labor Vector meta-aggregator)",
  "aliases": [
   {
    "pattern": "\\bAnthropic Economic Index\\b",
    "flags": "i"
   },
   {
    "pattern": "\\bAnthropic/EconomicIndex\\b",
    "flags": ""
   },
   {
    "pattern": "\\bEconomic Index (report|Survey)\\b",
    "flags": "i"
   },
   {
    "pattern": "\\bAnthropic AI Usage Index\\b",
    "flags": "i"
   },
   {
    "pattern": "\\b2511\\.15080\\b",
    "flags": ""
   },
   {
    "pattern": "\\b2503\\.04761\\b",
    "flags": ""
   }
  ],
  "classes": [
   {
    "term": "labor performed with AI: delegation, collaboration mode and task completion",
    "pattern": "\\b(augment\\w* (vs\\.?|versus|and|or|against|over) automat\\w+|automat\\w+ (vs\\.?|versus|and|or|against|over) augment\\w+|task (success|completion)|collaboration modes?|(delegat\\w+|hand\\w*) (\\w+ ){0,4}to (Claude|the (model|system|assistant|AI)|AI)|labou?r (performed )?with (an )?AI)\\b",
    "flags": "i",
    "basis": "field: B11 (January 2026 report, Chapter 2: 'AI autonomy measures the degree to which users delegate decision-making to Claude'; 'Task success measures Claude’s assessment of whether Claude completes tasks successfully'); B12 (June 2026 report, footnote list of collaboration modes: 'Directive: Human delegates complete task execution to AI with minimal interaction'); B2 (arXiv:2511.15080 abstract: 'directive task delegation rising from 27% to 39%'); B13 (Stanford Canaries Dashboard: 'decomposes this usage into automation and augmentation patterns'). The census's own intersection for the entity, 'labor-with-ai' ('Labor performed with AI: assistance, delegation, task fidelity, correction burdens and user labor drag'), census.json intersections and rows[slug == 'anthropic-economic-index'].intersections."
   },
   {
    "term": "AI exposure of occupations and tasks",
    "pattern": "\\b((AI|occupational|job|task|observed|theoretical) exposure|exposure (to|of) (AI|LLMs?|large language models|generative AI))\\b",
    "flags": "i",
    "basis": "field: B12 (June 2026 report, 'AI and work tasks': 'we constructed a measure of observed exposure , which captures the share of occupational tasks we already see being done with Claude'); B13 (Canaries Dashboard: 'We match the occupation-level ADP data to various measures of exposure: Eloundou et al. (2024) and the Anthropic Economic Index'). The census's evidence_kind for the entity, 'exposure estimate' (census.json rows[slug == 'anthropic-economic-index'].evidence_kind)."
   },
   {
    "term": "a provider's interaction data on its own product",
    "pattern": "\\b((usage|interaction|conversation) (data|logs?|telemetry)|(Claude|ChatGPT|Copilot|Gemini) (usage|conversations|transcripts))\\b",
    "flags": "i",
    "basis": "field: B11 ('we analyze a random sample of 1M conversations from Claude.ai Free, Pro and Max conversations … and 1M transcripts from our first-party (1P) API traffic'); B12 ('Claude’s impact on the economy will be visible in economic aggregates like employment and productivity as much as its usage logs'); B2 abstract ('we document patterns of Claude usage over time'). The census's evidence_kind 'interaction data' and its first negative tag, 'a platform-specific observation of Anthropic's own product, Claude' (census.json rows[slug == 'anthropic-economic-index'].evidence_kind, .negative_tags[0])."
   },
   {
    "term": "productivity gains from AI and who receives them",
    "pattern": "\\b(productivity (gains?|growth|effects?|boosts?)|(captur\\w+|receiv\\w+|shar\\w+) (\\w+ ){0,2}(the )?(economic )?gains)\\b",
    "flags": "i",
    "basis": "field: B11 (Chapter 4: 'Adjusting productivity estimates for task reliability roughly halves the implied gains, from 1.8 to about 1.0 percentage points of annual labor productivity growth over the next decade'); B12 ('large majorities of people report productivity gains'; 'the hope that the economic gains from AI will be widely shared'). The census's intersection 'gains-and-losses' and its direction.gains for the entity ('not recorded as a distribution'), census.json rows[slug == 'anthropic-economic-index'].intersections, .direction.gains."
   },
   {
    "term": "a model's assessment of its own output (task success as the model assesses it)",
    "pattern": "\\b(self-assess\\w*|self-evaluat\\w*|self-audit\\w*|(assess|scor|evaluat|grad)\\w* (itself|its own|their own)( output)?)\\b",
    "flags": "i",
    "basis": "field: B11 (January 2026 report, Chapter 2: 'Task success measures Claude’s assessment of whether Claude completes tasks successfully'; Introduction: the primitives 'which we generate by asking Claude specific questions about anonymized Claude.ai and first-party (1P) API transcripts'); B12 (June 2026 report, footnote 02: 'transcripts are only read by another instance of Claude'). The census's negative tags for the entity: 'task success is Claude's assessment of whether Claude completes tasks' and 'NOT independent of the provider: the transcripts are classified by another instance of Claude' (census.json rows[slug == 'anthropic-economic-index'].negative_tags[2], [4]), and its stated_limits[0] 'task success is Claude's own assessment'."
   }
  ],
  "classes_note": "Five classes, each the census's own intersection, evidence kind, negative tag or stated limit for the entity, stated by field sources at the loci given: (1) labor performed with AI, delegation, collaboration mode and task completion (intersection 'labor-with-ai'; B11 Chapter 2, B12 footnotes, B2 abstract, B13); (2) AI exposure of occupations and tasks (evidence_kind 'exposure estimate'; B12, B13); (3) a provider's interaction data on its own product (evidence_kind 'interaction data', negative_tags[0]; B11, B12, B2); (4) productivity gains and who receives them (intersection 'gains-and-losses', direction.gains; B11 Chapter 4, B12); (5) a model's assessment of its own output, task success as the model assesses it (negative_tags[2] and [4], stated_limits[0]; B11 Chapter 2 and Introduction, B12 footnote 02). The archive's instruments of labor direction bear on class (1): Directionality of Semantic Labor (#771, with its definition surface #1392), its directionality stack DS-6 and its operators task-origin and User Labor Drag (#771 §§ abstract, 8), where they measure whether a synthetic system's labor advanced the task its user commissioned; the census's lv_intersection names that meeting as the archive's reading. The archive's self-audit work bears on class (5): the Self-Audit Module for public summarizers (#780, v3 #156) and its anti-self-flattery principle, where it states what a model's score of its own output returns without ground truth it does not control. The semantic-labor line bears on class (4) where a deposit states who captures the value of labor performed with or for a model. The aliases are the entity's names in the field and the census (Anthropic Economic Index; Economic Index report / Survey; Anthropic AI Usage Index; the Hugging Face dataset id; the arXiv numbers of the second and third reports). 'AEI' is not an alias (ambiguous). 'Clio' is Anthropic's privacy-preserving analysis system, a separate entity the reports use. Admission by reading (§3.4): D where a deposit names the entity and says something of it; O-class where a sentence applies an archive category to a class above; a bare mention, a list or a reference line is not a claim.",
  "exclude_deposits": [],
  "notes": "entity-keyed pass, 2026-10-29, on the brief rebuild/negative-of-the-negative/v09/BRIEF-NEW-ROW-AGGREGATOR.md with the addenda BRIEF-NEW-ROW-PROVENANCE.md and BRIEF-NEW-ROW-LABOR.md; address 'Anthropic Economic Index' (anthropic-economic-index-aio-20261029, OBS-e148b3285858, ADDR-166e7e31c8af), Google AI Overview (expanded), signed out, incognito. The archive's own records of the entity (the Capture Registry's findings and analysis at the capture, the Labor Vector census record) are ledgered beside the deposits as CAP-1-NN and LV-NN. Second pass: one citation hop forward and back from the deposits admitted by reading (#95, #124, #138, #142, #771, #777, #780, #788, #855, #1556, #1634).",
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   95,
   124,
   138,
   142,
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   855,
   1556,
   1634
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 "note": "Candidates by string only. Admission by reading, recorded in the row's reading file; nothing here is admitted."
}