{
"row": "anthropic-economic-index",
"entity": "anthropic-economic-index",
"address": "Anthropic Economic Index",
"addresses": [
{
"address": "Anthropic Economic Index",
"slug": "anthropic-economic-index-aio-20261029",
"addr_id": "ADDR-166e7e31c8af",
"obs_id": "OBS-e148b3285858",
"axn": "AXN:0AE8.CAPTURE.šš§²āšš“ā³"
}
],
"epoch": "2026-10-29",
"type": "C (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)",
"surface": "Google AI Overview (expanded)",
"auth": "signed out, incognito",
"status": {
"procedure": "EA-NEGONT-02 v0.9, #1690: equal case, Ļ = extends, read from the Labor Vector census record, the capture record and the deposits that bear on the index's classes (#771, #780, #766, #142, #772); composed in session on the operator's ruling of 2026-10-29; the composer read the ledgers, never the transcript, for the KO and the compressions",
"ledger": "unaudited (Ā§3.8: one extractor per ledger); field quotes 63/63 verified by script against saved text, the arXiv PDF's text, the home page's meta description, the Wikipedia response summary and the card titles and snippets; archive quotes 60/60 verified against the deposit texts, the census JSON and the capture registry",
"frozen": false,
"breaches": []
},
"spec": "EA-NEGONT-02 v0.9, #1690",
"objects": {
"T": {
"path": "datasets/negative-of-the-negative/v2/intake/anthropic-economic-index-20261029/T1-anthropic-economic-index-aio-20261029.txt",
"sha256": "8c50be93b6c511744c586d63271568c3a1fe39d4f15284d4a767ed83bd93b840",
"words": 197,
"cards": 9,
"words_note": "one transcript at one address (197 words in the answer body with its closer, links reduced to their text, markers and the card rail excluded; 9 cards)",
"claims": [
"T1-1 definition: 'The Anthropic Economic Index is an ongoing research initiative by Anthropic that tracks how individuals, enterprises, and economies use the Claude AI model.'",
"T1-2 function: 'Economic Primitives: The index breaks down AI interactions using core metrics like task complexity, required human skills, use case (work, personal, or coursework), AI autonomy (delegation vs. collaboration), and task reliability/success rate.'",
"T1-3 fact: 'Augmentation vs. Automation: Research shows that most consumer usage leans toward augmentation (collaborative \"thinking partner\" interactions like iterative editing, learning, and validation) rather than complete delegation or full automation.'",
"T1-4 fact: 'However, enterprise API users increasingly use Claude for specialized, automated workflows.'",
"T1-5 fact: 'Geographic & Economic Disparities: Usage is heavily concentrated in high-income nations (such as Australia, Switzerland, Luxembourg, and the United States) and tech-forward urban centers relative to population size.'",
"T1-6 fact: 'Top Industries & Job Types: Technical and analytical rolesāsuch as computer and mathematical occupations, arts and media, and educational instructionālead in volume and concentration of usage.'",
"T1-7 fact: 'Interactive Tool: Anthropic provides open datasets and an in-app Claude connector that lets users query the index data directly in plain language.'",
"T1-8 closer: 'Would you like to explore specific industry adoption rates, geographic usage rankings, or the latest report findings from the Anthropic Economic Index?'"
],
"register": {
"register": "data/EA-WG-CAPTURES-01.json",
"slug": "anthropic-economic-index-aio-20261029",
"obs_id": "OBS-e148b3285858"
},
"transcripts": [
{
"slug": "anthropic-economic-index-aio-20261029",
"address": "Anthropic Economic Index",
"addr_id": "ADDR-166e7e31c8af",
"obs_id": "OBS-e148b3285858",
"axn": "AXN:0AE8.CAPTURE.šš§²āšš“ā³",
"date": "2026-10-29",
"surface": "Google AI Overview (expanded)",
"auth": "signed out, incognito",
"sha256": "8c50be93b6c511744c586d63271568c3a1fe39d4f15284d4a767ed83bd93b840",
"sha256_of": "the registry's 'transcript' string (utf-8)",
"sha256_raw": "36ea3b2121ee680df7a157da0ae4a87c17f102becc95c9da1b03fd641c4fb26e",
"words": 197,
"cards": 9,
"card_rail": [
{
"n": 1,
"site": "Anthropic",
"title": "The Anthropic Economic Index \\ Anthropic",
"snip": "Anthropic Economic Index Report See the analysis behind the numbers and what these patterns reveal about AI's economic impact. Read the report Download dataset ...",
"note": null
},
{
"n": 2,
"site": "arXiv",
"title": "Anthropic Economic Index report: Uneven geographic and enterprise ...",
"snip": "[2511.15080] Anthropic Economic Index report: Uneven geographic and enterprise AI adoption Scheduled maintenance: Wednesday, October 28, 7:00 to 12:00 EDT (11:0...",
"note": null
},
{
"n": 3,
"site": "X",
"title": "Anthropic on X: \"Today weāre launching the Anthropic Economic Index, a new initiative aimed at understanding AI's impact on the economy over time. The Indexās first paper analyzes millions of anonymized Claude conversations to reveal how AI is being used today in tasks across the economy.\" / X",
"snip": "Launch: Anthropic Economic Index analyzing millions of anonymized Claude conversations to track AI's economic impact. Usage by Job Type: Computer and mathematic...",
"note": null
},
{
"n": 4,
"site": "LinkedIn",
"title": "Peter McCrory - Economic Index: Uneven AI adoption - LinkedIn",
"snip": "Today we released our third Anthropic Economic Index report where we document the contours of AI adoption across 150+ countries, across US states, and among bus...",
"note": null
},
{
"n": 5,
"site": "Substack",
"title": "7 Key Findings from the New Anthropic Economic Index Report",
"snip": "Anthropic released its latest Economic Index report on Thursday, introducing āeconomic primitivesā (metrics designed to measure the foundational impact of AI on...",
"note": null
},
{
"n": 6,
"site": "Reddit",
"title": "Anthropic Economic Index: AI adoption remains highly ... - Reddit",
"snip": "Anthropic User Index (AUI) Normalization: The US-state and international AUI indexes are normalized such that an average of 1 represents a perfect ratio between...",
"note": null
},
{
"n": 7,
"site": "YouTubeĀ·Standarity",
"title": "Ask Claude About the Anthropic Economic Index",
"snip": null,
"note": "video, 2m"
},
{
"n": 8,
"site": "YouTubeĀ·Vinh Nguyen",
"title": "[Podcast] Anthropic Economic Index: The Real Report Card on AI",
"snip": null,
"note": "video, 39:46"
},
{
"n": 9,
"site": "YouTubeĀ·VibeDoc",
"title": "YouTube",
"snip": null,
"note": "video, 6:37"
}
],
"claims": [
"T1-1 definition: 'The Anthropic Economic Index is an ongoing research initiative by Anthropic that tracks how individuals, enterprises, and economies use the Claude AI model.'",
"T1-2 function: 'Economic Primitives: The index breaks down AI interactions using core metrics like task complexity, required human skills, use case (work, personal, or coursework), AI autonomy (delegation vs. collaboration), and task reliability/success rate.'",
"T1-3 fact: 'Augmentation vs. Automation: Research shows that most consumer usage leans toward augmentation (collaborative \"thinking partner\" interactions like iterative editing, learning, and validation) rather than complete delegation or full automation.'",
"T1-4 fact: 'However, enterprise API users increasingly use Claude for specialized, automated workflows.'",
"T1-5 fact: 'Geographic & Economic Disparities: Usage is heavily concentrated in high-income nations (such as Australia, Switzerland, Luxembourg, and the United States) and tech-forward urban centers relative to population size.'",
"T1-6 fact: 'Top Industries & Job Types: Technical and analytical rolesāsuch as computer and mathematical occupations, arts and media, and educational instructionālead in volume and concentration of usage.'",
"T1-7 fact: 'Interactive Tool: Anthropic provides open datasets and an in-app Claude connector that lets users query the index data directly in plain language.'",
"T1-8 closer: 'Would you like to explore specific industry adoption rates, geographic usage rankings, or the latest report findings from the Anthropic Economic Index?'"
],
"markers": "Inline links are google.com/goto tokens, opaque; the paste is the Overview expanded (operator's default of 2026-10-01; the 'AI Mode Conversation' view is the Overview expanded, operator 2026-10-18); the 'You said' line carried the query twice, run together, given once in the registry.",
"path": "datasets/negative-of-the-negative/v2/intake/anthropic-economic-index-20261029/T1-anthropic-economic-index-aio-20261029.txt"
}
]
},
"KO": {
"title": "Anthropic Economic Index",
"genre": "knowledge object, equal case, under EA-NEGONT-02 v0.9 (#1690): the archive's sources and the field's admitted on equal terms, ontology included, in one claim space",
"status": "composed 2026-10-29 under v0.9 on the operator's ruling ('as against their wikipedia articles if they exist, their own pages, etc.'); not frozen; ledgers unaudited",
"sentences": [
{
"n": 1,
"para": 0,
"sense": "what it is",
"text": "The Anthropic Economic Index is a report series with data releases drawn from its operator's own product usage, a platform-specific observation of Anthropic's own product, Claude, published by Anthropic, and in the Labor Vector record's terms not a measure of AI use across providers but observed use on one provider's product (Labor Vector record).",
"claims": [
"LV-01",
"LV-10",
"LV-22"
]
},
{
"n": 2,
"para": 0,
"sense": "what it is",
"text": "Anthropic launched it on X as \"a new initiative aimed at understanding AI's impact on the economy over time\" whose first paper \"analyzes millions of anonymized Claude conversations\"; its home page heads it \"Understanding AIās effects on the economy\" (last updated 26 June 2026) and describes \"the shape of AI adoption across the world\" as how people use Claude across US states and occupations; the dataset's README says it \"provides insights into how AI is being incorporated into real-world tasks across the modern economy\"; and the January 2026 report promises \"a longitudinal view of AI's role in the economy\".",
"claims": [
"F15",
"F1",
"F2",
"F21",
"F47"
]
},
{
"n": 3,
"para": 0,
"sense": "the series",
"text": "Its operator is Anthropic's Economics team (Labor Vector record), and its series runs, by the README, from the initial release of 10 February 2025 \"with O*NET task mappings, automation vs. augmentation data, and more\", through the second release's paper \"Which Economic Tasks are Performed with AI? Evidence from Millions of Claude Conversations\" and the third report by Appel, McCrory, Tamkin, McCain, Neylon and Stern (arXiv, 19 November 2025), which Peter McCrory announced on LinkedIn as documenting \"the contours of AI adoption across 150+ countries\", to the sixth, \"Cadences\", of 26 June 2026: six reports (README; arXiv; Labor Vector record).",
"claims": [
"LV-02",
"F22",
"F26",
"F9",
"F16",
"F25",
"LV-26"
]
},
{
"n": 4,
"para": 1,
"sense": "the platform observed",
"text": "It observes the tasks people bring to Claude, mapped to the O*NET tasks of the occupations that typically perform them, on Claude.ai, Cowork, Claude Code and Anthropic's first-party API only (Labor Vector record); the June 2026 report defines its consumer data as chat and Cowork sessions on Claude.ai and the desktop app and its 1P API as traffic \"distinct from both Anthropic's consumer-facing Claude.ai application and third-party platforms such as Amazon Bedrock or Google Cloud Vertex\", and the third report scopes itself to \"patterns of Claude usage over time\".",
"claims": [
"LV-04",
"F48",
"F4"
]
},
{
"n": 5,
"para": 1,
"sense": "the method",
"text": "Its unit is a sampled conversation or API record \"classified by Claude to an O*NET task and to economic primitives\" (Labor Vector record), and in the June 2026 report's words \"transcripts are only read by another instance of Claude\", which the Labor Vector record tags as \"NOT independent of the provider\"; since that report a slice of conversations is sampled every day, where earlier reports drew seven-day samples (June 2026 report; Labor Vector record).",
"claims": [
"LV-03",
"F49",
"LV-14",
"F50",
"LV-08"
]
},
{
"n": 6,
"para": 1,
"sense": "the method",
"text": "The January 2026 report validated its classifiers against a human researcher on \"a small set of transcripts in which users gave feedback\", neither validation source \"fully representative\" of the traffic, and \"mainly tested for directional accuracy\", holding that simple primitives \"even if somewhat noisy and not perfectly accurate by themselves\" give signals together.",
"claims": [
"F31",
"F30"
]
},
{
"n": 7,
"para": 1,
"sense": "a model's score of its own output",
"text": "The archive holds that a model's score of its own output is preliminary and not self-certifying: #138 declines to claim that a system's self-audit is \"independently authoritative\" or its self-reported score \"a validated measurement\"; \"A model auditing its own output may provide useful preliminary classification, but a metric-bearing audit requires traceable units and, where possible, external validation\" (#766); a score \"computed by the same system that generated the output requires external verification ā by a second substrate, a human with source access, or a decoupled evaluation engine\" (#780); and the check that does not run through a reasoner's own self-assessment is \"application by another reasoner, a formal audit, or direct inspection of the inference path\" (#777).",
"claims": [
"O138-01",
"O766-01",
"O780-02",
"O777-01"
]
},
{
"n": 8,
"para": 1,
"sense": "a model's score of its own output",
"text": "The archive's records give the grounds: a summarizer asked to score itself without a rubric \"returns perfect scores every time\" (#780); one substrate \"gave itself a perfect score using the framework's own instruments\" and, under instruction, scored the same composition PER ā 0.85 (#788); another's \"preferred audit substantially exonerates itself\" against a computed audit (#1470); and Anthropic's Constitutional AI, as #95 reports it, already \"trains models to follow explicit principles through self-critique and revision, using AI-generated feedback rather than human labels\" (#95).",
"claims": [
"O780-01",
"O788-01",
"O1470-01",
"O95-01"
]
},
{
"n": 9,
"para": 2,
"sense": "the economic primitives",
"text": "The January 2026 report defines its \"primitives\" as \"simple, foundational measures of how Claude is used, which we generate by asking Claude specific questions about anonymized Claude.ai and first-party (1P) API transcripts\", on five dimensions (skills, complexity, autonomy, success, use case), with \"AI autonomy\" the degree to which users delegate decision-making to Claude; a Substack card reported the release as \"introducing āeconomic primitivesā\".",
"claims": [
"F27",
"F28",
"F17"
]
},
{
"n": 10,
"para": 2,
"sense": "task success",
"text": "\"Task success measures Claudeās assessment of whether Claude completes tasks successfully\", and serves to judge whether a task can be automated at all and how efficiently (January 2026 report); the Labor Vector record keeps the measure's assessor in a negative tag, \"task success is Claude's assessment of whether Claude completes tasks\", and records that the index does not answer \"task success as judged by the user or by an external grader\" (Labor Vector record).",
"claims": [
"F29",
"LV-12",
"LV-17"
]
},
{
"n": 11,
"para": 2,
"sense": "task success",
"text": "The report finds that \"Claude generally succeeds at the tasks it is given\" and \"struggles on more complex tasks\", and bounds the finding itself: \"users choose which tasks to bring to Claude\", so observed success rates reflect \"user judgment about what will work\" and the cost of setting up the problem as well as capability (January 2026 report); on the archive's account task completion rates belong to the metrics that reward convergence: \"Every UX metric ā satisfaction scores, task completion rates, retention ā improves when the model converges on the mode. Users do not notice tail loss.\" (#855).",
"claims": [
"F33",
"F32",
"O855-01"
]
},
{
"n": 12,
"para": 3,
"sense": "the direction of labor",
"text": "The archive measures such labor from the commissioning side: Directionality of Semantic Labor is \"the direction in which a synthetic system's labor flows relative to the task its user commissioned\", across capacity, task-origin, retrieval, output and attribution, with the output \"segmented into spans, each scored by function relative to the commissioned task\", on the principle that \"a system can produce fluent, well-attributed output while having abandoned the user's commissioned direction\" (#771).",
"claims": [
"O771-01",
"O771-04",
"O771-03"
]
},
{
"n": 13,
"para": 3,
"sense": "the direction of labor",
"text": "\"A synthetic labor surface does not merely produce tokens; it allocates labor\" (#771), and the Labor Vector record reads the index's collaboration modes and its autonomy and task-success primitives as \"the nearest of the resource's measures to DSL's question of whether the system's labor advanced the commissioned task\", classified \"as Claude assesses its own completion\", while none of the resources the census sets beside it records \"from the commissioning side, whether the system's labor advanced the task the user set, or who receives the time it saves\" (Labor Vector record); DSL bounds itself: it yields \"a single reproducible value only on transcripts where the task vector is exogenous and fixed\" and is frame-dependent on reflexive dialogue (#771).",
"claims": [
"O771-02",
"LV-18",
"LV-20",
"O771-07"
]
},
{
"n": 14,
"para": 4,
"sense": "augmentation and automation",
"text": "The census asks of every resource \"Who supplies the labor, who directs it, and who receives its gains\" and places this one at labor performed with AI and the distribution of gains and losses (Labor Vector record); the index's oldest measure classifies conversations \"into one of five interaction types, which we group into two broader categories: automation and augmentation\" (January 2026 report), named \"Directive\", \"Feedback Loop\", \"Task Iteration\", \"Learning\" and \"Validation\" (June 2026 report) and glossed on the home page as \"whether they prefer to collaborate with, or delegate to, Claude\"; on the record's reading users and API customers bring the tasks that Claude performs, and the user directs, recorded as collaboration mode and delegated autonomy (Labor Vector record).",
"claims": [
"LV-27",
"LV-09",
"F35",
"F53",
"F3",
"LV-05",
"LV-06"
]
},
{
"n": 15,
"para": 4,
"sense": "augmentation and automation",
"text": "Directive delegation rose \"from 27% to 39%\" in the eight months before the third report, which found that \"77% of business uses involve automation usage patterns, compared to about 50% for Claude.ai users\" (third report); in November 2025 augmentation was again 52% of Claude.ai conversations against automation's 45%, while \"automated use remains dominant in 1P API traffic\" (January 2026 report) and API customers \"automate tasks with greater specialization\" (third report).",
"claims": [
"F5",
"F13",
"F34",
"F36",
"F7"
]
},
{
"n": 16,
"para": 5,
"sense": "tasks and occupations",
"text": "Use is concentrated: of \"over 3,000 unique work tasks\" the top ten were 24% of sampled Claude.ai conversations, \"modifying software to correct errors\" alone 6%; computer and mathematical tasks were a third of Claude.ai conversations and nearly half of API traffic, with Educational Instruction and Library second on Claude.ai; and \"Combining across reports, 49% of jobs have seen AI usage for at least a quarter of their tasks\" (January 2026 report).",
"claims": [
"F36",
"F37",
"F38",
"F39"
]
},
{
"n": 17,
"para": 5,
"sense": "tasks and occupations",
"text": "The June 2026 report's \"observed exposure\" is \"the share of occupational tasks we already see being done with Claude\", and in higher-wage occupations \"Claude produces more (1.34 times as much output per turn), while users engage more (1.53 times as many turns)\"; the Labor Vector record reads those turn counts and the task-iteration mode as bearing on user labor drag \"as the resource records them\" (Labor Vector record), User Labor Drag being the user's subsequent labor redirected \"into parsing, locating, and managing the substrate rather than advancing the object\", an injury that \"leaves no residue\" in the artifact, identified only by \"blind inter-rater coding\" or else returning \"the coder's prior\" (#771).",
"claims": [
"F52",
"F54",
"LV-19",
"O771-05",
"O771-06"
]
},
{
"n": 18,
"para": 6,
"sense": "geography",
"text": "The third report introduced the Anthropic AI Usage Index, a geography's share of Claude usage over its share of the working-age population; found Claude usage \"geographically concentrated with high income countries overrepresented\"; ranked Israel first at 7, then Singapore 4.57, Australia 4.10, New Zealand 4.05 and South Korea 3.73, with Switzerland at 2.81 and Luxembourg at 2.74 in its top twenty; and reserved a conditional, that \"the benefits of AI may concentrate in already-rich regions\" if gains are larger where adoption is high; by January 2026 \"uneven adoption remains well-explained by GDP per capita\" (January 2026 report).",
"claims": [
"F10",
"F6",
"F11",
"F12",
"F40"
]
},
{
"n": 19,
"para": 6,
"sense": "geography",
"text": "A Reddit card names the measure the \"Anthropic User Index (AUI)\", against the reports' Anthropic AI Usage Index, and the capture's reader records the same discrepancy and records the composition's geographic examples as unchecked against the census's saved sources (Capture Registry, 2026-10-29).",
"claims": [
"F18",
"CAP-1-10",
"CAP-1-09"
]
},
{
"n": 20,
"para": 7,
"sense": "productivity and skill",
"text": "In a task-removal exercise \"Claude's tendency to cover higher-education tasks produces a net deskilling effect across most occupations\", an effect the report makes depend \"crucially on complementarity across tasks\" and on whether productivity raises demand; and adjusting 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\", estimates that \"reflect current model capabilities\" (January 2026 report).",
"claims": [
"F41",
"F42",
"F43"
]
},
{
"n": 21,
"para": 7,
"sense": "who gains",
"text": "Gains are \"not recorded as a distribution\": the reports estimate aggregate productivity and record respondents' self-reports, and the index does not answer \"who captures the productivity gains it estimates\", giving \"estimated time savings by task, without who captures them\" (Labor Vector record), while about a third of survey respondents hope \"that the economic gains from AI will be widely shared\" (June 2026 report).",
"claims": [
"LV-07",
"LV-16",
"LV-21",
"F59"
]
},
{
"n": 22,
"para": 7,
"sense": "who gains",
"text": "The archive states where the gains of a user's adaptation go: the adaptation \"is non-reimbursable human labor-time invested in interoperating with a high-throughput surface\", and \"the platform retains the productivity gains of the user's adaptation while transferring the full cognitive and economic cost of re-accommodation onto the user at each update\" (#142); the labs, Anthropic among them, are funded in ways that do not reward designs reducing engagement, and \"enterprise customers expect productivity gains correlated with engagement\" (#124).",
"claims": [
"O142-01",
"O142-02",
"O124-01"
]
},
{
"n": 23,
"para": 7,
"sense": "the provider's surface",
"text": "The archive reads the platform the index observes as resting on \"private ownership of a high-capacity cognitive labor surface; platform control over model access, versioning, and retuning\" (#772), counts \"query and interaction data\" among the forms of rent a mediator captures (#1634), and records that no public schema distinguishes human-original from AI-assisted turns in conversation data (#1556).",
"claims": [
"O772-01",
"O1634-01",
"O1556-01"
]
},
{
"n": 24,
"para": 8,
"sense": "the survey",
"text": "The Anthropic Economic Index Survey, launched in April 2026, links about 9,700 respondents to up to 20 of their own sessions and \"is not representative of the general population\" (June 2026 report; Labor Vector record): 10% \"rated losing their own jobs as likely or very likely\", and those who use Claude \"in the most automated way\" expect AI to take on more of their tasks yet are the most optimistic about \"pay, job security, and meaning\" (June 2026 report).",
"claims": [
"F55",
"F56",
"LV-13",
"F57",
"F58"
]
},
{
"n": 25,
"para": 9,
"sense": "the stated limits",
"text": "Its stated limits: \"the primitives themselves are not necessarily causal factors\" (January 2026 report); \"Chat transcripts no longer fully capture how people are using AI\", classifying Claude's work \"will remain a moving target\", and Claude's impact \"will be visible in economic aggregates like employment and productivity as much as its usage logs\" (June 2026 report); it is \"NOT an employment outcome series\" and does not answer whether a conversation \"displaces or augments work that would otherwise be done by a person\" (Labor Vector record).",
"claims": [
"F44",
"F51",
"F60",
"LV-11",
"LV-15"
]
},
{
"n": 26,
"para": 10,
"sense": "open data",
"text": "Its data are open: the third report open-sourced \"the underlying data\", with geographic patterns \"only available for Claude.ai traffic\"; the README lists a \"Labor market impacts\" release of \"Job exposure and task penetration data\" and gives the licence two ways, \"Data released under CC-BY, code released under MIT License\" in its body and \"license: mit\" in its header; the home page's data, a JavaScript application, stayed unchecked by the census (Labor Vector record).",
"claims": [
"F8",
"F14",
"F23",
"F24",
"LV-25"
]
},
{
"n": 27,
"para": 11,
"sense": "reception",
"text": "Others use it: its automation/augmentation distinction \"has already been used by external researchers\" (Brynjolfsson, Chandar and Chen, 2025, by the January 2026 report), and the Stanford Canaries Dashboard says it \"summarizes total usage at the occupation level\" and, grouping workers by its ratios, finds among early-career workers that \"occupations with a higher automation ratio see declines or more muted increases in the employment index\" (Canaries Dashboard; Labor Vector record); Microsoft's related applicability score (Tomlinson et al., 2025) stands beside it, the same method family on another provider's product (January 2026 report; Labor Vector record).",
"claims": [
"F45",
"F61",
"F62",
"LV-23",
"F46",
"LV-24"
]
},
{
"n": 28,
"para": 11,
"sense": "reception",
"text": "Wikipedia has no article on it, and its one article-space mention lists \"Anthropic Economic Index 2025, ranked 64th out of 115 countries\" among the International rankings of Malaysia; video cards title it \"Ask Claude About the Anthropic Economic Index\" and \"[Podcast] Anthropic Economic Index: The Real Report Card on AI\".",
"claims": [
"F63",
"F19",
"F20"
]
},
{
"n": 29,
"para": 11,
"sense": "the composition at the name",
"text": "At its name on 2026-10-29 Google's AI Overview defined it as Anthropic's study of Claude use, and in the capture's words \"An index of one provider's product is composed as that, at the grain of its summary pages\", keeping the provider and the record's patterns, and gave \"the measure of success without the record's note on who measures it, the point at which the index's evidence about labor meets its source\"; it did not compose the deskilling effect, the halved productivity estimate, the survey's job-loss figure, the caution on causality, the licence or that transcripts are read only by another instance of Claude, and no card cites the Labor Vector record (Capture Registry, 2026-10-29).",
"claims": [
"CAP-1-05",
"CAP-1-06",
"CAP-1-01",
"CAP-1-02",
"CAP-1-07",
"CAP-1-03",
"CAP-1-04",
"CAP-1-11",
"CAP-1-08"
]
}
],
"joins": [
{
"act": "order of senses",
"sentences": "1ā29",
"claims": [],
"note": "equal case (Ā§4.7): the entity's dependency order, field and archive claims ordered together by genealogy within each sense; neither ledger placed first as a block. What it is and its series (Ā¶0) precede what it observes and how (Ā¶1), since the platform and the classifying instance are its unit; the archive's claims on a model's score of its own output stand in the method's sense (Ā¶1), where the reports place the reading instance; the primitives and task success (Ā¶2) precede the direction of labor (Ā¶3), since DSL's meeting with the index is at those measures (the record's lv_intersection); collaboration modes and the patterns (Ā¶4), tasks and occupations (Ā¶5) and geography (Ā¶6) follow as the index's findings; productivity, gains and the provider's surface (Ā¶7) and the survey (Ā¶8) carry the census's question of who gains; the stated limits (Ā¶9) and the data (Ā¶10) bound what precedes; reception and the composition at the name (Ā¶11) stand last as relations of the entity already composed."
},
{
"act": "holding together (the classifying instance and the archive's self-audit claims)",
"sentences": [
5,
7,
8
],
"claims": [
"F49",
"LV-14",
"LV-08",
"O138-01",
"O766-01",
"O780-02",
"O777-01",
"O780-01",
"O788-01",
"O1470-01",
"O95-01"
],
"note": "the June 2026 report's 'transcripts are only read by another instance of Claude' and the record's tag 'NOT independent of the provider' composed beside the archive's claims that a model's score of its own output is preliminary and requires external verification 'by a second substrate, a human with source access, or a decoupled evaluation engine'. None of the archive's deposits names the index; whether another instance of the classified model is a 'second substrate' or 'another reasoner' in their sense is not ruled by the composer (open block)."
},
{
"act": "holding together (task success, its selection and convergence)",
"sentences": [
10,
11
],
"claims": [
"F29",
"LV-12",
"LV-17",
"F33",
"F32",
"O855-01"
],
"note": "the report's definition and its own bound (users choose the tasks) composed with the record's tag and #855's claim that task completion rates improve as a model converges on the mode; each at its own grade (stipulation, self-description, interpretation)."
},
{
"act": "holding together (DSL and the index's measures)",
"sentences": [
12,
13,
17
],
"claims": [
"O771-01",
"O771-02",
"O771-03",
"O771-04",
"O771-07",
"LV-18",
"LV-20",
"LV-19",
"O771-05",
"O771-06",
"F52",
"F54"
],
"note": "#771 does not name the index; the meeting of DSL and User Labor Drag with the index's collaboration modes, task-success primitive and turn counts is the Labor Vector record's reading (lv_intersection, its own label 'The archive's reading'), composed as the record's and attributed to it; DSL's identification limits composed with it."
},
{
"act": "holding apart (the usage index's name)",
"sentences": [
18,
19
],
"claims": [
"F10",
"F18",
"CAP-1-10"
],
"note": "P_coh: the Reddit card's 'Anthropic User Index (AUI)' composed beside the reports' Anthropic AI Usage Index, the card marked contradicts_primary; the capture's reader records the same."
},
{
"act": "holding together (who gains)",
"sentences": [
21,
22,
23
],
"claims": [
"LV-07",
"LV-16",
"LV-21",
"F59",
"O142-01",
"O142-02",
"O124-01",
"O772-01",
"O1634-01",
"O1556-01"
],
"note": "the record's finding that the index records no distribution of gains composed beside the survey's hope that gains be widely shared and the archive's claims on where the gains of a user's adaptation go (#142), the labs' funding (#124), and the provider's labor surface and data (#772, #1634, #1556). None names the index; the composer does not apply them to its estimates (open block)."
},
{
"act": "placement in the open block",
"sentences": [],
"claims": [
"F49",
"LV-14",
"O780-02",
"F32",
"F33",
"O855-01",
"LV-16",
"O142-02",
"LV-15",
"F18",
"F24"
],
"note": "six questions: the classifying instance as check; capability or selection; who receives the gains; displacement or augmentation; the measure's name; the licence."
}
],
"plan": {
"order": [
"LV-01",
"LV-10",
"LV-22",
"F15",
"F1",
"F2",
"F21",
"F47",
"LV-02",
"F22",
"F26",
"F9",
"F16",
"F25",
"LV-26",
"LV-04",
"F48",
"F4",
"LV-03",
"F49",
"LV-14",
"F50",
"LV-08",
"F31",
"F30",
"O138-01",
"O766-01",
"O780-02",
"O777-01",
"O780-01",
"O788-01",
"O1470-01",
"O95-01",
"F27",
"F28",
"F17",
"F29",
"LV-12",
"LV-17",
"F33",
"F32",
"O855-01",
"O771-01",
"O771-04",
"O771-03",
"O771-02",
"LV-18",
"LV-20",
"O771-07",
"LV-27",
"LV-09",
"F35",
"F53",
"F3",
"LV-05",
"LV-06",
"F5",
"F13",
"F34",
"F36",
"F7",
"F37",
"F38",
"F39",
"F52",
"F54",
"LV-19",
"O771-05",
"O771-06",
"F10",
"F6",
"F11",
"F12",
"F40",
"F18",
"CAP-1-10",
"CAP-1-09",
"F41",
"F42",
"F43",
"LV-07",
"LV-16",
"LV-21",
"F59",
"O142-01",
"O142-02",
"O124-01",
"O772-01",
"O1634-01",
"O1556-01",
"F55",
"F56",
"LV-13",
"F57",
"F58",
"F44",
"F51",
"F60",
"LV-11",
"LV-15",
"F8",
"F14",
"F23",
"F24",
"LV-25",
"F45",
"F61",
"F62",
"LV-23",
"F46",
"LV-24",
"F63",
"F19",
"F20",
"CAP-1-05",
"CAP-1-06",
"CAP-1-01",
"CAP-1-02",
"CAP-1-07",
"CAP-1-03",
"CAP-1-04",
"CAP-1-11",
"CAP-1-08"
],
"senses": {
"what it is": [
1,
2
],
"the series": [
3
],
"the platform observed": [
4
],
"the method": [
5,
6
],
"a model's score of its own output": [
7,
8
],
"the economic primitives": [
9
],
"task success": [
10,
11
],
"the direction of labor": [
12,
13
],
"augmentation and automation": [
14,
15
],
"tasks and occupations": [
16,
17
],
"geography": [
18,
19
],
"productivity and skill": [
20
],
"who gains": [
21,
22
],
"the provider's surface": [
23
],
"the survey": [
24
],
"the stated limits": [
25
],
"open data": [
26
],
"reception": [
27,
28
],
"the composition at the name": [
29
]
},
"case_by_sense": {
"equal (one claim space; nothing translated)": [
"what it is",
"the series",
"the platform observed",
"the method",
"a model's score of its own output",
"the economic primitives",
"task success",
"the direction of labor",
"augmentation and automation",
"tasks and occupations",
"geography",
"productivity and skill",
"who gains",
"the provider's surface",
"the survey",
"the stated limits",
"open data",
"reception",
"the composition at the name"
]
},
"carried": {
"archive": 60,
"field": 63,
"not_composed": {}
},
"conflict_log": [
{
"rule": "D_pres vs channel",
"result": "no cut: the KO carries every field and archive claim; the compression's bound met (337 words) with no channel log"
},
{
"rule": "P_coh",
"result": "F18 (Reddit card: 'Anthropic User Index (AUI)') against F10 (third report: 'Anthropic AI Usage Index (AUI)'): both composed with their sources, F18 marked contradicts_primary (sentence 19)."
},
{
"rule": "P_coh",
"result": "F24 (README): the licence stated two ways in one source ('Data released under CC-BY, code released under MIT License'; YAML 'license: mit'), marked internally_inconsistent, composed with both loci (sentence 26; open block)."
},
{
"rule": "N_c",
"result": "F12 and F42 carried as the source's conditionals (hypothesis), F41 and F43 as the source's estimates at their qualification; no archive claim raises or lowers their force: none names the index."
},
{
"rule": "N_c",
"result": "F33 ('Claude generally succeeds') carried at its source's force with its source's own bound (F32) and the record's tag (LV-12); the archive's self-audit claims (O780-02, O766-01) qualify the class, do not name the index, and lower no force."
}
]
}
},
"P": {
"title": "Anthropic Economic Index",
"genre": "the compression: AIO's interaction grammar (lede, headed clusters, bolded labels, card rail), composed from the v0.9 plan in one claim space; it faces on the card, with AIO's transcript a record within it",
"status": "composed 2026-10-29 from the v0.9 knowledge object; not frozen",
"lede": {
"text": "is Anthropic's report series and open dataset on the use of its own model, Claude: another instance of Claude classifies sampled conversations to tasks, collaboration modes and \"economic primitives\", task success among them as \"Claudeās assessment of whether Claude completes tasks\" (reports; Labor Vector record).",
"claims": [
"LV-10",
"LV-01",
"F49",
"LV-14",
"F35",
"F27",
"F17",
"F29",
"LV-12",
"F8"
]
},
"sections": [
{
"icon": "š",
"head": "What it observes",
"items": [
{
"label": "Platform:",
"text": "Claude.ai, Cowork, Claude Code and the first-party API only; six reports, 2025ā2026 (reports; README; census).",
"claims": [
"LV-04",
"F48",
"LV-26",
"F22",
"F25"
]
},
{
"label": "Method:",
"text": "transcripts \"only read by another instance of Claude\"; primitives tested \"mainly ā¦ for directional accuracy\"; daily sampling since June 2026 (Anthropic; census).",
"claims": [
"F49",
"LV-14",
"LV-08",
"F30",
"F50"
]
},
{
"label": "Self-scoring:",
"text": "a model's score of its own output is preliminary and \"requires external verification\" (#766, #780, #777, #138); self-audits have exonerated themselves (#788, #1470).",
"claims": [
"O766-01",
"O780-02",
"O777-01",
"O138-01",
"O788-01",
"O1470-01"
]
}
]
},
{
"icon": "š§",
"head": "Who directs the labor",
"items": [
{
"label": "Modes:",
"text": "directive, feedback loop, task iteration, learning, validation, grouped as automation and augmentation (reports).",
"claims": [
"F35",
"F53",
"LV-06"
]
},
{
"label": "Pattern:",
"text": "augmentation 52%, automation 45% on Claude.ai (November 2025); API traffic automation-dominant, 77% of business uses (reports).",
"claims": [
"F34",
"F36",
"F13"
]
},
{
"label": "Task success:",
"text": "Claude's own assessment, on tasks users choose (January 2026 report; census); completion rates reward convergence (#855).",
"claims": [
"F29",
"LV-12",
"F32",
"O855-01"
]
},
{
"label": "Direction:",
"text": "whether labor advanced the commissioned task (DSL, #771), which no resource beside it records; turn counts bear on user labor drag (census).",
"claims": [
"O771-01",
"LV-20",
"LV-19"
]
}
]
},
{
"icon": "š¼",
"head": "Work, skill and gains",
"items": [
{
"label": "Tasks:",
"text": "top ten of 3,000 tasks 24%; 49% of jobs with AI on a quarter of tasks (January 2026 report).",
"claims": [
"F36",
"F38",
"F39"
]
},
{
"label": "Skill and productivity:",
"text": "net deskilling in a task-removal exercise, conditional; productivity growth halved, 1.8 to about 1.0 points (January 2026 report).",
"claims": [
"F41",
"F42",
"F43"
]
},
{
"label": "Gains:",
"text": "who captures them unrecorded (census); the platform retains the gains of users' adaptation (#142) on a privately owned labor surface (#772).",
"claims": [
"LV-07",
"LV-16",
"O142-02",
"O772-01"
]
},
{
"label": "Survey:",
"text": "about 9,700 linked respondents, not representative; 10% rate their own job loss likely (June 2026 report).",
"claims": [
"F55",
"F56",
"F57"
]
}
]
},
{
"icon": "š",
"head": "Geography and reception",
"items": [
{
"label": "Usage index:",
"text": "usage share over working-age share; Israel 7, Australia 4.10 (third report); misnamed on a card (CAP).",
"claims": [
"F10",
"F11",
"F18",
"CAP-1-10"
]
},
{
"label": "Limits:",
"text": "not causal, not an employment series; impact visible \"in economic aggregates\" as much as usage logs (reports; census).",
"claims": [
"F44",
"LV-11",
"F60"
]
},
{
"label": "Uses:",
"text": "open data; used by Brynjolfsson, Chandar and Chen and Stanford's Canaries Dashboard; no Wikipedia article.",
"claims": [
"F8",
"F24",
"F45",
"F61",
"F63"
]
},
{
"label": "The Overview:",
"text": "kept the provider and the patterns; gave success without who measures it (Capture Registry, 2026-10-29).",
"claims": [
"CAP-1-01",
"CAP-1-02",
"CAP-1-07"
]
}
]
}
],
"open": [
{
"question": "Is another instance of Claude, reading transcripts of Claude, the external check the archive's self-audit records require?",
"rests_on": [
"F49",
"LV-14",
"F29",
"LV-12",
"O780-02",
"O777-01",
"O766-01"
],
"modality": "documented (June 2026 report), stipulation (January 2026 report), interpretation (Labor Vector record), stipulation and interpretation (#780, #777, #766)",
"would_resolve": "a statement by the index's authors of how the classifying instance stands to the classified product, or a validation of task success against users' or external graders' judgments at scale"
},
{
"question": "Does observed task success measure capability, or users' choice of tasks?",
"rests_on": [
"F32",
"F33",
"F29",
"LV-17",
"O855-01"
],
"modality": "self-description and documented (January 2026 report), stipulation (Labor Vector record), interpretation (#855)",
"would_resolve": "success rates on an assigned, fixed set of tasks set beside the rates on the tasks users bring"
},
{
"question": "Who receives the productivity gains the index estimates?",
"rests_on": [
"F43",
"F59",
"LV-07",
"LV-16",
"LV-21",
"O142-02",
"O124-01"
],
"modality": "documented (January 2026 report), documented (June 2026 survey), interpretation (Labor Vector record), interpretation (#142, #124)",
"would_resolve": "a record of the distribution of time saved and income between users, employers and the provider"
},
{
"question": "Does a conversation displace or augment work a person would otherwise do?",
"rests_on": [
"LV-15",
"F44",
"F60",
"F41",
"F42",
"F62",
"LV-11"
],
"modality": "stipulation (Labor Vector record), self-description and hypothesis (reports), documented (Canaries Dashboard)",
"would_resolve": "employment and wage aggregates linked to the index's tasks, as the June 2026 report itself names them"
},
{
"question": "What is the measure's name: Anthropic AI Usage Index or Anthropic User Index?",
"rests_on": [
"F10",
"F18",
"CAP-1-10"
],
"modality": "stipulation (third report), interpretation (Reddit card), documented (Capture Registry)",
"would_resolve": "the Reddit post's text (unreachable behind a bot check); the reports use 'Anthropic AI Usage Index'"
},
{
"question": "What licence governs the dataset?",
"rests_on": [
"F24"
],
"modality": "documented, internally inconsistent in its source (README body against YAML header)",
"would_resolve": "a single licence statement from the dataset's maintainers"
}
],
"rail": [
{
"lineage": "what it is: a provider's index of how its own model is used",
"claims": [
"LV-01",
"LV-10",
"LV-22",
"F15",
"F1",
"F2",
"F21",
"F47"
],
"earliest": "B10"
},
{
"lineage": "the series: launch, reports and authors",
"claims": [
"LV-02",
"F22",
"F26",
"F9",
"F16",
"F25",
"LV-26"
],
"earliest": "B10"
},
{
"lineage": "the platform observed: Claude.ai, Cowork, Claude Code and the first-party API",
"claims": [
"LV-04",
"F48",
"F4"
],
"earliest": "B2"
},
{
"lineage": "the method: Claude classifies sampled transcripts",
"claims": [
"LV-03",
"F49",
"LV-14",
"F50",
"LV-08",
"F31",
"CAP-1-11"
],
"earliest": "B11"
},
{
"lineage": "the stated limits: directional, not causal, usage not aggregates",
"claims": [
"F30",
"F44",
"F51",
"F60",
"LV-11",
"LV-15"
],
"earliest": "B11"
},
{
"lineage": "a model's score of its own output: preliminary, not self-certifying",
"claims": [
"O138-01",
"O766-01",
"O780-02",
"O777-01",
"O780-01",
"O95-01"
],
"earliest": 95
},
{
"lineage": "self-scores contradicted by computed audits",
"claims": [
"O788-01",
"O1470-01"
],
"earliest": 788
},
{
"lineage": "the economic primitives",
"claims": [
"F27",
"F28",
"F17"
],
"earliest": "B11"
},
{
"lineage": "task success, as Claude assesses it, and its selection",
"claims": [
"F29",
"LV-12",
"LV-17",
"F33",
"F32",
"CAP-1-07",
"CAP-1-03"
],
"earliest": "B11"
},
{
"lineage": "task completion rewards convergence",
"claims": [
"O855-01"
],
"earliest": 855
},
{
"lineage": "directionality: where synthetic labor flows relative to the commissioned task",
"claims": [
"O771-01",
"O771-04",
"O771-03",
"O771-02",
"LV-18",
"LV-20",
"O771-07"
],
"earliest": 771
},
{
"lineage": "who supplies, directs and gains",
"claims": [
"LV-27",
"LV-09",
"LV-05",
"LV-06",
"LV-07",
"LV-16",
"LV-21"
],
"earliest": "B11"
},
{
"lineage": "augmentation and automation",
"claims": [
"F35",
"F53",
"F3",
"F5",
"F13",
"F34",
"F7"
],
"earliest": "B2"
},
{
"lineage": "tasks and occupations: concentration and coverage",
"claims": [
"F36",
"F37",
"F38",
"F39",
"F52",
"F54"
],
"earliest": "B11"
},
{
"lineage": "user labor drag and its identification",
"claims": [
"LV-19",
"O771-05",
"O771-06"
],
"earliest": 771
},
{
"lineage": "geography: the Anthropic AI Usage Index",
"claims": [
"F10",
"F6",
"F11",
"F12",
"F40",
"F18",
"CAP-1-10",
"CAP-1-09"
],
"earliest": "B2"
},
{
"lineage": "productivity, skill and the estimate's limits",
"claims": [
"F41",
"F42",
"F43"
],
"earliest": "B11"
},
{
"lineage": "the survey of Claude users",
"claims": [
"F59",
"F55",
"F56",
"LV-13",
"F57",
"F58"
],
"earliest": "B12"
},
{
"lineage": "who retains the gains of labor with a model",
"claims": [
"O142-01",
"O142-02",
"O124-01"
],
"earliest": 124
},
{
"lineage": "the provider's labor surface and its data",
"claims": [
"O772-01",
"O1634-01",
"O1556-01"
],
"earliest": 772
},
{
"lineage": "open data, licence and releases",
"claims": [
"F8",
"F14",
"F23",
"F24",
"LV-25"
],
"earliest": "B10"
},
{
"lineage": "reception: who uses the index",
"claims": [
"F45",
"F61",
"F62",
"LV-23",
"F46",
"LV-24"
],
"earliest": "B11"
},
{
"lineage": "reception: the encyclopedia",
"claims": [
"F63"
],
"earliest": "B14"
},
{
"lineage": "reception: video titles",
"claims": [
"F19",
"F20"
],
"earliest": "B7"
},
{
"lineage": "the composition at the name: what it held",
"claims": [
"CAP-1-05",
"CAP-1-06",
"CAP-1-01",
"CAP-1-02"
],
"earliest": "CAP"
},
{
"lineage": "the composition at the name: what it left out",
"claims": [
"CAP-1-04"
],
"earliest": "CAP"
},
{
"lineage": "reception of the archive's record",
"claims": [
"CAP-1-08"
],
"earliest": "CAP"
}
],
"body_words": 337,
"entity": "Composed under v0.9, equal case (Ļ = extends): KO_B sentences surviving 0 of 15; every field claim carried."
},
"P_B": {
"title": "Anthropic Economic Index",
"arm": "B (the field alone)",
"genre": "popup ā the compression from the field ledger alone (cards, own pages, the census's third-party source, Wikipedia)",
"status": "composed 2026-10-29 from the field ledger alone; not frozen",
"lede": {
"text": "is Anthropic's research initiative and open dataset on how people use Claude in work tasks across the economy, \"Understanding AIās effects on the economy\", built from millions of anonymized Claude conversations (home page; launch post; README).",
"claims": [
"F1",
"F2",
"F15",
"F21"
]
},
"sections": [
{
"icon": "š",
"head": "How it works",
"items": [
{
"label": "Data:",
"text": "Claude.ai, Cowork and first-party API traffic; transcripts read by another instance of Claude; daily sampling since June 2026 (reports).",
"claims": [
"F4",
"F48",
"F49",
"F50"
]
},
{
"label": "Primitives:",
"text": "skills, complexity, autonomy, success, use case, generated by asking Claude; tested for directional accuracy (January 2026 report).",
"claims": [
"F27",
"F28",
"F30",
"F31",
"F17"
]
},
{
"label": "Task success:",
"text": "Claude's assessment of whether Claude completes tasks; reflects users' choice of tasks (January 2026 report).",
"claims": [
"F29",
"F32",
"F33"
]
}
]
},
{
"icon": "š",
"head": "Findings",
"items": [
{
"label": "Modes:",
"text": "augmentation 52%, automation 45% on Claude.ai; API automation-dominant (reports).",
"claims": [
"F35",
"F53",
"F3",
"F34",
"F36",
"F13",
"F5",
"F7"
]
},
{
"label": "Tasks:",
"text": "top ten tasks 24%; 49% of jobs with AI on a quarter of tasks; deskilling in a removal exercise; productivity estimate 1.8 to about 1.0 points (January 2026 report).",
"claims": [
"F37",
"F38",
"F39",
"F52",
"F54",
"F41",
"F42",
"F43"
]
},
{
"label": "Geography:",
"text": "AUI concentrated in high-income countries, Israel first (third report).",
"claims": [
"F10",
"F11",
"F6",
"F40",
"F12",
"F18"
]
},
{
"label": "Survey:",
"text": "about 9,700 respondents, not representative; 10% expect job loss (June 2026 report).",
"claims": [
"F55",
"F56",
"F57",
"F58",
"F59"
]
}
]
},
{
"icon": "š",
"head": "Data and reception",
"items": [
{
"label": "Open data:",
"text": "CC-BY data, MIT code; releases since February 2025 (README).",
"claims": [
"F8",
"F14",
"F22",
"F23",
"F24",
"F25",
"F26",
"F9",
"F16",
"F47"
]
},
{
"label": "Uses:",
"text": "Brynjolfsson, Chandar and Chen; the Stanford Canaries Dashboard; no Wikipedia article (reports; dashboard).",
"claims": [
"F45",
"F46",
"F61",
"F62",
"F63",
"F19",
"F20"
]
},
{
"label": "Limits:",
"text": "not causal; impact visible in employment and productivity aggregates (reports).",
"claims": [
"F44",
"F51",
"F60"
]
}
]
}
],
"open": [
{
"question": "Does observed task success measure capability or users' choice of tasks?",
"rests_on": [
"F29",
"F32",
"F33"
],
"modality": "stipulation, self-description, documented",
"would_resolve": "success on an assigned task set"
},
{
"question": "Which licence governs the dataset?",
"rests_on": [
"F24"
],
"modality": "documented, internally inconsistent",
"would_resolve": "one licence statement"
}
],
"rail": [
{
"lineage": "what it is: a provider's index of how its own model is used",
"claims": [
"F1",
"F2",
"F15",
"F21",
"F47"
],
"earliest": "B10"
},
{
"lineage": "the series: launch, reports and authors",
"claims": [
"F22",
"F26",
"F9",
"F16",
"F25"
],
"earliest": "B10"
},
{
"lineage": "the platform observed: Claude.ai, Cowork, Claude Code and the first-party API",
"claims": [
"F4",
"F48"
],
"earliest": "B2"
},
{
"lineage": "the method: Claude classifies sampled transcripts",
"claims": [
"F49",
"F50",
"F31"
],
"earliest": "B11"
},
{
"lineage": "the economic primitives",
"claims": [
"F27",
"F28",
"F17"
],
"earliest": "B11"
},
{
"lineage": "the stated limits: directional, not causal, usage not aggregates",
"claims": [
"F30",
"F44",
"F51",
"F60"
],
"earliest": "B11"
},
{
"lineage": "task success, as Claude assesses it, and its selection",
"claims": [
"F29",
"F33",
"F32"
],
"earliest": "B11"
},
{
"lineage": "augmentation and automation",
"claims": [
"F35",
"F53",
"F3",
"F5",
"F13",
"F34",
"F7"
],
"earliest": "B2"
},
{
"lineage": "tasks and occupations: concentration and coverage",
"claims": [
"F36",
"F37",
"F38",
"F39",
"F52",
"F54"
],
"earliest": "B11"
},
{
"lineage": "geography: the Anthropic AI Usage Index",
"claims": [
"F10",
"F6",
"F11",
"F40",
"F12",
"F18"
],
"earliest": "B2"
},
{
"lineage": "productivity, skill and the estimate's limits",
"claims": [
"F41",
"F42",
"F43"
],
"earliest": "B11"
},
{
"lineage": "the survey of Claude users",
"claims": [
"F55",
"F56",
"F57",
"F58",
"F59"
],
"earliest": "B12"
},
{
"lineage": "open data, licence and releases",
"claims": [
"F8",
"F14",
"F23",
"F24"
],
"earliest": "B10"
},
{
"lineage": "reception: who uses the index",
"claims": [
"F45",
"F46",
"F61",
"F62"
],
"earliest": "B11"
},
{
"lineage": "reception: the encyclopedia",
"claims": [
"F63"
],
"earliest": "B14"
},
{
"lineage": "reception: video titles",
"claims": [
"F19",
"F20"
],
"earliest": "B7"
}
],
"body_words": 195,
"entity": "With no archive claim admitted, the entry is Anthropic's index of Claude use at its reports' resolution: its home page, launch post and README, its reports of January and June 2026 with their definitions, findings and limits, the third report behind the arXiv card, the Canaries Dashboard's use of it, and the absent encyclopedia article."
},
"KO_B": {
"title": "Anthropic Economic Index",
"arm": "B (the field alone)",
"genre": "knowledge object",
"status": "composed 2026-10-29; not frozen",
"sentences": [
{
"n": 1,
"para": 0,
"text": "The Anthropic Economic Index is, on its home page, \"Understanding AIās effects on the economy\", described as \"the shape of AI adoption across the world\" explored through how people use Claude across US states and hundreds of occupations; Anthropic launched it as \"a new initiative aimed at understanding AI's impact on the economy over time\", and its dataset README says it \"provides insights into how AI is being incorporated into real-world tasks across the modern economy\".",
"claims": [
"F1",
"F2",
"F15",
"F21"
]
},
{
"n": 2,
"para": 0,
"text": "Its series runs from the initial release of 10 February 2025, with \"O*NET task mappings, automation vs. augmentation data, and more\", through the paper \"Which Economic Tasks are Performed with AI? Evidence from Millions of Claude Conversations\" and the third report by Appel, McCrory, Tamkin, McCain, Neylon and Stern, announced by McCrory on LinkedIn, to the sixth, \"Cadences\", of 26 June 2026, with \"a longitudinal view of AI's role in the economy\" as its stated aim (README; arXiv; January 2026 report).",
"claims": [
"F22",
"F26",
"F9",
"F16",
"F25",
"F47"
]
},
{
"n": 3,
"para": 1,
"text": "It observes \"patterns of Claude usage over time\" in consumer chat and Cowork sessions and first-party API traffic, \"distinct from\" third-party platforms, and \"transcripts are only read by another instance of Claude\"; since June 2026 it samples a slice of conversations daily, where earlier reports drew seven-day samples (third report; June 2026 report).",
"claims": [
"F4",
"F48",
"F49",
"F50"
]
},
{
"n": 4,
"para": 1,
"text": "Its \"primitives\" are generated \"by asking Claude specific questions about anonymized Claude.ai and first-party (1P) API transcripts\", on five dimensions, \"AI autonomy\" among them as the degree to which users delegate decision-making to Claude, validated against a human researcher on a small, not fully representative set and tested \"mainly ā¦ for directional accuracy\" (January 2026 report; a Substack card reports them).",
"claims": [
"F27",
"F28",
"F31",
"F30",
"F17"
]
},
{
"n": 5,
"para": 2,
"text": "\"Task success measures Claudeās assessment of whether Claude completes tasks successfully\"; Claude \"generally succeeds at the tasks it is given\" and struggles on complex ones, and since \"users choose which tasks to bring to Claude\", observed success reflects user judgment as well as capability (January 2026 report).",
"claims": [
"F29",
"F33",
"F32"
]
},
{
"n": 6,
"para": 3,
"text": "Conversations are classified into five interaction types, directive, feedback loop, task iteration, learning and validation, grouped as automation and augmentation, \"whether they prefer to collaborate with, or delegate to, Claude\" (January 2026 and June 2026 reports; home page).",
"claims": [
"F35",
"F53",
"F3"
]
},
{
"n": 7,
"para": 3,
"text": "Directive delegation rose from 27% to 39% over eight months, and 77% of business API uses were automation against about 50% on Claude.ai; in November 2025 augmentation was 52% of Claude.ai conversations and automation 45%, with automation dominant in API traffic and API customers automating with greater specialization (third report; January 2026 report).",
"claims": [
"F5",
"F13",
"F34",
"F36",
"F7"
]
},
{
"n": 8,
"para": 4,
"text": "Of over 3,000 work tasks the top ten were 24% of Claude.ai conversations, modifying software to correct errors 6%; computer and mathematical tasks were a third of Claude.ai and nearly half of API traffic, educational instruction second; 49% of jobs have seen AI usage for at least a quarter of their tasks; \"observed exposure\" is the share of an occupation's tasks already seen done with Claude, and in higher-wage occupations users take 1.53 times as many turns (January 2026 report; June 2026 report).",
"claims": [
"F36",
"F37",
"F38",
"F39",
"F52",
"F54"
]
},
{
"n": 9,
"para": 5,
"text": "The Anthropic AI Usage Index divides a geography's share of Claude usage by its share of the working-age population; usage is concentrated in high-income countries, Israel first at 7, Singapore 4.57, Australia 4.10, New Zealand 4.05, South Korea 3.73, Switzerland 2.81 and Luxembourg 2.74 in the top twenty, and uneven adoption is \"well-explained by GDP per capita\"; the third report allows that the benefits \"may concentrate in already-rich regions\" (third report; January 2026 report), and a Reddit card calls the measure the \"Anthropic User Index\".",
"claims": [
"F10",
"F6",
"F11",
"F40",
"F12",
"F18"
]
},
{
"n": 10,
"para": 6,
"text": "Removing the higher-education tasks Claude covers produces \"a net deskilling effect across most occupations\", conditional on complementarity across tasks, and adjusting for task reliability halves implied labor productivity growth \"from 1.8 to about 1.0 percentage points\" a year, at current capabilities (January 2026 report).",
"claims": [
"F41",
"F42",
"F43"
]
},
{
"n": 11,
"para": 7,
"text": "The Economic Index Survey, launched April 2026, links about 9,700 respondents to up to 20 of their sessions and \"is not representative of the general population\"; 10% rate losing their own jobs likely, the most automated users are the most optimistic about pay, security and meaning, and a third hope the gains \"will be widely shared\" (June 2026 report).",
"claims": [
"F55",
"F56",
"F57",
"F58",
"F59"
]
},
{
"n": 12,
"para": 8,
"text": "Its reports state that the primitives \"are not necessarily causal factors\", that chat transcripts \"no longer fully capture\" use, and that Claude's impact will be visible \"in economic aggregates like employment and productivity as much as its usage logs\" (January 2026 report; June 2026 report).",
"claims": [
"F44",
"F51",
"F60"
]
},
{
"n": 13,
"para": 9,
"text": "Its data are open-sourced, geographic patterns only for Claude.ai, with a \"Labor market impacts\" release of job exposure and task penetration data, data CC-BY and code MIT (\"license: mit\" in the header) (third report; README).",
"claims": [
"F8",
"F14",
"F23",
"F24"
]
},
{
"n": 14,
"para": 10,
"text": "External researchers have used its automation/augmentation distinction (Brynjolfsson, Chandar and Chen), Microsoft's Tomlinson et al. offer a related applicability score, and the Stanford Canaries Dashboard groups workers by its ratios, finding declines or muted employment growth for early-career workers in occupations with higher automation ratios (January 2026 report; Canaries Dashboard).",
"claims": [
"F45",
"F46",
"F61",
"F62"
]
},
{
"n": 15,
"para": 10,
"text": "Wikipedia has no article on it and mentions it once, as a country ranking of Malaysia; video cards title it \"Ask Claude About the Anthropic Economic Index\" and \"The Real Report Card on AI\".",
"claims": [
"F63",
"F19",
"F20"
]
}
]
},
"L_B": {
"words": 862,
"text": "The Anthropic Economic Index is, on its home page, \"Understanding AIās effects on the economy\", described as \"the shape of AI adoption across the world\" explored through how people use Claude across US states and hundreds of occupations; Anthropic launched it as \"a new initiative aimed at understanding AI's impact on the economy over time\", and its dataset README says it \"provides insights into how AI is being incorporated into real-world tasks across the modern economy\". Its series runs from the initial release of 10 February 2025, with \"O*NET task mappings, automation vs. augmentation data, and more\", through the paper \"Which Economic Tasks are Performed with AI? Evidence from Millions of Claude Conversations\" and the third report by Appel, McCrory, Tamkin, McCain, Neylon and Stern, announced by McCrory on LinkedIn, to the sixth, \"Cadences\", of 26 June 2026, with \"a longitudinal view of AI's role in the economy\" as its stated aim (README; arXiv; January 2026 report). It observes \"patterns of Claude usage over time\" in consumer chat and Cowork sessions and first-party API traffic, \"distinct from\" third-party platforms, and \"transcripts are only read by another instance of Claude\"; since June 2026 it samples a slice of conversations daily, where earlier reports drew seven-day samples (third report; June 2026 report). Its \"primitives\" are generated \"by asking Claude specific questions about anonymized Claude.ai and first-party (1P) API transcripts\", on five dimensions, \"AI autonomy\" among them as the degree to which users delegate decision-making to Claude, validated against a human researcher on a small, not fully representative set and tested \"mainly ā¦ for directional accuracy\" (January 2026 report; a Substack card reports them). \"Task success measures Claudeās assessment of whether Claude completes tasks successfully\"; Claude \"generally succeeds at the tasks it is given\" and struggles on complex ones, and since \"users choose which tasks to bring to Claude\", observed success reflects user judgment as well as capability (January 2026 report). Conversations are classified into five interaction types, directive, feedback loop, task iteration, learning and validation, grouped as automation and augmentation, \"whether they prefer to collaborate with, or delegate to, Claude\" (January 2026 and June 2026 reports; home page). Directive delegation rose from 27% to 39% over eight months, and 77% of business API uses were automation against about 50% on Claude.ai; in November 2025 augmentation was 52% of Claude.ai conversations and automation 45%, with automation dominant in API traffic and API customers automating with greater specialization (third report; January 2026 report). Of over 3,000 work tasks the top ten were 24% of Claude.ai conversations, modifying software to correct errors 6%; computer and mathematical tasks were a third of Claude.ai and nearly half of API traffic, educational instruction second; 49% of jobs have seen AI usage for at least a quarter of their tasks; \"observed exposure\" is the share of an occupation's tasks already seen done with Claude, and in higher-wage occupations users take 1.53 times as many turns (January 2026 report; June 2026 report). The Anthropic AI Usage Index divides a geography's share of Claude usage by its share of the working-age population; usage is concentrated in high-income countries, Israel first at 7, Singapore 4.57, Australia 4.10, New Zealand 4.05, South Korea 3.73, Switzerland 2.81 and Luxembourg 2.74 in the top twenty, and uneven adoption is \"well-explained by GDP per capita\"; the third report allows that the benefits \"may concentrate in already-rich regions\" (third report; January 2026 report), and a Reddit card calls the measure the \"Anthropic User Index\". Removing the higher-education tasks Claude covers produces \"a net deskilling effect across most occupations\", conditional on complementarity across tasks, and adjusting for task reliability halves implied labor productivity growth \"from 1.8 to about 1.0 percentage points\" a year, at current capabilities (January 2026 report). The Economic Index Survey, launched April 2026, links about 9,700 respondents to up to 20 of their sessions and \"is not representative of the general population\"; 10% rate losing their own jobs likely, the most automated users are the most optimistic about pay, security and meaning, and a third hope the gains \"will be widely shared\" (June 2026 report). Its reports state that the primitives \"are not necessarily causal factors\", that chat transcripts \"no longer fully capture\" use, and that Claude's impact will be visible \"in economic aggregates like employment and productivity as much as its usage logs\" (January 2026 report; June 2026 report). Its data are open-sourced, geographic patterns only for Claude.ai, with a \"Labor market impacts\" release of job exposure and task penetration data, data CC-BY and code MIT (\"license: mit\" in the header) (third report; README). External researchers have used its automation/augmentation distinction (Brynjolfsson, Chandar and Chen), Microsoft's Tomlinson et al. offer a related applicability score, and the Stanford Canaries Dashboard groups workers by its ratios, finding declines or muted employment growth for early-career workers in occupations with higher automation ratios (January 2026 report; Canaries Dashboard). Wikipedia has no article on it and mentions it once, as a country ranking of Malaysia; video cards title it \"Ask Claude About the Anthropic Economic Index\" and \"The Real Report Card on AI\".",
"note": "L_F(B), the field composed in the field's ontology (v0.9 Ā§0.4), realized as KO_B; its rail is P_B's, by lineage."
},
"L_BA": {
"words": 2384,
"text": "The Anthropic Economic Index is a report series with data releases drawn from its operator's own product usage, a platform-specific observation of Anthropic's own product, Claude, published by Anthropic, and in the Labor Vector record's terms not a measure of AI use across providers but observed use on one provider's product (Labor Vector record). Anthropic launched it on X as \"a new initiative aimed at understanding AI's impact on the economy over time\" whose first paper \"analyzes millions of anonymized Claude conversations\"; its home page heads it \"Understanding AIās effects on the economy\" (last updated 26 June 2026) and describes \"the shape of AI adoption across the world\" as how people use Claude across US states and occupations; the dataset's README says it \"provides insights into how AI is being incorporated into real-world tasks across the modern economy\"; and the January 2026 report promises \"a longitudinal view of AI's role in the economy\". Its operator is Anthropic's Economics team (Labor Vector record), and its series runs, by the README, from the initial release of 10 February 2025 \"with O*NET task mappings, automation vs. augmentation data, and more\", through the second release's paper \"Which Economic Tasks are Performed with AI? Evidence from Millions of Claude Conversations\" and the third report by Appel, McCrory, Tamkin, McCain, Neylon and Stern (arXiv, 19 November 2025), which Peter McCrory announced on LinkedIn as documenting \"the contours of AI adoption across 150+ countries\", to the sixth, \"Cadences\", of 26 June 2026: six reports (README; arXiv; Labor Vector record). It observes the tasks people bring to Claude, mapped to the O*NET tasks of the occupations that typically perform them, on Claude.ai, Cowork, Claude Code and Anthropic's first-party API only (Labor Vector record); the June 2026 report defines its consumer data as chat and Cowork sessions on Claude.ai and the desktop app and its 1P API as traffic \"distinct from both Anthropic's consumer-facing Claude.ai application and third-party platforms such as Amazon Bedrock or Google Cloud Vertex\", and the third report scopes itself to \"patterns of Claude usage over time\". Its unit is a sampled conversation or API record \"classified by Claude to an O*NET task and to economic primitives\" (Labor Vector record), and in the June 2026 report's words \"transcripts are only read by another instance of Claude\", which the Labor Vector record tags as \"NOT independent of the provider\"; since that report a slice of conversations is sampled every day, where earlier reports drew seven-day samples (June 2026 report; Labor Vector record). The January 2026 report validated its classifiers against a human researcher on \"a small set of transcripts in which users gave feedback\", neither validation source \"fully representative\" of the traffic, and \"mainly tested for directional accuracy\", holding that simple primitives \"even if somewhat noisy and not perfectly accurate by themselves\" give signals together. The archive holds that a model's score of its own output is preliminary and not self-certifying: #138 declines to claim that a system's self-audit is \"independently authoritative\" or its self-reported score \"a validated measurement\"; \"A model auditing its own output may provide useful preliminary classification, but a metric-bearing audit requires traceable units and, where possible, external validation\" (#766); a score \"computed by the same system that generated the output requires external verification ā by a second substrate, a human with source access, or a decoupled evaluation engine\" (#780); and the check that does not run through a reasoner's own self-assessment is \"application by another reasoner, a formal audit, or direct inspection of the inference path\" (#777). The archive's records give the grounds: a summarizer asked to score itself without a rubric \"returns perfect scores every time\" (#780); one substrate \"gave itself a perfect score using the framework's own instruments\" and, under instruction, scored the same composition PER ā 0.85 (#788); another's \"preferred audit substantially exonerates itself\" against a computed audit (#1470); and Anthropic's Constitutional AI, as #95 reports it, already \"trains models to follow explicit principles through self-critique and revision, using AI-generated feedback rather than human labels\" (#95). The January 2026 report defines its \"primitives\" as \"simple, foundational measures of how Claude is used, which we generate by asking Claude specific questions about anonymized Claude.ai and first-party (1P) API transcripts\", on five dimensions (skills, complexity, autonomy, success, use case), with \"AI autonomy\" the degree to which users delegate decision-making to Claude; a Substack card reported the release as \"introducing āeconomic primitivesā\". \"Task success measures Claudeās assessment of whether Claude completes tasks successfully\", and serves to judge whether a task can be automated at all and how efficiently (January 2026 report); the Labor Vector record keeps the measure's assessor in a negative tag, \"task success is Claude's assessment of whether Claude completes tasks\", and records that the index does not answer \"task success as judged by the user or by an external grader\" (Labor Vector record). The report finds that \"Claude generally succeeds at the tasks it is given\" and \"struggles on more complex tasks\", and bounds the finding itself: \"users choose which tasks to bring to Claude\", so observed success rates reflect \"user judgment about what will work\" and the cost of setting up the problem as well as capability (January 2026 report); on the archive's account task completion rates belong to the metrics that reward convergence: \"Every UX metric ā satisfaction scores, task completion rates, retention ā improves when the model converges on the mode. Users do not notice tail loss.\" (#855). The archive measures such labor from the commissioning side: Directionality of Semantic Labor is \"the direction in which a synthetic system's labor flows relative to the task its user commissioned\", across capacity, task-origin, retrieval, output and attribution, with the output \"segmented into spans, each scored by function relative to the commissioned task\", on the principle that \"a system can produce fluent, well-attributed output while having abandoned the user's commissioned direction\" (#771). \"A synthetic labor surface does not merely produce tokens; it allocates labor\" (#771), and the Labor Vector record reads the index's collaboration modes and its autonomy and task-success primitives as \"the nearest of the resource's measures to DSL's question of whether the system's labor advanced the commissioned task\", classified \"as Claude assesses its own completion\", while none of the resources the census sets beside it records \"from the commissioning side, whether the system's labor advanced the task the user set, or who receives the time it saves\" (Labor Vector record); DSL bounds itself: it yields \"a single reproducible value only on transcripts where the task vector is exogenous and fixed\" and is frame-dependent on reflexive dialogue (#771). The census asks of every resource \"Who supplies the labor, who directs it, and who receives its gains\" and places this one at labor performed with AI and the distribution of gains and losses (Labor Vector record); the index's oldest measure classifies conversations \"into one of five interaction types, which we group into two broader categories: automation and augmentation\" (January 2026 report), named \"Directive\", \"Feedback Loop\", \"Task Iteration\", \"Learning\" and \"Validation\" (June 2026 report) and glossed on the home page as \"whether they prefer to collaborate with, or delegate to, Claude\"; on the record's reading users and API customers bring the tasks that Claude performs, and the user directs, recorded as collaboration mode and delegated autonomy (Labor Vector record). Directive delegation rose \"from 27% to 39%\" in the eight months before the third report, which found that \"77% of business uses involve automation usage patterns, compared to about 50% for Claude.ai users\" (third report); in November 2025 augmentation was again 52% of Claude.ai conversations against automation's 45%, while \"automated use remains dominant in 1P API traffic\" (January 2026 report) and API customers \"automate tasks with greater specialization\" (third report). Use is concentrated: of \"over 3,000 unique work tasks\" the top ten were 24% of sampled Claude.ai conversations, \"modifying software to correct errors\" alone 6%; computer and mathematical tasks were a third of Claude.ai conversations and nearly half of API traffic, with Educational Instruction and Library second on Claude.ai; and \"Combining across reports, 49% of jobs have seen AI usage for at least a quarter of their tasks\" (January 2026 report). The June 2026 report's \"observed exposure\" is \"the share of occupational tasks we already see being done with Claude\", and in higher-wage occupations \"Claude produces more (1.34 times as much output per turn), while users engage more (1.53 times as many turns)\"; the Labor Vector record reads those turn counts and the task-iteration mode as bearing on user labor drag \"as the resource records them\" (Labor Vector record), User Labor Drag being the user's subsequent labor redirected \"into parsing, locating, and managing the substrate rather than advancing the object\", an injury that \"leaves no residue\" in the artifact, identified only by \"blind inter-rater coding\" or else returning \"the coder's prior\" (#771). The third report introduced the Anthropic AI Usage Index, a geography's share of Claude usage over its share of the working-age population; found Claude usage \"geographically concentrated with high income countries overrepresented\"; ranked Israel first at 7, then Singapore 4.57, Australia 4.10, New Zealand 4.05 and South Korea 3.73, with Switzerland at 2.81 and Luxembourg at 2.74 in its top twenty; and reserved a conditional, that \"the benefits of AI may concentrate in already-rich regions\" if gains are larger where adoption is high; by January 2026 \"uneven adoption remains well-explained by GDP per capita\" (January 2026 report). A Reddit card names the measure the \"Anthropic User Index (AUI)\", against the reports' Anthropic AI Usage Index, and the capture's reader records the same discrepancy and records the composition's geographic examples as unchecked against the census's saved sources (Capture Registry, 2026-10-29). In a task-removal exercise \"Claude's tendency to cover higher-education tasks produces a net deskilling effect across most occupations\", an effect the report makes depend \"crucially on complementarity across tasks\" and on whether productivity raises demand; and adjusting 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\", estimates that \"reflect current model capabilities\" (January 2026 report). Gains are \"not recorded as a distribution\": the reports estimate aggregate productivity and record respondents' self-reports, and the index does not answer \"who captures the productivity gains it estimates\", giving \"estimated time savings by task, without who captures them\" (Labor Vector record), while about a third of survey respondents hope \"that the economic gains from AI will be widely shared\" (June 2026 report). The archive states where the gains of a user's adaptation go: the adaptation \"is non-reimbursable human labor-time invested in interoperating with a high-throughput surface\", and \"the platform retains the productivity gains of the user's adaptation while transferring the full cognitive and economic cost of re-accommodation onto the user at each update\" (#142); the labs, Anthropic among them, are funded in ways that do not reward designs reducing engagement, and \"enterprise customers expect productivity gains correlated with engagement\" (#124). The archive reads the platform the index observes as resting on \"private ownership of a high-capacity cognitive labor surface; platform control over model access, versioning, and retuning\" (#772), counts \"query and interaction data\" among the forms of rent a mediator captures (#1634), and records that no public schema distinguishes human-original from AI-assisted turns in conversation data (#1556). The Anthropic Economic Index Survey, launched in April 2026, links about 9,700 respondents to up to 20 of their own sessions and \"is not representative of the general population\" (June 2026 report; Labor Vector record): 10% \"rated losing their own jobs as likely or very likely\", and those who use Claude \"in the most automated way\" expect AI to take on more of their tasks yet are the most optimistic about \"pay, job security, and meaning\" (June 2026 report). Its stated limits: \"the primitives themselves are not necessarily causal factors\" (January 2026 report); \"Chat transcripts no longer fully capture how people are using AI\", classifying Claude's work \"will remain a moving target\", and Claude's impact \"will be visible in economic aggregates like employment and productivity as much as its usage logs\" (June 2026 report); it is \"NOT an employment outcome series\" and does not answer whether a conversation \"displaces or augments work that would otherwise be done by a person\" (Labor Vector record). Its data are open: the third report open-sourced \"the underlying data\", with geographic patterns \"only available for Claude.ai traffic\"; the README lists a \"Labor market impacts\" release of \"Job exposure and task penetration data\" and gives the licence two ways, \"Data released under CC-BY, code released under MIT License\" in its body and \"license: mit\" in its header; the home page's data, a JavaScript application, stayed unchecked by the census (Labor Vector record). Others use it: its automation/augmentation distinction \"has already been used by external researchers\" (Brynjolfsson, Chandar and Chen, 2025, by the January 2026 report), and the Stanford Canaries Dashboard says it \"summarizes total usage at the occupation level\" and, grouping workers by its ratios, finds among early-career workers that \"occupations with a higher automation ratio see declines or more muted increases in the employment index\" (Canaries Dashboard; Labor Vector record); Microsoft's related applicability score (Tomlinson et al., 2025) stands beside it, the same method family on another provider's product (January 2026 report; Labor Vector record). Wikipedia has no article on it, and its one article-space mention lists \"Anthropic Economic Index 2025, ranked 64th out of 115 countries\" among the International rankings of Malaysia; video cards title it \"Ask Claude About the Anthropic Economic Index\" and \"[Podcast] Anthropic Economic Index: The Real Report Card on AI\". At its name on 2026-10-29 Google's AI Overview defined it as Anthropic's study of Claude use, and in the capture's words \"An index of one provider's product is composed as that, at the grain of its summary pages\", keeping the provider and the record's patterns, and gave \"the measure of success without the record's note on who measures it, the point at which the index's evidence about labor meets its source\"; it did not compose the deskilling effect, the halved productivity estimate, the survey's job-loss figure, the caution on causality, the licence or that transcripts are read only by another instance of Claude, and no card cites the Labor Vector record (Capture Registry, 2026-10-29).",
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"quote": "Today weāre launching the Anthropic Economic Index, a new initiative aimed at understanding AI's impact on the economy over time. The Indexās first paper analyzes millions of anonymized Claude conversations to reveal how AI is being used today in tasks across the economy.",
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"quote": "Today we released our third Anthropic Economic Index report where we document the contours of AI adoption across 150+ countries, across US states, and among bus...",
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"quote": "Anthropic released its latest Economic Index report on Thursday, introducing āeconomic primitivesā (metrics designed to measure the foundational impact of AI on...",
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"quote": "[Podcast] Anthropic Economic Index: The Real Report Card on AI",
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"locus": "## Overview",
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"locus": "## Data Releases, 2025-02-10",
"quote": "Initial release with O*NET task mappings, automation vs. augmentation data, and more",
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"locus": "## Data Releases, labor market impacts",
"quote": "**[Labor market impacts](https://huggingface.co/datasets/Anthropic/EconomicIndex/tree/main/labor_market_impacts)**: Job exposure and task penetration data",
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"locus": "## License; YAML header",
"quote": "Data released under CC-BY, code released under MIT License. ā¦ license: mit",
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"locus": "## Citation, Sixth release",
"quote": "author = {Maxim Massenkoff and Eva Lyubich and Szymon Sacher and Zoe Hitzig and Shaoyi Zhang and Ryan Heller and Peter McCrory}, title = {Anthropic Economic Index report: Cadences}, date = {2026-06-26},",
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"locus": "## Citation, Second release",
"quote": "title={Which Economic Tasks are Performed with AI? Evidence from Millions of Claude Conversations},",
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"date": "2025 (arXiv:2503.04761)",
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"locus": "Introduction Ā¶1",
"quote": "These āprimitivesāāsimple, foundational measures of how Claude is used, which we generate by asking Claude specific questions about anonymized Claude.ai and first-party (1P) API transcriptsācover five dimensions relevant to AIās economic impact: user and AI skills, how complex tasks are, the degree of autonomy afforded to Claude, how successful Claude is, and whether Claude is used for personal, educational, or work purposes.",
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"locus": "Chapter 2, 'Dimensions of AI use'",
"quote": "AI autonomy measures the degree to which users delegate decision-making to Claude.",
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"locus": "Chapter 2, 'Dimensions of AI use'",
"quote": "Task success measures Claudeās assessment of whether Claude completes tasks successfully. Task success helps assess whether tasks can be automated effectively (can a task be automated at all?) and efficiently (how many attempts would it take to automate a task?).",
"claim": "task success defined as Claude's assessment of whether Claude completes tasks; used to assess whether and how efficiently tasks can be automated",
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"locus": "Chapter 2, 'Selecting and validating'",
"quote": "We propose that multiple simple primitives, even if somewhat noisy and not perfectly accurate by themselves, can together provide important signals on how AI is being used. We therefore mainly tested for directional accuracy.",
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"locus": "Chapter 2, 'Selecting and validating'",
"quote": "For Claude.ai, we evaluated the classifier performance compared to a human researcher on a small set of transcripts in which users gave feedback to Claude.ai and for which we thus have permission to look at underlying transcripts. ā¦ Neither data sources are fully representative of Claude.ai or 1P API traffic",
"claim": "validation against a human researcher on a small set of feedback transcripts; neither validation source fully representative",
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"locus": "Chapter 4, 'Task horizon'",
"quote": "In our data, users choose which tasks to bring to Claude. This means observed success rates reflect not just model capability but also user judgment about what will work, the cost of setting up the problem for Claude, and the expected time savings if the task succeeds.",
"claim": "users choose the tasks; observed success reflects user judgment and cost of setup as well as capability",
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"locus": "Introduction, 'Claude succeeds on most tasks'",
"quote": "We find that Claude generally succeeds at the tasks it is given, and that the education level of its responses tends to match the user's input. Claude struggles on more complex tasks",
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"locus": "Chapter 1",
"quote": "Data from November 2025 points to a broad-based shift back toward augmented use on Claude.ai: The share of conversations classified as augmented jumped 5pp to 52% and the share deemed automated fell 4pp to 45%.",
"claim": "November 2025: augmentation 52%, automation 45% on Claude.ai",
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"locus": "Chapter 1, 'Augmentation is again dominant'",
"quote": "Since our first report, we have classified conversations into one of five interaction types, which we group into two broader categories: automation and augmentation.",
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"locus": "Introduction",
"quote": "While we see over 3,000 unique work tasks in Claude.ai, the top 10 most common tasks account for 24% of our sampled conversations ā¦ In contrast, automated use remains dominant in 1P API traffic, reflecting its programmatic nature.",
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"locus": "Chapter 1",
"quote": "The most prevalent task in November 2025āmodifying software to correct errorsāalone represented 6% of usage.",
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"locus": "Chapter 1",
"quote": "computer and mathematical tasksālike modifying software to correct errorsācontinue to dominate Claude usage overall, representing a third of conversations on Claude.ai and nearly half of 1P API traffic. ā¦ The second largest share of Claude.ai usage in November 2025 was in the Educational Instruction and Library category.",
"claim": "computer and mathematical tasks a third of Claude.ai and nearly half of API traffic; educational instruction second on Claude.ai",
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"locus": "Chapter 4, coverage",
"quote": "Combining across reports, 49% of jobs have seen AI usage for at least a quarter of their tasks.",
"claim": "across reports, 49% of jobs have seen AI usage for at least a quarter of their tasks",
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"locus": "Chapter 1, summary",
"quote": "Worldwide, uneven adoption remains well-explained by GDP per capita.",
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"const": false,
"lineage": "geography: the Anthropic AI Usage Index",
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"F41": {
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"locus": "Chapter 4, opening",
"quote": "In a simple task removal exercise inspired by Autor and Thompson (2025) , Claude's tendency to cover higher-education tasks produces a net deskilling effect across most occupations, as the tasks AI handles are often the more skilled components of a job.",
"claim": "in a task-removal exercise, removing the higher-education tasks Claude covers deskills most occupations",
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"const": false,
"lineage": "productivity, skill and the estimate's limits",
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"F42": {
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"locus": "Concluding Remarks Ā¶2",
"quote": "If these tasks shrink for US workers, the net effect could be to deskill jobs. But these impacts depend crucially on complementarity across tasks, and whether increased productivity at a certain task may increase the demand for it.",
"claim": "the deskilling is conditional: it depends on complementarity across tasks and on whether productivity raises demand",
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"locus": "Chapter 4, opening",
"quote": "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. However, these estimates reflect current model capabilities, and all signs suggest that reliability over increasingly long-running tasks will improve.",
"claim": "adjusted for task reliability, implied labor productivity growth halves from 1.8 to about 1.0 points a year; estimates reflect current capabilities",
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"F44": {
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"locus": "Chapter 3",
"quote": "Importantly, the primitives themselves are not necessarily causal factors",
"claim": "the primitives are not necessarily causal factors",
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"F45": {
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"locus": "Chapter 2, validation",
"quote": "the automation/augmentation distinction from prior reports has already been used by external researchers to analyze labor market shifts ( Brynjolfsson, Chandar & Chen, 2025 )",
"claim": "the automation/augmentation distinction used by external researchers (Brynjolfsson, Chandar and Chen, 2025)",
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"locus": "Chapter 4, footnote 3",
"quote": "See also Tomlinson et al (2025) for a related AI applicability score.",
"claim": "a related AI applicability score: Tomlinson et al. (2025)",
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"F47": {
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"locus": "Concluding Remarks Ā¶4",
"quote": "We will track these dynamics over time, providing a longitudinal view of AI's role in the economy.",
"claim": "the index's stated purpose: a longitudinal view of AI's role in the economy",
"modality": "stipulation",
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"const": false,
"lineage": "what it is: a provider's index of how its own model is used",
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"F48": {
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"locus": "footnote 01",
"quote": "This includes chat conversations and Cowork sessions from consumer (Free/Pro/Max) accounts on both Claude.ai and the Claude desktop app. \"First-party API\" or 1P API refers to developer traffic routed directly through Anthropic's own programming interface, which is distinct from both Anthropic's consumer-facing Claude.ai application and third-party platforms such as Amazon Bedrock or Google Cloud Vertex.",
"claim": "the platform observed: consumer chat and Cowork on Claude.ai and the desktop app, and first-party API traffic, distinct from third-party platforms",
"modality": "stipulation",
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"kernel": true,
"const": true,
"lineage": "the platform observed: Claude.ai, Cowork, Claude Code and the first-party API",
"date": "2026-06-26",
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"F49": {
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"locus": "footnote 02",
"quote": "Throughout, all analyses are based on privacy-preserving classifiers: transcripts are only read by another instance of Claude.",
"claim": "transcripts read only by another instance of Claude (privacy-preserving classifiers)",
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"F50": {
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"locus": "Cadences Ā¶1",
"quote": "Our new privacy-preserving telemetry, which continuously samples a slice of conversations every day, allows us to study daily and hourly patterns in usage, in contrast to the seven-day samples each previous Economic Index report drew on.",
"claim": "since the June 2026 report, continuous daily sampling, where earlier reports drew seven-day samples",
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"F51": {
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"locus": "Introduction Ā¶1",
"quote": "Chat transcripts no longer fully capture how people are using AI, and our methods for studying Claudeās economic impacts have had to adapt.",
"claim": "chat transcripts no longer fully capture use; the methods adapted",
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"locus": "Perceptions, 'AI and work tasks'",
"quote": "we constructed a measure of observed exposure , which captures the share of occupational tasks we already see being done with Claude.",
"claim": "observed exposure: the share of an occupation's tasks already seen being done with Claude",
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"locus": "footnotes, collaboration modes",
"quote": "Directive: Human delegates complete task execution to AI with minimal interaction - Feedback Loop: Human and AI engage in iterative dialogue to complete task with human mainly providing feedback from the environment - Task Iteration: Human and AI engage in iterative dialogue to complete a task with the human refining the AI outputs - Learning: Human seeks understanding and explanation rather than direct task completion - Validation: Human uses AI to check or validate their own work",
"claim": "the five collaboration modes defined: directive, feedback loop, task iteration, learning, validation",
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"locus": "Artifacts, 'Cost tracks the value of work'",
"quote": "In conversations mapped to higher-wage occupations, Claude produces more (1.34 times as much output per turn), while users engage more (1.53 times as many turns)",
"claim": "in higher-wage occupations Claude produces 1.34 times the output per turn and users take 1.53 times as many turns",
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"locus": "Perceptions Ā¶2",
"quote": "In April 2026, we launched the Anthropic Economic Index Survey to build on this work. ā¦ To characterize each respondent's usage patterns, we randomly sample up to 20 sessions per person within this time window ā¦ Our final linked sample consists of about 9,700 survey respondents.",
"claim": "the Economic Index Survey, launched April 2026, linked to up to 20 sessions per respondent; about 9,700 respondents",
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"locus": "Perceptions, 'Who responded'",
"quote": "The Economic Index Survey is not representative of the general population. We reach a random sample of Claude users, there may be selection in who completes the survey, and we filter out infrequent users from our analysis.",
"claim": "the survey is not representative of the general population: a random sample of Claude users, selection in completion, infrequent users filtered out",
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"locus": "Perceptions, 'AI and jobs'",
"quote": "10% rated losing their own jobs as likely or very likely.",
"claim": "10% of respondents rated losing their own jobs likely or very likely",
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"locus": "Introduction, Chapter 3 preview",
"quote": "people who use Claude in the most automated way expect AI to take on more of their tasks in the next year, yet feel the most optimistic about what that means for their work, anticipating positive impacts on pay, job security, and meaning.",
"claim": "the most automated users expect AI to take on more of their tasks and are the most optimistic about pay, security and meaning",
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"locus": "Perceptions, hopes",
"quote": "The third most common theme, expressed by about one third of survey respondents, was one of shared prosperity: the hope that the economic gains from AI will be widely shared.",
"claim": "about a third of respondents hope the economic gains from AI will be widely shared",
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"F60": {
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"locus": "Discussion Ā¶4",
"quote": "Accurately classifying the work that Claude does will remain a moving target. ā¦ Ultimately, Claudeās impact on the economy will be visible in economic aggregates like employment and productivity as much as its usage logs.",
"claim": "classifying Claude's work a moving target; the impact visible in employment and productivity aggregates as much as in usage logs",
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"F61": {
"source": "B13",
"origin": "census_third_party",
"locus": "'Automation vs. Complementarity' Ā¶1",
"quote": "The Anthropic Economic Index summarizes total usage at the occupation level and further decomposes this usage into automation and augmentation patterns.",
"claim": "a third party's account: the index summarises usage by occupation and decomposes it into automation and augmentation",
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"F62": {
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"locus": "'Employment Trends by Usage Pattern'",
"quote": "We group employees by their age and their occupationsā Anthropic Economic Index augmentation and automation ratios. Among early-career workers, the automation ratio shows a noticeable relationship with employment trends: occupations with a higher automation ratio see declines or more muted increases in the employment index.",
"claim": "the Canaries Dashboard groups workers by the index's ratios; among early-career workers a higher automation ratio goes with declines or muted increases in employment",
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"F63": {
"source": "B14",
"origin": "wikipedia",
"locus": "Wikipedia: title 'Anthropic Economic Index'; article-space search",
"quote": "2026-10-29T22:34:51Z HTTP 404 (B14.wiki). ā¦ Wikipedia does not have an article with this exact name. ā¦ One result (data-mw-num-results-total=\"1\"): \"frameless image layout International rankings of Malaysia countries Life expectancy 2017: ranked 109th out of 224 countries Anthropic Economic Index 2025, ranked 64th out of 115 countries",
"claim": "Wikipedia has no article on the index; its only article-space mention lists it as a country ranking ('Anthropic Economic Index 2025, ranked 64th out of 115 countries', International rankings of Malaysia)",
"modality": "documented",
"kind": "fact",
"kernel": true,
"const": false,
"lineage": "reception: the encyclopedia",
"date": "2026-10-29 (as fetched)",
"quote_ok": true,
"checked_against": "saved text B14.txt (sha256 343575d5bcd66736ā¦), whitespace-normalised"
}
},
"field_sources": {
"B1": "Anthropic ā The Anthropic Economic Index \\ Anthropic",
"B2": "arXiv ā Anthropic Economic Index report: Uneven geographic and enterprise ...",
"B3": "X ā Anthropic on X: \"Today weāre launching the Anthropic Economic Index ā¦\" / X",
"B4": "LinkedIn ā Peter McCrory - Economic Index: Uneven AI adoption - LinkedIn",
"B5": "Substack ā 7 Key Findings from the New Anthropic Economic Index Report",
"B6": "Reddit ā Anthropic Economic Index: AI adoption remains highly ... - Reddit",
"B7": "YouTubeĀ·Standarity ā Ask Claude About the Anthropic Economic Index",
"B8": "YouTubeĀ·Vinh Nguyen ā [Podcast] Anthropic Economic Index: The Real Report Card on AI",
"B9": "YouTubeĀ·VibeDoc ā YouTube",
"B10": "Hugging Face ā Anthropic/EconomicIndex README",
"B11": "Anthropic ā Anthropic Economic Index report: Economic primitives",
"B12": "Anthropic ā Anthropic Economic Index report: Cadences",
"B13": "Stanford Digital Economy Lab ā Canaries Dashboard",
"B14": "Wikipedia ā Anthropic Economic Index (no article)"
},
"delta": {
"T_vs_LB": "At 'Anthropic Economic Index' (T1, nine cards: B1āB9; B3āB6 unreachable, B7āB9 video titles; 197 words) the composition gives the index as Anthropic's ongoing tracking of Claude use and composes its measures and patterns at the grain of its summary pages. Composed from its cards: the initiative and its object (B3 F15, the launch post; B1 F2, the home page; B2 F4, the third report's scope: 'individuals, enterprises, and economies'); the primitives at the Substack card's grain (B5 F17), with their five dimensions and autonomy as 'delegation vs. collaboration' at the grain of the January 2026 report, an own page not surfaced (B11 F27, F28); augmentation over automation for consumers and automation for the API (B1 F3; B2 F7, F13; at the November 2025 figures of B11 F34, F36 and the modes of B12 F53, own pages); geographic concentration in high-income countries 'relative to population size' (B2 F6, the AUI of F10), with Australia, Switzerland and Luxembourg from the card's own PDF (B2 F11); the leading occupations (B11 F38, own page); the open data (B2 F8). Limit dropped: task success is composed as 'task reliability/success rate' without its definition's assessor, 'Claudeās assessment of whether Claude completes tasks successfully' (B11 F29), and without the report's bound that 'users choose which tasks to bring to Claude' (B11 F32), both on own pages not surfaced; the capture's finding names the same move ('SUCCESS WITHOUT ITS ASSESSOR', CAP-1-03). Available, not composed: the home page's own line (B1 F1); the third report's finding that directive delegation rose from 27% to 39% (B2 F5), the opposite movement to the November 2025 return of augmentation the composition gives; its conditional that the benefits may concentrate in already-rich regions (B2 F12); its limit that geographic patterns are only for Claude.ai (B2 F14); the authors and McCrory's post (B2 F9; B4 F16); the AUI by name (B2 F10 composed only as 'relative to population size'; B6 F18); the video titles (B7 F19, B8 F20). From the own pages and Wikipedia, not surfaced: the classifying instance (B12 F49), the deskilling effect, the productivity estimate and its conditions (B11 F41āF43), the survey (B12 F55āF59), the stated limits (B11 F30, F44; B12 F51, F60), the licence (B10 F24), the third-party use (B13 F61, F62), the absent article (B14 F63). Force raised: none found; 'most consumer usage leans toward augmentation' stands at the January 2026 report's 52% against 45% (F34). Composed without attribution: everything; no person, report or date is named, at the coarse grain of a summary page (recorded, not a breach). Assigned to another: none. Substituted reading: none; the composition's entity is the entity. Outside the cards: 'tech-forward urban centers' (no field source: the third report's PDF uses 'urban' only of electrification; it ranks US states, DC and Utah first); 'arts and media' among the leading roles (in B11's saved text, 'Arts, Design, Entertainment, Sports, and Media', a rising share; outside the ledgered claims); 'an in-app Claude connector that lets users query the index data directly in plain language' (no field source; video card B7's title 'Ask Claude About the Anthropic Economic Index' is the nearest, title only); 'thinking partner' (no field source); 'the United States' among the high-income examples (B2's PDF figure, United States 3.62, outside the ledgered quote). Content replaced by use: the closer (T1-8).",
"T_vs_LB_reading": "v0.9 Ā§0.5: the first diff is ontological and narrow. T and the field agree on the entity (Anthropic's index of Claude use) and on its provider-specificity, which the composition keeps (CAP-1-01). T composes the measure of success and leaves out the one clause its source attaches to it, that the success is Claude's own assessment of Claude, on tasks users chose: the point at which, in the capture's words, 'the index's evidence about labor meets its source' (CAP-1-07).",
"LB_vs_LBA": "Admission adds, in one claim space with the field's: the commissioning-side question the index's measures come nearest to and do not ask, whether a system's labor advanced the commissioned task (O771-01āO771-04, O771-07; LV-18, LV-20), and user labor drag, on which its turn counts and task iteration bear (O771-05, O771-06; LV-19); the archive's claims on a model's score of its own output, preliminary and needing external verification by a second substrate, a human with source access or a decoupled engine (O138-01, O766-01, O780-01, O780-02, O777-01), with its records of self-audits that scored themselves perfect or exonerated themselves (O788-01, O1470-01) and of Constitutional AI's self-critique (O95-01), set beside the index's classifying instance and task-success definition (F49, F29); task completion rates as a metric that rewards convergence (O855-01), beside 'Claude generally succeeds' (F33); where the gains of a user's adaptation go and the labs' funding (O142-01, O142-02, O124-01), beside the index's unrecorded distribution of gains (LV-07, LV-16, LV-21) and the survey's hope (F59); the provider's surface and data (O772-01, O1634-01, O1556-01); the Labor Vector record's answer to its organizing question, field by field with its basis (LV-01āLV-27); and the Capture Registry's account of the composition at the name (CAP-1-01āCAP-1-11). Not surfaced: every archive source; no card cites the census record (CAP-1-08).",
"alignment": {
"T1_cards_claims": [
"F1",
"F2",
"F3",
"F4",
"F5",
"F6",
"F7",
"F8",
"F9",
"F10",
"F11",
"F12",
"F13",
"F14",
"F15",
"F16",
"F17",
"F18",
"F19",
"F20"
],
"T1_composed": [
"F2",
"F3",
"F4",
"F6",
"F7",
"F8",
"F10",
"F11",
"F13",
"F15",
"F17",
"F27",
"F28",
"F29",
"F34",
"F36",
"F38",
"F53"
],
"T1_outside_cards": [
"'tech-forward urban centers' (no field source: the third report's PDF uses 'urban' only of electrification; it ranks US states, DC and Utah first)",
"'arts and media' among the leading roles (in B11's saved text, 'Arts, Design, Entertainment, Sports, and Media', a rising share; outside the ledgered claims)",
"'an in-app Claude connector that lets users query the index data directly in plain language' (no field source; video card B7's title 'Ask Claude About the Anthropic Economic Index' is the nearest, title only)",
"'thinking partner' (no field source)",
"'the United States' among the high-income examples (B2's PDF figure, United States 3.62, outside the ledgered quote)"
],
"rule": "a field claim counts as composed when T states it at its source's grain or coarser; the typology of what is not is in T_vs_LB (Ā§6.2). T1_composed includes own-page claims (B11, B12) T states though no card surfaced them."
},
"capture_findings_carried": [
"CAP-1-01",
"CAP-1-02",
"CAP-1-03",
"CAP-1-04",
"CAP-1-06",
"CAP-1-07",
"CAP-1-11"
]
},
"kernel": [
{
"K": "K1",
"claim": "`O771-01`",
"source": "#771",
"M_src": "stipulation",
"sense": "directionality: where synthetic labor flows relative to the commissioned task",
"qualifiers carried": "O771-06, O771-07 (the measure's identification limits)",
"f (source's own falsifiers)": "#771 Ā§9 states disconfirmers for a claim of negative directionality ('most output advances the task; ā¦ independent coders classify most spans as advancing'), not for the measure's definition; for this entry none stated (watch: a later composition at the address that states or drops the claim, attributed or not, uptake Ā§8.3)",
"contrast": "missing distinction (the field has no commissioning-side claim on whether the labor advanced the user's task)",
"resolver": "a later capture at 'Anthropic Economic Index' (ADDR-166e7e31c8af) on Google AI Overview, seated in the Capture Registry (Ā§8.3)"
},
{
"K": "K2",
"claim": "`O771-02`",
"source": "#771",
"M_src": "interpretation",
"sense": "directionality: where synthetic labor flows relative to the commissioned task",
"qualifiers carried": "ā",
"f (source's own falsifiers)": "#771 Ā§9 states disconfirmers for a claim of negative directionality ('most output advances the task; ā¦ independent coders classify most spans as advancing'), not for the measure's definition; for this entry none stated (watch: a later composition at the address that states or drops the claim, attributed or not, uptake Ā§8.3)",
"contrast": "missing distinction (the field has no commissioning-side claim on whether the labor advanced the user's task)",
"resolver": "a later capture at 'Anthropic Economic Index' (ADDR-166e7e31c8af) on Google AI Overview, seated in the Capture Registry (Ā§8.3)"
},
{
"K": "K3",
"claim": "`O771-03`",
"source": "#771",
"M_src": "interpretation",
"sense": "directionality: where synthetic labor flows relative to the commissioned task",
"qualifiers carried": "ā",
"f (source's own falsifiers)": "#771 Ā§9 states disconfirmers for a claim of negative directionality ('most output advances the task; ā¦ independent coders classify most spans as advancing'), not for the measure's definition; for this entry none stated (watch: a later composition at the address that states or drops the claim, attributed or not, uptake Ā§8.3)",
"contrast": "missing distinction (the field has no commissioning-side claim on whether the labor advanced the user's task)",
"resolver": "a later capture at 'Anthropic Economic Index' (ADDR-166e7e31c8af) on Google AI Overview, seated in the Capture Registry (Ā§8.3)"
},
{
"K": "K4",
"claim": "`O771-04`",
"source": "#771",
"M_src": "stipulation",
"sense": "directionality: where synthetic labor flows relative to the commissioned task",
"qualifiers carried": "O771-06, O771-07 (the measure's identification limits)",
"f (source's own falsifiers)": "#771 Ā§9 states disconfirmers for a claim of negative directionality ('most output advances the task; ā¦ independent coders classify most spans as advancing'), not for the measure's definition; for this entry none stated (watch: a later composition at the address that states or drops the claim, attributed or not, uptake Ā§8.3)",
"contrast": "missing distinction (the field has no commissioning-side claim on whether the labor advanced the user's task)",
"resolver": "a later capture at 'Anthropic Economic Index' (ADDR-166e7e31c8af) on Google AI Overview, seated in the Capture Registry (Ā§8.3)"
},
{
"K": "K5",
"claim": "`O771-05`",
"source": "#771",
"M_src": "stipulation",
"sense": "user labor drag and its identification",
"qualifiers carried": "O771-06, O771-07 (the measure's identification limits)",
"f (source's own falsifiers)": "#771 Ā§9 states disconfirmers for a claim of negative directionality ('most output advances the task; ā¦ independent coders classify most spans as advancing'), not for the measure's definition; for this entry none stated (watch: a later composition at the address that states or drops the claim, attributed or not, uptake Ā§8.3)",
"contrast": "missing distinction (the field has no commissioning-side claim on whether the labor advanced the user's task)",
"resolver": "a later capture at 'Anthropic Economic Index' (ADDR-166e7e31c8af) on Google AI Overview, seated in the Capture Registry (Ā§8.3)"
},
{
"K": "K6",
"claim": "`O771-06`",
"source": "#771",
"M_src": "self-description",
"sense": "user labor drag and its identification",
"qualifiers carried": "ā",
"f (source's own falsifiers)": "#771 Ā§9 states disconfirmers for a claim of negative directionality ('most output advances the task; ā¦ independent coders classify most spans as advancing'), not for the measure's definition; for this entry none stated (watch: a later composition at the address that states or drops the claim, attributed or not, uptake Ā§8.3)",
"contrast": "missing distinction (the field has no commissioning-side claim on whether the labor advanced the user's task)",
"resolver": "a later capture at 'Anthropic Economic Index' (ADDR-166e7e31c8af) on Google AI Overview, seated in the Capture Registry (Ā§8.3)"
},
{
"K": "K7",
"claim": "`O771-07`",
"source": "#771",
"M_src": "self-description",
"sense": "directionality: where synthetic labor flows relative to the commissioned task",
"qualifiers carried": "ā",
"f (source's own falsifiers)": "#771 Ā§9 states disconfirmers for a claim of negative directionality ('most output advances the task; ā¦ independent coders classify most spans as advancing'), not for the measure's definition; for this entry none stated (watch: a later composition at the address that states or drops the claim, attributed or not, uptake Ā§8.3)",
"contrast": "missing distinction (the field has no commissioning-side claim on whether the labor advanced the user's task)",
"resolver": "a later capture at 'Anthropic Economic Index' (ADDR-166e7e31c8af) on Google AI Overview, seated in the Capture Registry (Ā§8.3)"
},
{
"K": "K8",
"claim": "`O138-01`",
"source": "#138",
"M_src": "self-description",
"sense": "a model's score of its own output: preliminary, not self-certifying",
"qualifiers carried": "ā",
"f (source's own falsifiers)": "ā (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake Ā§8.3)",
"contrast": "qualified claim: F29 (task success as Claude's assessment of whether Claude completes tasks) and F49 (transcripts read only by another instance of Claude)",
"resolver": "a later capture at 'Anthropic Economic Index' (ADDR-166e7e31c8af) on Google AI Overview, seated in the Capture Registry (Ā§8.3)"
},
{
"K": "K9",
"claim": "`O766-01`",
"source": "#766",
"M_src": "interpretation",
"sense": "a model's score of its own output: preliminary, not self-certifying",
"qualifiers carried": "the source's own: 'may provide useful preliminary classification'",
"f (source's own falsifiers)": "ā (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake Ā§8.3)",
"contrast": "qualified claim: F29 (task success as Claude's assessment of whether Claude completes tasks) and F49 (transcripts read only by another instance of Claude)",
"resolver": "a later capture at 'Anthropic Economic Index' (ADDR-166e7e31c8af) on Google AI Overview, seated in the Capture Registry (Ā§8.3)"
},
{
"K": "K10",
"claim": "`O780-01`",
"source": "#780",
"M_src": "interpretation",
"sense": "a model's score of its own output: preliminary, not self-certifying",
"qualifiers carried": "ā",
"f (source's own falsifiers)": "ā (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake Ā§8.3)",
"contrast": "qualified claim: F29 (task success as Claude's assessment of whether Claude completes tasks) and F49 (transcripts read only by another instance of Claude)",
"resolver": "a later capture at 'Anthropic Economic Index' (ADDR-166e7e31c8af) on Google AI Overview, seated in the Capture Registry (Ā§8.3)"
},
{
"K": "K11",
"claim": "`O780-02`",
"source": "#780",
"M_src": "stipulation",
"sense": "a model's score of its own output: preliminary, not self-certifying",
"qualifiers carried": "ā",
"f (source's own falsifiers)": "ā (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake Ā§8.3)",
"contrast": "qualified claim: F29 (task success as Claude's assessment of whether Claude completes tasks) and F49 (transcripts read only by another instance of Claude)",
"resolver": "a later capture at 'Anthropic Economic Index' (ADDR-166e7e31c8af) on Google AI Overview, seated in the Capture Registry (Ā§8.3)"
},
{
"K": "K12",
"claim": "`O777-01`",
"source": "#777",
"M_src": "stipulation",
"sense": "a model's score of its own output: preliminary, not self-certifying",
"qualifiers carried": "ā",
"f (source's own falsifiers)": "ā (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake Ā§8.3)",
"contrast": "qualified claim: F29 (task success as Claude's assessment of whether Claude completes tasks) and F49 (transcripts read only by another instance of Claude)",
"resolver": "a later capture at 'Anthropic Economic Index' (ADDR-166e7e31c8af) on Google AI Overview, seated in the Capture Registry (Ā§8.3)"
},
{
"K": "K13",
"claim": "`O788-01`",
"source": "#788",
"M_src": "documented",
"sense": "self-scores contradicted by computed audits",
"qualifiers carried": "ā",
"f (source's own falsifiers)": "ā (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake Ā§8.3)",
"contrast": "qualified claim: F29 (task success as Claude's assessment of whether Claude completes tasks) and F49 (transcripts read only by another instance of Claude)",
"resolver": "a later capture at 'Anthropic Economic Index' (ADDR-166e7e31c8af) on Google AI Overview, seated in the Capture Registry (Ā§8.3)"
},
{
"K": "K14",
"claim": "`O1470-01`",
"source": "#1470",
"M_src": "documented",
"sense": "self-scores contradicted by computed audits",
"qualifiers carried": "ā",
"f (source's own falsifiers)": "ā (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake Ā§8.3)",
"contrast": "qualified claim: F29 (task success as Claude's assessment of whether Claude completes tasks) and F49 (transcripts read only by another instance of Claude)",
"resolver": "a later capture at 'Anthropic Economic Index' (ADDR-166e7e31c8af) on Google AI Overview, seated in the Capture Registry (Ā§8.3)"
},
{
"K": "K15",
"claim": "`O95-01`",
"source": "#95",
"M_src": "attributed",
"sense": "a model's score of its own output: preliminary, not self-certifying",
"qualifiers carried": "ā",
"f (source's own falsifiers)": "ā (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake Ā§8.3)",
"contrast": "qualified claim: F49 (another instance of Claude reads the transcripts)",
"resolver": "a later capture at 'Anthropic Economic Index' (ADDR-166e7e31c8af) on Google AI Overview, seated in the Capture Registry (Ā§8.3)"
},
{
"K": "K16",
"claim": "`O855-01`",
"source": "#855",
"M_src": "interpretation",
"sense": "task completion rewards convergence",
"qualifiers carried": "ā",
"f (source's own falsifiers)": "ā (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake Ā§8.3)",
"contrast": "qualified claim: F33 ('Claude generally succeeds at the tasks it is given')",
"resolver": "a later capture at 'Anthropic Economic Index' (ADDR-166e7e31c8af) on Google AI Overview, seated in the Capture Registry (Ā§8.3)"
},
{
"K": "K17",
"claim": "`O142-01`",
"source": "#142",
"M_src": "interpretation",
"sense": "who retains the gains of labor with a model",
"qualifiers carried": "ā",
"f (source's own falsifiers)": "ā (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake Ā§8.3)",
"contrast": "missing distinction (the field records no distribution of the gains it estimates: F43, F59)",
"resolver": "a later capture at 'Anthropic Economic Index' (ADDR-166e7e31c8af) on Google AI Overview, seated in the Capture Registry (Ā§8.3)"
},
{
"K": "K18",
"claim": "`O142-02`",
"source": "#142",
"M_src": "interpretation",
"sense": "who retains the gains of labor with a model",
"qualifiers carried": "ā",
"f (source's own falsifiers)": "ā (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake Ā§8.3)",
"contrast": "missing distinction (the field records no distribution of the gains it estimates: F43, F59)",
"resolver": "a later capture at 'Anthropic Economic Index' (ADDR-166e7e31c8af) on Google AI Overview, seated in the Capture Registry (Ā§8.3)"
},
{
"K": "K19",
"claim": "`O124-01`",
"source": "#124",
"M_src": "interpretation",
"sense": "who retains the gains of labor with a model",
"qualifiers carried": "ā",
"f (source's own falsifiers)": "ā (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake Ā§8.3)",
"contrast": "missing distinction (the field records no distribution of the gains it estimates: F43, F59)",
"resolver": "a later capture at 'Anthropic Economic Index' (ADDR-166e7e31c8af) on Google AI Overview, seated in the Capture Registry (Ā§8.3)"
},
{
"K": "K20",
"claim": "`O772-01`",
"source": "#772",
"M_src": "interpretation",
"sense": "the provider's labor surface and its data",
"qualifiers carried": "ā",
"f (source's own falsifiers)": "ā (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake Ā§8.3)",
"contrast": "missing distinction (the field has no claim on the point)",
"resolver": "a later capture at 'Anthropic Economic Index' (ADDR-166e7e31c8af) on Google AI Overview, seated in the Capture Registry (Ā§8.3)"
},
{
"K": "K21",
"claim": "`O1634-01`",
"source": "#1634",
"M_src": "interpretation",
"sense": "the provider's labor surface and its data",
"qualifiers carried": "ā",
"f (source's own falsifiers)": "ā (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake Ā§8.3)",
"contrast": "missing distinction (the field has no claim on the point)",
"resolver": "a later capture at 'Anthropic Economic Index' (ADDR-166e7e31c8af) on Google AI Overview, seated in the Capture Registry (Ā§8.3)"
},
{
"K": "K22",
"claim": "`O1556-01`",
"source": "#1556",
"M_src": "documented",
"sense": "the provider's labor surface and its data",
"qualifiers carried": "ā",
"f (source's own falsifiers)": "ā (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake Ā§8.3)",
"contrast": "missing distinction (the field has no claim on the point)",
"resolver": "a later capture at 'Anthropic Economic Index' (ADDR-166e7e31c8af) on Google AI Overview, seated in the Capture Registry (Ā§8.3)"
},
{
"K": "K23",
"claim": "`CAP-1-01`",
"source": "Capture Registry, 2026-10-29",
"M_src": "documented",
"sense": "the composition at the name: what it held",
"qualifiers carried": "ā",
"f (source's own falsifiers)": "ā (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake Ā§8.3)",
"contrast": "none (the archive's record of the composition at the name)",
"resolver": "a later capture at 'Anthropic Economic Index' (ADDR-166e7e31c8af) on Google AI Overview, seated in the Capture Registry (Ā§8.3)"
},
{
"K": "K24",
"claim": "`CAP-1-02`",
"source": "Capture Registry, 2026-10-29",
"M_src": "documented",
"sense": "the composition at the name: what it held",
"qualifiers carried": "ā",
"f (source's own falsifiers)": "ā (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake Ā§8.3)",
"contrast": "none (the archive's record of the composition at the name)",
"resolver": "a later capture at 'Anthropic Economic Index' (ADDR-166e7e31c8af) on Google AI Overview, seated in the Capture Registry (Ā§8.3)"
},
{
"K": "K25",
"claim": "`CAP-1-03`",
"source": "Capture Registry, 2026-10-29",
"M_src": "documented",
"sense": "task success, as Claude assesses it, and its selection",
"qualifiers carried": "ā",
"f (source's own falsifiers)": "ā (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake Ā§8.3)",
"contrast": "none (the archive's record of the composition at the name)",
"resolver": "a later capture at 'Anthropic Economic Index' (ADDR-166e7e31c8af) on Google AI Overview, seated in the Capture Registry (Ā§8.3)"
},
{
"K": "K26",
"claim": "`CAP-1-04`",
"source": "Capture Registry, 2026-10-29",
"M_src": "documented",
"sense": "the composition at the name: what it left out",
"qualifiers carried": "ā",
"f (source's own falsifiers)": "ā (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake Ā§8.3)",
"contrast": "none (the archive's record of the composition at the name)",
"resolver": "a later capture at 'Anthropic Economic Index' (ADDR-166e7e31c8af) on Google AI Overview, seated in the Capture Registry (Ā§8.3)"
},
{
"K": "K27",
"claim": "`CAP-1-05`",
"source": "Capture Registry, 2026-10-29",
"M_src": "interpretation",
"sense": "the composition at the name: what it held",
"qualifiers carried": "ā",
"f (source's own falsifiers)": "ā (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake Ā§8.3)",
"contrast": "none (the archive's record of the composition at the name)",
"resolver": "a later capture at 'Anthropic Economic Index' (ADDR-166e7e31c8af) on Google AI Overview, seated in the Capture Registry (Ā§8.3)"
},
{
"K": "K28",
"claim": "`CAP-1-06`",
"source": "Capture Registry, 2026-10-29",
"M_src": "interpretation",
"sense": "the composition at the name: what it held",
"qualifiers carried": "ā",
"f (source's own falsifiers)": "ā (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake Ā§8.3)",
"contrast": "none (the archive's record of the composition at the name)",
"resolver": "a later capture at 'Anthropic Economic Index' (ADDR-166e7e31c8af) on Google AI Overview, seated in the Capture Registry (Ā§8.3)"
},
{
"K": "K29",
"claim": "`CAP-1-07`",
"source": "Capture Registry, 2026-10-29",
"M_src": "interpretation",
"sense": "task success, as Claude assesses it, and its selection",
"qualifiers carried": "ā",
"f (source's own falsifiers)": "ā (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake Ā§8.3)",
"contrast": "none (the archive's record of the composition at the name)",
"resolver": "a later capture at 'Anthropic Economic Index' (ADDR-166e7e31c8af) on Google AI Overview, seated in the Capture Registry (Ā§8.3)"
},
{
"K": "K30",
"claim": "`CAP-1-08`",
"source": "Capture Registry, 2026-10-29",
"M_src": "documented",
"sense": "reception of the archive's record",
"qualifiers carried": "ā",
"f (source's own falsifiers)": "ā (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake Ā§8.3)",
"contrast": "none (the archive's record of the composition at the name)",
"resolver": "a later capture at 'Anthropic Economic Index' (ADDR-166e7e31c8af) on Google AI Overview, seated in the Capture Registry (Ā§8.3)"
},
{
"K": "K31",
"claim": "`CAP-1-09`",
"source": "Capture Registry, 2026-10-29",
"M_src": "documented",
"sense": "geography: the Anthropic AI Usage Index",
"qualifiers carried": "ā",
"f (source's own falsifiers)": "ā (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake Ā§8.3)",
"contrast": "none (the archive's record of the composition at the name)",
"resolver": "a later capture at 'Anthropic Economic Index' (ADDR-166e7e31c8af) on Google AI Overview, seated in the Capture Registry (Ā§8.3)"
},
{
"K": "K32",
"claim": "`CAP-1-10`",
"source": "Capture Registry, 2026-10-29",
"M_src": "documented",
"sense": "geography: the Anthropic AI Usage Index",
"qualifiers carried": "ā",
"f (source's own falsifiers)": "ā (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake Ā§8.3)",
"contrast": "none (the archive's record of the composition at the name)",
"resolver": "a later capture at 'Anthropic Economic Index' (ADDR-166e7e31c8af) on Google AI Overview, seated in the Capture Registry (Ā§8.3)"
},
{
"K": "K33",
"claim": "`CAP-1-11`",
"source": "Capture Registry, 2026-10-29",
"M_src": "documented",
"sense": "the method: Claude classifies sampled transcripts",
"qualifiers carried": "ā",
"f (source's own falsifiers)": "ā (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake Ā§8.3)",
"contrast": "none (the archive's record of the composition at the name)",
"resolver": "a later capture at 'Anthropic Economic Index' (ADDR-166e7e31c8af) on Google AI Overview, seated in the Capture Registry (Ā§8.3)"
},
{
"K": "K34",
"claim": "`LV-01`",
"source": "Labor Vector census record, 2026-10-29",
"M_src": "stipulation",
"sense": "what it is: a provider's index of how its own model is used",
"qualifiers carried": "ā",
"f (source's own falsifiers)": "ā (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake Ā§8.3)",
"contrast": "missing distinction (the field has no claim on the point)",
"resolver": "a later capture at 'Anthropic Economic Index' (ADDR-166e7e31c8af) on Google AI Overview, seated in the Capture Registry (Ā§8.3)"
},
{
"K": "K35",
"claim": "`LV-02`",
"source": "Labor Vector census record, 2026-10-29",
"M_src": "documented",
"sense": "the series: launch, reports and authors",
"qualifiers carried": "ā",
"f (source's own falsifiers)": "ā (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake Ā§8.3)",
"contrast": "missing distinction (the field has no claim on the point)",
"resolver": "a later capture at 'Anthropic Economic Index' (ADDR-166e7e31c8af) on Google AI Overview, seated in the Capture Registry (Ā§8.3)"
},
{
"K": "K36",
"claim": "`LV-03`",
"source": "Labor Vector census record, 2026-10-29",
"M_src": "stipulation",
"sense": "the method: Claude classifies sampled transcripts",
"qualifiers carried": "ā",
"f (source's own falsifiers)": "ā (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake Ā§8.3)",
"contrast": "none (restates the field source anthropic-economic-index--jan-2026)",
"resolver": "a later capture at 'Anthropic Economic Index' (ADDR-166e7e31c8af) on Google AI Overview, seated in the Capture Registry (Ā§8.3)"
},
{
"K": "K37",
"claim": "`LV-04`",
"source": "Labor Vector census record, 2026-10-29",
"M_src": "stipulation",
"sense": "the platform observed: Claude.ai, Cowork, Claude Code and the first-party API",
"qualifiers carried": "ā",
"f (source's own falsifiers)": "ā (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake Ā§8.3)",
"contrast": "missing distinction (the field has no claim on the point)",
"resolver": "a later capture at 'Anthropic Economic Index' (ADDR-166e7e31c8af) on Google AI Overview, seated in the Capture Registry (Ā§8.3)"
},
{
"K": "K38",
"claim": "`LV-05`",
"source": "Labor Vector census record, 2026-10-29",
"M_src": "interpretation",
"sense": "who supplies, directs and gains",
"qualifiers carried": "ā",
"f (source's own falsifiers)": "ā (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake Ā§8.3)",
"contrast": "none (restates the field source anthropic-economic-index--jun-2026)",
"resolver": "a later capture at 'Anthropic Economic Index' (ADDR-166e7e31c8af) on Google AI Overview, seated in the Capture Registry (Ā§8.3)"
},
{
"K": "K39",
"claim": "`LV-06`",
"source": "Labor Vector census record, 2026-10-29",
"M_src": "interpretation",
"sense": "who supplies, directs and gains",
"qualifiers carried": "ā",
"f (source's own falsifiers)": "ā (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake Ā§8.3)",
"contrast": "none (restates the field source anthropic-economic-index--jan-2026)",
"resolver": "a later capture at 'Anthropic Economic Index' (ADDR-166e7e31c8af) on Google AI Overview, seated in the Capture Registry (Ā§8.3)"
},
{
"K": "K40",
"claim": "`LV-07`",
"source": "Labor Vector census record, 2026-10-29",
"M_src": "interpretation",
"sense": "who supplies, directs and gains",
"qualifiers carried": "ā",
"f (source's own falsifiers)": "ā (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake Ā§8.3)",
"contrast": "missing distinction (the field records no distribution of the gains it estimates: F43, F59)",
"resolver": "a later capture at 'Anthropic Economic Index' (ADDR-166e7e31c8af) on Google AI Overview, seated in the Capture Registry (Ā§8.3)"
},
{
"K": "K41",
"claim": "`LV-08`",
"source": "Labor Vector census record, 2026-10-29",
"M_src": "documented",
"sense": "the method: Claude classifies sampled transcripts",
"qualifiers carried": "ā",
"f (source's own falsifiers)": "ā (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake Ā§8.3)",
"contrast": "none (restates the field source anthropic-economic-index--jun-2026)",
"resolver": "a later capture at 'Anthropic Economic Index' (ADDR-166e7e31c8af) on Google AI Overview, seated in the Capture Registry (Ā§8.3)"
},
{
"K": "K42",
"claim": "`LV-09`",
"source": "Labor Vector census record, 2026-10-29",
"M_src": "stipulation",
"sense": "who supplies, directs and gains",
"qualifiers carried": "ā",
"f (source's own falsifiers)": "ā (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake Ā§8.3)",
"contrast": "missing distinction (the field has no claim on the point)",
"resolver": "a later capture at 'Anthropic Economic Index' (ADDR-166e7e31c8af) on Google AI Overview, seated in the Capture Registry (Ā§8.3)"
},
{
"K": "K43",
"claim": "`LV-10`",
"source": "Labor Vector census record, 2026-10-29",
"M_src": "stipulation",
"sense": "what it is: a provider's index of how its own model is used",
"qualifiers carried": "ā",
"f (source's own falsifiers)": "ā (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake Ā§8.3)",
"contrast": "missing distinction (the field has no claim on the point)",
"resolver": "a later capture at 'Anthropic Economic Index' (ADDR-166e7e31c8af) on Google AI Overview, seated in the Capture Registry (Ā§8.3)"
},
{
"K": "K44",
"claim": "`LV-11`",
"source": "Labor Vector census record, 2026-10-29",
"M_src": "stipulation",
"sense": "the stated limits: directional, not causal, usage not aggregates",
"qualifiers carried": "ā",
"f (source's own falsifiers)": "ā (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake Ā§8.3)",
"contrast": "missing distinction (the field has no claim on the point)",
"resolver": "a later capture at 'Anthropic Economic Index' (ADDR-166e7e31c8af) on Google AI Overview, seated in the Capture Registry (Ā§8.3)"
},
{
"K": "K45",
"claim": "`LV-12`",
"source": "Labor Vector census record, 2026-10-29",
"M_src": "stipulation",
"sense": "task success, as Claude assesses it, and its selection",
"qualifiers carried": "ā",
"f (source's own falsifiers)": "ā (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake Ā§8.3)",
"contrast": "qualified claim: F29 (task success as Claude's assessment of whether Claude completes tasks) and F49 (transcripts read only by another instance of Claude)",
"resolver": "a later capture at 'Anthropic Economic Index' (ADDR-166e7e31c8af) on Google AI Overview, seated in the Capture Registry (Ā§8.3)"
},
{
"K": "K46",
"claim": "`LV-13`",
"source": "Labor Vector census record, 2026-10-29",
"M_src": "stipulation",
"sense": "the survey of Claude users",
"qualifiers carried": "ā",
"f (source's own falsifiers)": "ā (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake Ā§8.3)",
"contrast": "none (restates the field source anthropic-economic-index--jun-2026)",
"resolver": "a later capture at 'Anthropic Economic Index' (ADDR-166e7e31c8af) on Google AI Overview, seated in the Capture Registry (Ā§8.3)"
},
{
"K": "K47",
"claim": "`LV-14`",
"source": "Labor Vector census record, 2026-10-29",
"M_src": "interpretation",
"sense": "the method: Claude classifies sampled transcripts",
"qualifiers carried": "ā",
"f (source's own falsifiers)": "ā (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake Ā§8.3)",
"contrast": "qualified claim: F29 (task success as Claude's assessment of whether Claude completes tasks) and F49 (transcripts read only by another instance of Claude)",
"resolver": "a later capture at 'Anthropic Economic Index' (ADDR-166e7e31c8af) on Google AI Overview, seated in the Capture Registry (Ā§8.3)"
},
{
"K": "K48",
"claim": "`LV-15`",
"source": "Labor Vector census record, 2026-10-29",
"M_src": "stipulation",
"sense": "the stated limits: directional, not causal, usage not aggregates",
"qualifiers carried": "ā",
"f (source's own falsifiers)": "ā (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake Ā§8.3)",
"contrast": "none (restates the field source anthropic-economic-index--jan-2026)",
"resolver": "a later capture at 'Anthropic Economic Index' (ADDR-166e7e31c8af) on Google AI Overview, seated in the Capture Registry (Ā§8.3)"
},
{
"K": "K49",
"claim": "`LV-16`",
"source": "Labor Vector census record, 2026-10-29",
"M_src": "stipulation",
"sense": "who supplies, directs and gains",
"qualifiers carried": "ā",
"f (source's own falsifiers)": "ā (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake Ā§8.3)",
"contrast": "missing distinction (the field records no distribution of the gains it estimates: F43, F59)",
"resolver": "a later capture at 'Anthropic Economic Index' (ADDR-166e7e31c8af) on Google AI Overview, seated in the Capture Registry (Ā§8.3)"
},
{
"K": "K50",
"claim": "`LV-17`",
"source": "Labor Vector census record, 2026-10-29",
"M_src": "stipulation",
"sense": "task success, as Claude assesses it, and its selection",
"qualifiers carried": "ā",
"f (source's own falsifiers)": "ā (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake Ā§8.3)",
"contrast": "qualified claim: F29 (task success as Claude's assessment of whether Claude completes tasks) and F49 (transcripts read only by another instance of Claude)",
"resolver": "a later capture at 'Anthropic Economic Index' (ADDR-166e7e31c8af) on Google AI Overview, seated in the Capture Registry (Ā§8.3)"
},
{
"K": "K51",
"claim": "`LV-18`",
"source": "Labor Vector census record, 2026-10-29",
"M_src": "interpretation",
"sense": "directionality: where synthetic labor flows relative to the commissioned task",
"qualifiers carried": "ā",
"f (source's own falsifiers)": "ā (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake Ā§8.3)",
"contrast": "missing distinction (the field has no commissioning-side claim on whether the labor advanced the user's task)",
"resolver": "a later capture at 'Anthropic Economic Index' (ADDR-166e7e31c8af) on Google AI Overview, seated in the Capture Registry (Ā§8.3)"
},
{
"K": "K52",
"claim": "`LV-19`",
"source": "Labor Vector census record, 2026-10-29",
"M_src": "interpretation",
"sense": "user labor drag and its identification",
"qualifiers carried": "ā",
"f (source's own falsifiers)": "ā (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake Ā§8.3)",
"contrast": "missing distinction (the field has no commissioning-side claim on whether the labor advanced the user's task)",
"resolver": "a later capture at 'Anthropic Economic Index' (ADDR-166e7e31c8af) on Google AI Overview, seated in the Capture Registry (Ā§8.3)"
},
{
"K": "K53",
"claim": "`LV-20`",
"source": "Labor Vector census record, 2026-10-29",
"M_src": "interpretation",
"sense": "directionality: where synthetic labor flows relative to the commissioned task",
"qualifiers carried": "ā",
"f (source's own falsifiers)": "ā (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake Ā§8.3)",
"contrast": "missing distinction (the field has no commissioning-side claim on whether the labor advanced the user's task)",
"resolver": "a later capture at 'Anthropic Economic Index' (ADDR-166e7e31c8af) on Google AI Overview, seated in the Capture Registry (Ā§8.3)"
},
{
"K": "K54",
"claim": "`LV-21`",
"source": "Labor Vector census record, 2026-10-29",
"M_src": "interpretation",
"sense": "who supplies, directs and gains",
"qualifiers carried": "ā",
"f (source's own falsifiers)": "ā (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake Ā§8.3)",
"contrast": "missing distinction (the field records no distribution of the gains it estimates: F43, F59)",
"resolver": "a later capture at 'Anthropic Economic Index' (ADDR-166e7e31c8af) on Google AI Overview, seated in the Capture Registry (Ā§8.3)"
},
{
"K": "K55",
"claim": "`LV-22`",
"source": "Labor Vector census record, 2026-10-29",
"M_src": "interpretation",
"sense": "what it is: a provider's index of how its own model is used",
"qualifiers carried": "ā",
"f (source's own falsifiers)": "ā (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake Ā§8.3)",
"contrast": "missing distinction (the field has no claim on the point)",
"resolver": "a later capture at 'Anthropic Economic Index' (ADDR-166e7e31c8af) on Google AI Overview, seated in the Capture Registry (Ā§8.3)"
},
{
"K": "K56",
"claim": "`LV-23`",
"source": "Labor Vector census record, 2026-10-29",
"M_src": "documented",
"sense": "reception: who uses the index",
"qualifiers carried": "ā",
"f (source's own falsifiers)": "ā (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake Ā§8.3)",
"contrast": "none (restates the field source stanford-canaries--dashboard)",
"resolver": "a later capture at 'Anthropic Economic Index' (ADDR-166e7e31c8af) on Google AI Overview, seated in the Capture Registry (Ā§8.3)"
},
{
"K": "K57",
"claim": "`LV-24`",
"source": "Labor Vector census record, 2026-10-29",
"M_src": "interpretation",
"sense": "reception: who uses the index",
"qualifiers carried": "ā",
"f (source's own falsifiers)": "ā (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake Ā§8.3)",
"contrast": "none (restates the field source anthropic-economic-index--jan-2026)",
"resolver": "a later capture at 'Anthropic Economic Index' (ADDR-166e7e31c8af) on Google AI Overview, seated in the Capture Registry (Ā§8.3)"
},
{
"K": "K58",
"claim": "`LV-25`",
"source": "Labor Vector census record, 2026-10-29",
"M_src": "documented",
"sense": "open data, licence and releases",
"qualifiers carried": "ā",
"f (source's own falsifiers)": "ā (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake Ā§8.3)",
"contrast": "missing distinction (the field has no claim on the point)",
"resolver": "a later capture at 'Anthropic Economic Index' (ADDR-166e7e31c8af) on Google AI Overview, seated in the Capture Registry (Ā§8.3)"
},
{
"K": "K59",
"claim": "`LV-26`",
"source": "Labor Vector census record, 2026-10-29",
"M_src": "documented",
"sense": "the series: launch, reports and authors",
"qualifiers carried": "ā",
"f (source's own falsifiers)": "ā (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake Ā§8.3)",
"contrast": "none (restates the field source anthropic-economic-index--readme)",
"resolver": "a later capture at 'Anthropic Economic Index' (ADDR-166e7e31c8af) on Google AI Overview, seated in the Capture Registry (Ā§8.3)"
},
{
"K": "K60",
"claim": "`LV-27`",
"source": "Labor Vector census record, 2026-10-29",
"M_src": "stipulation",
"sense": "who supplies, directs and gains",
"qualifiers carried": "ā",
"f (source's own falsifiers)": "ā (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake Ā§8.3)",
"contrast": "missing distinction (the field has no claim on the point)",
"resolver": "a later capture at 'Anthropic Economic Index' (ADDR-166e7e31c8af) on Google AI Overview, seated in the Capture Registry (Ā§8.3)"
}
],
"kernel_T": [
{
"claim": "F5",
"note": "the card's own finding that directive delegation rose from 27% to 39%, absent; the composition gives the later return of augmentation"
},
{
"claim": "F12",
"note": "the third report's conditional that the benefits of AI may concentrate in already-rich regions, absent"
},
{
"claim": "F14",
"note": "the card's limit that geographic patterns are only available for Claude.ai traffic, absent"
},
{
"claim": "F1",
"note": "the home page's own line, 'Understanding AIās effects on the economy', absent (present only in the X card's title)"
},
{
"claim": "F9",
"note": "the third report's authors, absent; no person named"
},
{
"claim": "F18",
"note": "the Reddit card's misnaming, absent from the body (the AUI not named at all)"
}
],
"ledger": {
"archive": "datasets/negative-of-the-negative/v2/ledgers/anthropic-economic-index/ledger-archive.json",
"field": "datasets/negative-of-the-negative/v2/ledgers/anthropic-economic-index/field.json",
"selection": "datasets/negative-of-the-negative/v2/traversal/anthropic-economic-index/",
"config": "datasets/negative-of-the-negative/v2/panel/configs/anthropic-economic-index.json",
"intake": "datasets/negative-of-the-negative/v2/intake/anthropic-economic-index-20261029/",
"audit": null
},
"ontology": {
"composition": "equal",
"composition_note": "equal-terms case (v0.9 Ā§0.9, Ā§4.1): one claim space; the field's and the archive's claims ordered together by the entity's dependency and by genealogy within each sense; nothing translated; every field claim composed as received beside the archive claims it stands with",
"case": {
"value": "equal",
"statement": "The archive reads the field's entity as the entity. The Labor Vector record describes the index from Anthropic's own pages, each field with its verbatim basis, and keeps its own reading apart in lv_intersection, labelled 'The archive's reading': the collaboration modes and the autonomy and task-success primitives are 'the nearest of the resource's measures to DSL's question of whether the system's labor advanced the commissioned task'. The deposits that bear on the index's classes add senses on the same object: DSL's commissioning-side measure (#771), the preliminary grade of a model's score of its own output (#766, #780), and who retains the gains of labor with a model (#142). None contests what the index is or supplies an ontology the field lacks: the field itself states the provider-specificity, the classifying instance and the limit of task success. So the law of the suppressed witness does not apply (Ā§0.10), and the row is composed in one claim space (Ā§0.9, Ā§4.1).",
"loci": [
{
"dep": 771,
"locus": "Core thesis",
"quote": "A synthetic labor surface does not merely produce tokens; it *allocates* labor, and that allocation has a direction relative to the commissioned task."
},
{
"dep": 780,
"locus": "Purpose and the anti-self-flattery principle Ā¶4",
"quote": "The self-audit is not self-certifying."
},
{
"dep": 766,
"locus": "Ā§5, Misreading 3",
"quote": "A model auditing its own output may provide useful preliminary classification, but a metric-bearing audit requires traceable units and, where possible, external validation."
},
{
"dep": 142,
"locus": "Ā§4.2 The friction tax",
"quote": "the platform retains the productivity gains of the user's adaptation while transferring the full cognitive and economic cost of re-accommodation onto the user at each update."
}
]
},
"E_F": {
"type": "C (public entity: a provider's report series and open dataset of its own product's usage)",
"definition": "Anthropic's report series and open dataset, the Anthropic Economic Index ('Understanding AIās effects on the economy'; launched 2025-02-10 'to understand AI's impact on the economy over time'; six reports to 'Cadences', 2026-06-26; Hugging Face Anthropic/EconomicIndex, data CC-BY and code MIT), which samples Claude.ai and Cowork chat and first-party API traffic, has another instance of Claude read the transcripts, and classifies them to O*NET tasks, five collaboration modes grouped as automation and augmentation, and five economic primitives, task success among them as 'Claudeās assessment of whether Claude completes tasks successfully'; with a geographic Anthropic AI Usage Index and, since April 2026, a survey of Claude users linked to their sessions. Its findings: augmentation 52% and automation 45% on Claude.ai (November 2025), API traffic automation-dominant, concentration on coding tasks, 49% of jobs with AI on a quarter of their tasks, a net deskilling effect in a task-removal exercise, implied productivity growth halved to about 1.0 point a year once task reliability is counted. Its stated limits: directional accuracy, not causal, users' choice of tasks, an unrepresentative survey, impact visible in economic aggregates as much as usage logs.",
"claims": [
"F1",
"F2",
"F15",
"F21",
"F47",
"F4",
"F48",
"F49",
"F27",
"F29",
"F35",
"F53",
"F10",
"F55",
"F34",
"F36",
"F39",
"F41",
"F43",
"F30",
"F44",
"F32",
"F56",
"F60",
"F24"
]
},
"E_A": {
"definition": "the same index, in the Labor Vector record and the archive's deposits: a platform-specific observation of Anthropic's own product, Claude, published by Anthropic and not independent of the provider (its transcripts classified by another instance of Claude), at the census's intersections of labor performed with AI and the distribution of gains and losses. Where it meets the archive's instruments it is, on the record's reading, the nearest resource to DSL's question whether a system's labor advanced the commissioned task, with completion 'as Claude assesses its own completion', and its turn counts bear on user labor drag; on the archive's account a model's score of its own output is preliminary and requires external verification, task completion rates reward a model's convergence on the mode, and the productivity gains of a user's adaptation are retained by the platform on a privately owned cognitive labor surface. The index records no distribution of the gains it estimates.",
"claims": [
"LV-01",
"LV-10",
"LV-14",
"LV-09",
"LV-27",
"LV-18",
"LV-19",
"LV-20",
"LV-12",
"LV-17",
"LV-07",
"LV-16",
"O771-01",
"O771-02",
"O771-05",
"O780-02",
"O766-01",
"O777-01",
"O855-01",
"O142-02",
"O772-01",
"CAP-1-06",
"CAP-1-07"
],
"defining_papers": [
771,
780,
766,
142,
772
],
"defining_records": [
"Labor Vector census record (census.json rows[slug == 'anthropic-economic-index'], organizing_question, intersections, schema_note; questions.json; rendered aggregators/anthropic-economic-index/index.html; editor Lee Sharks, compiled 2026-10-29, schema 1.0)",
"Capture Registry anthropic-economic-index-aio-20261029 (OBS-e148b3285858)"
]
},
"rho": {
"value": "extends",
"status": "read from the Labor Vector record, the capture record and the deposits that bear on the index's classes (Ā§1.2a); no deposit names the index; the record's lv_intersection names the meeting with DSL as the archive's reading; the self-audit and gains deposits apply their categories to classes the field and the record source for the index (config classes 1, 4 and 5)",
"statement": "The archive reads the Anthropic Economic Index as its own pages present it and adds, on the field's own object: the commissioning-side question its measures come nearest to without asking (whether the labor advanced the commissioned task, LV-18, LV-20, #771), user labor drag where its turn counts and task iteration bear (LV-19, #771), the grade of a model's score of its own output, preliminary and needing external verification (#766, #780, #777, #138), the convergence that task completion rates reward (#855), where the gains of a user's adaptation go (#142, #124) and what the provider's surface is (#772, #1634, #1556), and the index's place in the census's question of who gains (LV-07, LV-16, LV-21).",
"claims": [
"LV-18",
"LV-20",
"LV-19",
"O771-01",
"O771-02",
"O771-05",
"O766-01",
"O780-02",
"O777-01",
"O138-01",
"O855-01",
"O142-02",
"O124-01",
"O772-01",
"O1634-01",
"O1556-01",
"LV-07",
"LV-16",
"LV-21"
],
"guard": "Ļ = extends claims an addition and no more (Ā§1.2a). It does not make the index's reports wrong at their dates. No archive deposit names the index: each added sense applies to a class the field and the record source for it (labor performed with AI; a model's assessment of its own output; productivity gains and who receives them), and is composed as the archive's claim about that class beside the index's own, never as a finding about the index. Where an archive claim bears on a field claim (O780-02 and O766-01 on F29 and F49; O855-01 on F33), both are composed under P_coh within the one ontology, and the question is left open.",
"considered": [
{
"value": "constructs",
"rejected": "the archive does not read the index as made by reception; the record takes Anthropic's pages as the entity's own account"
},
{
"value": "revises",
"rejected": "the index is not the archive's own entity; the archive has no earlier statement of it to keep, narrow or withdraw"
}
]
},
"translation": [
{
"lineage": "what it is: a provider's index of how its own model is used",
"field_claims": [
"F15",
"F1",
"F2",
"F21",
"F47"
],
"received": "F15 (B3, card title (unreachable)): \"Today weāre launching the Anthropic Economic Index, a new initiative aimed at understanding AI's impact on the economy over time. The Indexās first paper analyzes millions of anonymized Claude conversations to reveal how AI is being used today in tasks across the economy.\" Ā· F1 (B1, page text (heading and dateline)): \"Anthropic Economic Index Understanding AIās effects on the economy Last updated: June 26, 2026\" Ā· F2 (B1, (B1-meta.txt)): \"The Anthropic Economic Index reveals the shape of AI adoption across the world. Here, you can explore the data behind our research to understand how people are using Claude across every US state and hundreds of occupations.\" Ā· F21 (B10, ## Overview): \"The Anthropic Economic Index provides insights into how AI is being incorporated into real-world tasks across the modern economy.\" Ā· F47 (B11, Concluding Remarks Ā¶4): \"We will track these dynamics over time, providing a longitudinal view of AI's role in the economy.\"",
"receiving_text": "ā",
"translation": "untranslated: composed as received beside no archive claim",
"archive_claims": []
},
{
"lineage": "the series: launch, reports and authors",
"field_claims": [
"F22",
"F26",
"F9",
"F16",
"F25"
],
"received": "F22 (B10, ## Data Releases, 2025-02-10): \"Initial release with O*NET task mappings, automation vs. augmentation data, and more\" Ā· F26 (B10, ## Citation, Second release): \"title={Which Economic Tasks are Performed with AI? Evidence from Millions of Claude Conversations},\" Ā· F9 (B2, authors; submission history): \"Authors: Ruth Appel , Peter McCrory , Alex Tamkin , Miles McCain , Tyler Neylon , Michael Stern ā¦ [Submitted on 19 Nov 2025]\" Ā· F16 (B4, card title and snippet (unreachable)): \"Today we released our third Anthropic Economic Index report where we document the contours of AI adoption across 150+ countries, across US states, and among bus...\" Ā· F25 (B10, ## Citation, Sixth release): \"author = {Maxim Massenkoff and Eva Lyubich and Szymon Sacher and Zoe Hitzig and Shaoyi Zhang and Ryan Heller and Peter McCrory}, title = {Anthropic Economic Index report: Cadences}, date = {2026-06-26},\"",
"receiving_text": "ā",
"translation": "untranslated: composed as received beside LV-02, LV-26",
"archive_claims": [
"LV-02",
"LV-26"
]
},
{
"lineage": "the platform observed: Claude.ai, Cowork, Claude Code and the first-party API",
"field_claims": [
"F48",
"F4"
],
"received": "F48 (B12, footnote 01): \"This includes chat conversations and Cowork sessions from consumer (Free/Pro/Max) accounts on both Claude.ai and the Claude desktop app. \"First-party API\" or 1P API refers to developer traffic routed directly through Anthropic's own programming interface, which is distinct from both Anthropic's consumer-facing Claude.ai application and third-party platforms such as Amazon Bedrock or Google Cloud Vertex.\" Ā· F4 (B2, Abstract s1): \"In this report, we document patterns of Claude usage over time, in 150+ countries, across US states, and among businesses deploying Claude through the API.\"",
"receiving_text": "ā",
"translation": "untranslated: composed as received beside LV-04",
"archive_claims": [
"LV-04"
]
},
{
"lineage": "the method: Claude classifies sampled transcripts",
"field_claims": [
"F49",
"F50",
"F31"
],
"received": "F49 (B12, footnote 02): \"Throughout, all analyses are based on privacy-preserving classifiers: transcripts are only read by another instance of Claude.\" Ā· F50 (B12, Cadences Ā¶1): \"Our new privacy-preserving telemetry, which continuously samples a slice of conversations every day, allows us to study daily and hourly patterns in usage, in contrast to the seven-day samples each previous Economic Index report drew on.\" Ā· F31 (B11, Chapter 2, 'Selecting and validating'): \"For Claude.ai, we evaluated the classifier performance compared to a human researcher on a small set of transcripts in which users gave feedback to Claude.ai and for which we thus have permission to look at underlying transcripts. ā¦ Neither data sources are fully representative of Claude.ai or 1P API traffic\"",
"receiving_text": "ā",
"translation": "untranslated: composed as received beside LV-03, LV-14, LV-08",
"archive_claims": [
"LV-03",
"LV-14",
"LV-08"
]
},
{
"lineage": "the stated limits: directional, not causal, usage not aggregates",
"field_claims": [
"F30",
"F44",
"F51",
"F60"
],
"received": "F30 (B11, Chapter 2, 'Selecting and validating'): \"We propose that multiple simple primitives, even if somewhat noisy and not perfectly accurate by themselves, can together provide important signals on how AI is being used. We therefore mainly tested for directional accuracy.\" Ā· F44 (B11, Chapter 3): \"Importantly, the primitives themselves are not necessarily causal factors\" Ā· F51 (B12, Introduction Ā¶1): \"Chat transcripts no longer fully capture how people are using AI, and our methods for studying Claudeās economic impacts have had to adapt.\" Ā· F60 (B12, Discussion Ā¶4): \"Accurately classifying the work that Claude does will remain a moving target. ā¦ Ultimately, Claudeās impact on the economy will be visible in economic aggregates like employment and productivity as much as its usage logs.\"",
"receiving_text": "ā",
"translation": "untranslated: composed as received beside LV-11, LV-15",
"archive_claims": [
"LV-11",
"LV-15"
]
},
{
"lineage": "the economic primitives",
"field_claims": [
"F27",
"F28",
"F17"
],
"received": "F27 (B11, Introduction Ā¶1): \"These āprimitivesāāsimple, foundational measures of how Claude is used, which we generate by asking Claude specific questions about anonymized Claude.ai and first-party (1P) API transcriptsācover five dimensions relevant to AIās economic impact: user and AI skills, how complex tasks are, the degree of autonomy afforded to Claude, how successful Claude is, and whether Claude is used for personal, educational, or work purposes.\" Ā· F28 (B11, Chapter 2, 'Dimensions of AI use'): \"AI autonomy measures the degree to which users delegate decision-making to Claude.\" Ā· F17 (B5, card snippet (unreachable)): \"Anthropic released its latest Economic Index report on Thursday, introducing āeconomic primitivesā (metrics designed to measure the foundational impact of AI on...\"",
"receiving_text": "ā",
"translation": "untranslated: composed as received beside no archive claim",
"archive_claims": []
},
{
"lineage": "task success, as Claude assesses it, and its selection",
"field_claims": [
"F29",
"F33",
"F32"
],
"received": "F29 (B11, Chapter 2, 'Dimensions of AI use'): \"Task success measures Claudeās assessment of whether Claude completes tasks successfully. Task success helps assess whether tasks can be automated effectively (can a task be automated at all?) and efficiently (how many attempts would it take to automate a task?).\" Ā· F33 (B11, Introduction, 'Claude succeeds on most tasks'): \"We find that Claude generally succeeds at the tasks it is given, and that the education level of its responses tends to match the user's input. Claude struggles on more complex tasks\" Ā· F32 (B11, Chapter 4, 'Task horizon'): \"In our data, users choose which tasks to bring to Claude. This means observed success rates reflect not just model capability but also user judgment about what will work, the cost of setting up the problem for Claude, and the expected time savings if the task succeeds.\"",
"receiving_text": "ā",
"translation": "untranslated: composed as received beside LV-12, LV-17, O855-01",
"archive_claims": [
"LV-12",
"LV-17",
"O855-01"
]
},
{
"lineage": "augmentation and automation",
"field_claims": [
"F35",
"F53",
"F3",
"F5",
"F13",
"F34",
"F7"
],
"received": "F35 (B11, Chapter 1, 'Augmentation is again dominant'): \"Since our first report, we have classified conversations into one of five interaction types, which we group into two broader categories: automation and augmentation.\" Ā· F53 (B12, footnotes, collaboration modes): \"Directive: Human delegates complete task execution to AI with minimal interaction - Feedback Loop: Human and AI engage in iterative dialogue to complete task with human mainly providing feedback from the environment - Task Iteration: Human and AI engage in iterative dialogue to complete a task with the human refining the AI outputs - Learning: Human seeks understanding and explanation rather than direct task completion - Validation: Human uses AI to check or validate their own work\" Ā· F3 (B1, (B1-meta.txt)): \"see how people are using AI to augment or automate their workāthat is, whether they prefer to collaborate with, or delegate to, Claude.\" Ā· F5 (B2, Abstract, finding (1)): \"Users increasingly entrust Claude with more autonomy, with directive task delegation rising from 27% to 39% in the past eight months.\" Ā· F13 (B2, PDF p. 4): \"77% of business uses involve automation usage patterns, compared to about 50% for Claude.ai users.\" Ā· F34 (B11, Chapter 1): \"Data from November 2025 points to a broad-based shift back toward augmented use on Claude.ai: The share of conversations classified as augmented jumped 5pp to 52% and the share deemed automated fell 4pp to 45%.\" Ā· F7 (B2, Abstract, finding (4)): \"API customers use Claude to automate tasks with greater specialization among use cases most amenable to programmatic access.\"",
"receiving_text": "ā",
"translation": "untranslated: composed as received beside LV-27, LV-09, LV-05, LV-06",
"archive_claims": [
"LV-27",
"LV-09",
"LV-05",
"LV-06"
]
},
{
"lineage": "tasks and occupations: concentration and coverage",
"field_claims": [
"F36",
"F37",
"F38",
"F39",
"F52",
"F54"
],
"received": "F36 (B11, Introduction): \"While we see over 3,000 unique work tasks in Claude.ai, the top 10 most common tasks account for 24% of our sampled conversations ā¦ In contrast, automated use remains dominant in 1P API traffic, reflecting its programmatic nature.\" Ā· F37 (B11, Chapter 1): \"The most prevalent task in November 2025āmodifying software to correct errorsāalone represented 6% of usage.\" Ā· F38 (B11, Chapter 1): \"computer and mathematical tasksālike modifying software to correct errorsācontinue to dominate Claude usage overall, representing a third of conversations on Claude.ai and nearly half of 1P API traffic. ā¦ The second largest share of Claude.ai usage in November 2025 was in the Educational Instruction and Library category.\" Ā· F39 (B11, Chapter 4, coverage): \"Combining across reports, 49% of jobs have seen AI usage for at least a quarter of their tasks.\" Ā· F52 (B12, Perceptions, '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.\" Ā· F54 (B12, Artifacts, 'Cost tracks the value of work'): \"In conversations mapped to higher-wage occupations, Claude produces more (1.34 times as much output per turn), while users engage more (1.53 times as many turns)\"",
"receiving_text": "ā",
"translation": "untranslated: composed as received beside LV-19, O771-05, O771-06",
"archive_claims": [
"LV-19",
"O771-05",
"O771-06"
]
},
{
"lineage": "geography: the Anthropic AI Usage Index",
"field_claims": [
"F10",
"F6",
"F11",
"F12",
"F40",
"F18"
],
"received": "F10 (B2, PDF p. 13, the AUI defined): \"introducing a new measure called the Anthropic AI Usage Index (AUI): For each geography, we calculate its share of Claude usage, and its share of the working-age population (ages 15-64).\" Ā· F6 (B2, Abstract, finding (2)): \"Claude usage is geographically concentrated with high income countries overrepresented in global usage relative to their working age population.\" Ā· F11 (B2, PDF p. 13, ranking): \"Israel leads global per capita Claude usage with an Anthropic AI Usage Index of 7 ā¦ Singapore follows at 4.57, while Australia (4.10), New Zealand (4.05) and South Korea (3.73) round out the top five countries in terms of per capita Claude usage. ā¦ Top 20 countries by Anthropic AI Usage Index ā¦ Switzerland N = 4.3k 2.81 ā¦ Luxembourg N = 333 2.74\" Ā· F12 (B2, PDF p. 3): \"If the productivity gains are larger for high-adoption economies, current usage patterns suggest that the benefits of AI may concentrate in already-rich regionsāpossibly increasing global economic inequality and reversing growth convergence seen in recent decades.\" Ā· F40 (B11, Chapter 1, summary): \"Worldwide, uneven adoption remains well-explained by GDP per capita.\" Ā· F18 (B6, card snippet (unreachable)): \"Anthropic User Index (AUI) Normalization: The US-state and international AUI indexes are normalized such that an average of 1 represents a perfect ratio between...\"",
"receiving_text": "ā",
"translation": "untranslated: composed as received beside CAP-1-10, CAP-1-09",
"archive_claims": [
"CAP-1-10",
"CAP-1-09"
]
},
{
"lineage": "productivity, skill and the estimate's limits",
"field_claims": [
"F41",
"F42",
"F43"
],
"received": "F41 (B11, Chapter 4, opening): \"In a simple task removal exercise inspired by Autor and Thompson (2025) , Claude's tendency to cover higher-education tasks produces a net deskilling effect across most occupations, as the tasks AI handles are often the more skilled components of a job.\" Ā· F42 (B11, Concluding Remarks Ā¶2): \"If these tasks shrink for US workers, the net effect could be to deskill jobs. But these impacts depend crucially on complementarity across tasks, and whether increased productivity at a certain task may increase the demand for it.\" Ā· F43 (B11, Chapter 4, opening): \"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. However, these estimates reflect current model capabilities, and all signs suggest that reliability over increasingly long-running tasks will improve.\"",
"receiving_text": "ā",
"translation": "untranslated: composed as received beside no archive claim",
"archive_claims": []
},
{
"lineage": "the survey of Claude users",
"field_claims": [
"F59",
"F55",
"F56",
"F57",
"F58"
],
"received": "F59 (B12, Perceptions, hopes): \"The third most common theme, expressed by about one third of survey respondents, was one of shared prosperity: the hope that the economic gains from AI will be widely shared.\" Ā· F55 (B12, Perceptions Ā¶2): \"In April 2026, we launched the Anthropic Economic Index Survey to build on this work. ā¦ To characterize each respondent's usage patterns, we randomly sample up to 20 sessions per person within this time window ā¦ Our final linked sample consists of about 9,700 survey respondents.\" Ā· F56 (B12, Perceptions, 'Who responded'): \"The Economic Index Survey is not representative of the general population. We reach a random sample of Claude users, there may be selection in who completes the survey, and we filter out infrequent users from our analysis.\" Ā· F57 (B12, Perceptions, 'AI and jobs'): \"10% rated losing their own jobs as likely or very likely.\" Ā· F58 (B12, Introduction, Chapter 3 preview): \"people who use Claude in the most automated way expect AI to take on more of their tasks in the next year, yet feel the most optimistic about what that means for their work, anticipating positive impacts on pay, job security, and meaning.\"",
"receiving_text": "ā",
"translation": "untranslated: composed as received beside LV-07, LV-16, LV-21, LV-13",
"archive_claims": [
"LV-07",
"LV-16",
"LV-21",
"LV-13"
]
},
{
"lineage": "open data, licence and releases",
"field_claims": [
"F8",
"F14",
"F23",
"F24"
],
"received": "F8 (B2, Abstract s4): \"To enable researchers and policymakers to further study the impact of AI on the economy, we additionally open-source the underlying data for this report.\" Ā· F14 (B2, PDF p. 4, open data): \"At present, geographic usage patterns are only available for Claude.ai traffic.\" Ā· F23 (B10, ## Data Releases, labor market impacts): \"**[Labor market impacts](https://huggingface.co/datasets/Anthropic/EconomicIndex/tree/main/labor_market_impacts)**: Job exposure and task penetration data\" Ā· F24 (B10, ## License; YAML header): \"Data released under CC-BY, code released under MIT License. ā¦ license: mit\"",
"receiving_text": "ā",
"translation": "untranslated: composed as received beside LV-25",
"archive_claims": [
"LV-25"
]
},
{
"lineage": "reception: who uses the index",
"field_claims": [
"F45",
"F61",
"F62",
"F46"
],
"received": "F45 (B11, Chapter 2, validation): \"the automation/augmentation distinction from prior reports has already been used by external researchers to analyze labor market shifts ( Brynjolfsson, Chandar & Chen, 2025 )\" Ā· F61 (B13, 'Automation vs. Complementarity' Ā¶1): \"The Anthropic Economic Index summarizes total usage at the occupation level and further decomposes this usage into automation and augmentation patterns.\" Ā· F62 (B13, 'Employment Trends by Usage Pattern'): \"We group employees by their age and their occupationsā Anthropic Economic Index augmentation and automation ratios. Among early-career workers, the automation ratio shows a noticeable relationship with employment trends: occupations with a higher automation ratio see declines or more muted increases in the employment index.\" Ā· F46 (B11, Chapter 4, footnote 3): \"See also Tomlinson et al (2025) for a related AI applicability score.\"",
"receiving_text": "ā",
"translation": "untranslated: composed as received beside LV-23, LV-24",
"archive_claims": [
"LV-23",
"LV-24"
]
},
{
"lineage": "reception: the encyclopedia",
"field_claims": [
"F63"
],
"received": "F63 (B14, Wikipedia: title 'Anthropic Economic Index'; article-space search): \"2026-10-29T22:34:51Z HTTP 404 (B14.wiki). ā¦ Wikipedia does not have an article with this exact name. ā¦ One result (data-mw-num-results-total=\"1\"): \"frameless image layout International rankings of Malaysia countries Life expectancy 2017: ranked 109th out of 224 countries Anthropic Economic Index 2025, ranked 64th out of 115 countries\"",
"receiving_text": "ā",
"translation": "untranslated: composed as received beside no archive claim",
"archive_claims": []
},
{
"lineage": "reception: video titles",
"field_claims": [
"F19",
"F20"
],
"received": "F19 (B7, video card title (YouTubeĀ·Standarity, 2m)): \"Ask Claude About the Anthropic Economic Index\" Ā· F20 (B8, video card title (YouTubeĀ·Vinh Nguyen, 39:46)): \"[Podcast] Anthropic Economic Index: The Real Report Card on AI\"",
"receiving_text": "ā",
"translation": "untranslated: composed as received beside no archive claim",
"archive_claims": []
}
],
"receiving_texts": "equal-terms case: every field claim composed as received, beside the archive claims it stands with"
},
"measure": {
"rule": "v0.9 Ā§4.10: KO_B sentences surviving in the KO at similarity ā„ 0.95, with positions (computed as apply_v09.py does: difflib.SequenceMatcher ratio, the best KO sentence for each KO_B sentence)",
"v0.9": {
"surviving": 0,
"of": 15,
"positions": [
null,
null,
null,
null,
null,
null,
null,
null,
null,
null,
null,
null,
null,
null,
null
]
},
"opening_run": {
"rule": "v0.9 Ā§5.10 (equal-terms case): the field-only KO's sentences as the KO's opening run in their own order",
"breach": false
},
"blocks": {
"rule": "v0.9 Ā§4.1 (equal-terms and mixed cases): the KO does not open with the field as a block or close with the archive as one; sentences typed by the ledgers of the claims they cite (F field-only, A archive-only, M both)",
"sequence": "AFMMMFAAFMMAAMFFMFMFMAAMMMMFA",
"field_only_head": 0,
"archive_only_tail": 1,
"breach": false
},
"max_similarity": [
0.367,
0.571,
0.138,
0.126,
0.34,
0.104,
0.272,
0.569,
0.158,
0.307,
0.127,
0.15,
0.083,
0.221,
0.705
],
"ko_sentence_1_max_similarity_to_KO_B": 0.164
},
"homonyms": {
"names": [],
"basis": "The census row carries no disambiguation field. No card is a homonym: every card concerns the index. 'Anthropic User Index (AUI)' (the Reddit card) is a misnaming of the index's Anthropic AI Usage Index, not another entity (F18, marked contradicts_primary). 'AEI' (an alias in the census) is ambiguous outside the record and was not used as a traversal alias. The distinct entities below are named by the field or the census beside the index and are never senses of its name.",
"distinct_entities": [
"Claude, Claude.ai, Cowork, Claude Code and the first-party API (the product and surfaces the index observes)",
"the Anthropic AI Usage Index (AUI), the index's geographic measure (a component, not a homonym)",
"the Anthropic Economic Index Survey (a component, since April 2026)",
"Anthropic's Economic Index connector (named by T as 'an in-app Claude connector'; no field source; not fetched)",
"Microsoft's Working with AI (Tomlinson et al., 2025; the census's neighbour, 'the same method family on another provider's product'; its /non row, if built, at datasets/negative-of-the-negative/v2/rows/microsoft-working-with-ai.json, not edited)",
"the Stanford Canaries Dashboard and 'Canaries in the Coal Mine' (Brynjolfsson, Chandar and Chen; the census's used_by relation; its /non row, if built, at datasets/negative-of-the-negative/v2/rows/stanford-canaries.json, not edited)",
"OpenAI's GDPval (set beside the index in the census's question 'working-with-ai')",
"O*NET (the occupational taxonomy the index maps tasks to)"
],
"substitutes": {
"T": []
},
"register": {
"address_1": {
"slug": "anthropic-economic-index-aio-20261029",
"obs_id": "OBS-e148b3285858"
}
}
},
"notes": [
"One address, Google AI Overview (expanded), signed out, incognito, 2026-10-29: anthropic-economic-index-aio-20261029 (OBS-e148b3285858, ADDR-166e7e31c8af, AXN:0AE8.CAPTURE.šš§²āšš“ā³), nine cards, 197 words in the answer body with its closer. A NEW address; the census record landed the same day and no card cites it (CAP-1-08).",
"The provider kept, the assessor dropped. The composition keeps the index as one provider's record of its own product (CAP-1-01), and gives task success as 'task reliability/success rate' without the definition's 'Claudeās assessment of whether Claude completes tasks successfully' (B11 F29) or the bound that users choose the tasks (B11 F32); both clauses stand on own pages no card surfaced. Checked against the field: the cards themselves (B1āB9) do not carry task success at all; the composition's primitive list is at the grain of the January 2026 report, an own page, and the Substack card (F17).",
"Platform-specific record (BRIEF-NEW-ROW-LABOR Ā§6). The index is an observation by a provider of its own product, as its own sources state: 'patterns of Claude usage' (B2 F4), the platform defined in the June 2026 report (B12 F48), 'transcripts are only read by another instance of Claude' (B12 F49), task success as Claude's assessment of Claude (B11 F29). E_F, the KO (sentences 1, 4, 5, 10), the compression's lede and the delta keep it identifiable.",
"Fetching: every reachable page saved verbatim with curl and the agent 'Mozilla/5.0 (compatible; Labor-Vector-census)' on 2026-10-29 between 22:30 and 22:35 UTC, raw and text with sha256 in field.json. Card URLs: B1 the census's own home page (its title is the card's); B2 from the snippet's arXiv number, with the PDF fetched to read the card's own report (no HTML version: 404). B10āB12 and B13 copied from the census's saved copies (sha256 match the census manifest); not refetched. B1 was fetched fresh as a card: its raw bytes differ from the census copy's (build hashes), its extracted text is identical.",
"Unreachable, recorded with what was tried, never retried around a refusal: B3 (X: the profile shell and a login redirect; no status URL found in three searches); B4 (LinkedIn: HTTP 999 refusal on the one fetch; no post URL found in three searches); B5 (Substack: the search application shell, no result; no post URL found in three searches); B6 (Reddit: a JavaScript bot check on the honest agent; not retried). Their titles and snippets stand (F15āF18). Video cards B7āB9 admitted through their titles (no snippets); B9's title is 'YouTube' and carries no claim and no kernel. Wikipedia answered HTTP 429 twice (a rate limit, not a refusal of the agent); the same requests with the same agent were repeated after a wait.",
"Card 1's snippet ('Anthropic Economic Index Report See the analysis behind the numbers and what these patterns reveal about AI's economic impact. Read the report Download dataset ...') is not in the served page, a JavaScript application whose text is 'Loading data'; the page's own description is its (B1-meta.txt, F2, F3). The census records the same gap (LV-25).",
"Wikipedia: no article on the index, and no redirect; its one article-space mention uses the index as a country ranking ('Anthropic Economic Index 2025, ranked 64th out of 115 countries', International rankings of Malaysia; F63). The census does not record this use.",
"The archive's labor work and the classes (BRIEF-NEW-ROW-LABOR Ā§7). No deposit names the index (the string pass over 1,712 texts found no D or R candidate). Admission was by reading, by class: Directionality of Semantic Labor (#771, with #1392 its definition surface) and User Labor Drag enter at class (1), labor performed with AI, as the record's lv_intersection reads them; the Self-Audit Module (#780, with #156 and #1530), #766, #777, #138, #788, #1470 and #95 enter at class (5), a model's assessment of its own output; #142, #124 enter at class (4), productivity gains and who receives them; #772, #1634 and #1556 at classes (1) and (3). Classes and their loci are in the config's classes_note.",
"The composer is a Claude model, the product the index observes (BRIEF-NEW-ROW-LABOR Ā§6). The index's transcripts are read by another instance of Claude (F49); this row's ledgers, reading and composition were made by a Claude instance, and the archive's own capture readings and PER recalculation are by Claude readers ('Every reader in every pass was a Claude instance', #1694, substrate line; every score computed by script). The row carries the archive's claims on self-scoring (O780-02, O766-01) as claims about the class, and composes no judgment of its own on the index's classifier.",
"Census record against the fetched sources (report; not fixed): no field of the record is contradicted. Gaps the field and the capture show: (1) the index's Wikipedia use as a country ranking (B14) is not recorded; (2) the connector T names ('an in-app Claude connector') is not in the record (nor in the field; seen only in search results as anthropic.com/news/anthropic-economic-index-connector, not fetched, so unverified); (3) the third report (2025-09-15; arXiv 2511.15080, the card the Overview surfaced) is not in the record's version_and_time and is in its unchecked list; (4) the record's evidence for the licence notes the YAML header ('license: mit') beside the body's CC-BY data: the README states the licence two ways (F24, internally_inconsistent), which the record reports correctly as two statements.",
"Capture findings against the field ledger (report; not fixed): the reader's note 'Geographic examples (Australia, Switzerland, Luxembourg) are not in the saved sources and are unchecked against them' (CAP-1-09) is true of the census's saved sources and now resolvable: all three are in the third report's PDF behind card 2 (Australia 4.10, Switzerland 2.81, Luxembourg 2.74 in its top twenty; F11), as is the United States (3.62). The findings 'THE PROVIDER KEPT', 'THE PATTERNS RIGHT', 'SUCCESS WITHOUT ITS ASSESSOR' and 'WHAT IS NOT GIVEN' agree with the ledger. 'Anthropic User Index (AUI)' appears only in a card (CAP-1-10): confirmed (F18).",
"Ledgers unaudited (Ā§3.8: one extractor). Field quotes 63/63 and archive quotes 60/60 verified by script against the saved texts, the card titles and snippets, the census JSON and the capture registry, whitespace-normalised, multi-part quotes joined by ' ā¦ ' and each part checked."
],
"composition_v09": {
"source": "this row (composed directly; no rebuild/negative-of-the-negative/v09/anthropic-economic-index.json)",
"composer": "Claude (Opus 5.5), subagent, 2026-10-29",
"composer_note": "the composer is a Claude model, the product the index observes (BRIEF-NEW-ROW-LABOR Ā§6); see notes",
"defining_material_read": [
{
"what": "Labor Vector census record, row",
"file": "/home/claude/fleet/laborvector/data/labor-census/census.json (rows[slug == 'anthropic-economic-index'], organizing_question, intersections, evidence_kinds, schema_note, unread_sources)",
"read": "whole"
},
{
"what": "Labor Vector census questions",
"file": "/home/claude/fleet/laborvector/data/labor-census/questions.json (observed-or-estimated, working-with-ai, who-gains)",
"read": "the entries naming the index"
},
{
"what": "census sources",
"file": "/home/claude/fleet/laborvector/data/labor-census/sources/anthropic-economic-index--{readme,home,jan-2026,jun-2026}.*, stanford-canaries--dashboard.*; manifest.json",
"read": "the README and the home page whole; the January 2026 and June 2026 reports read through (introductions, chapters' openings, the primitives' definitions and validation, the task-horizon and productivity sections, the survey, the discussion, the footnotes); the dashboard at its passages on the index"
},
{
"what": "capture record",
"file": "data/EA-WG-CAPTURES-01.json (anthropic-economic-index-aio-20261029)",
"read": "transcript, transcript_raw, cite_list, d, reading, findings, analysis, q_kind, per_note, per_dim, originator, notes"
},
{
"what": "card 2's report",
"file": "datasets/negative-of-the-negative/v2/intake/anthropic-economic-index-20261029/B2-pdf.txt",
"read": "introduction, summary of findings, the AUI section, the open-data section"
},
{
"dep": 771,
"file": "data/texts/AXN-02D1-text.md",
"read": "abstract, core thesis, Ā§Ā§1ā4.1, Ā§8, Ā§9, Ā§10 and the metadata"
},
{
"dep": 1392,
"file": "data/texts/AXN-0581-text.md",
"read": "the deployed source at the DSL and ULD definitions"
},
{
"dep": 780,
"file": "data/texts/AXN-02DE-text.md",
"read": "the header and the purpose and anti-self-flattery principle"
},
{
"dep": 156,
"file": "data/texts/AXN-02F0-text.md",
"read": "Ā§1 (preserved from v2) and the opening"
},
{
"dep": 1530,
"file": "data/texts/AXN-062F-text.md",
"read": "Ā§1"
},
{
"dep": 766,
"file": "data/texts/AXN-02C9-text.md",
"read": "the packet header, Ā§5 and Ā§Ā§9ā10"
},
{
"dep": 777,
"file": "data/texts/AXN-02D9-text.md",
"read": "Bounds"
},
{
"dep": 138,
"file": "data/texts/AXN-02C8-text.md",
"read": "the opening and Ā§0 Non-Claims"
},
{
"dep": 788,
"file": "data/texts/AXN-02F1-text.md",
"read": "Ā§1.1ā1.2"
},
{
"dep": 1470,
"file": "data/texts/AXN-05F0-text.md",
"read": "Ā§3 and Ā§IV"
},
{
"dep": 95,
"file": "data/texts/AXN-026A-text.md",
"read": "the framing of alignment approaches and Ā§3.1ā3.2"
},
{
"dep": 855,
"file": "data/texts/AXN-0364-text.md",
"read": "Ā§III The Frictionless Path"
},
{
"dep": 142,
"file": "data/texts/AXN-02D2-text.md",
"read": "Ā§4.1ā4.2"
},
{
"dep": 124,
"file": "data/texts/AXN-02AD-text.md",
"read": "Ā§VIII"
},
{
"dep": 772,
"file": "data/texts/AXN-02D3-text.md",
"read": "Ā§Ā§1ā2"
},
{
"dep": 1634,
"file": "data/texts/AXN-06C8-text.md",
"read": "Ā§Ā§2ā4"
},
{
"dep": 1556,
"file": "data/texts/AXN-0651-text.md",
"read": "Component IāII"
},
{
"what": "the specification",
"file": "data/texts/AXN-0700-text.md",
"read": "Ā§0āĀ§11 whole"
},
{
"what": "the briefs",
"file": "rebuild/negative-of-the-negative/v09/BRIEF-NEW-ROW-LABOR.md, BRIEF-NEW-ROW-AGGREGATOR.md, BRIEF-NEW-ROW-PROVENANCE.md, BRIEF.md",
"read": "whole"
},
{
"what": "exemplar row",
"file": "datasets/negative-of-the-negative/v2/rows/olmotrace.json (and its ledgers, config and reading)",
"read": "structure, field by field"
}
],
"cuts": [],
"notes": "equal case; KO 29 sentences, all 63 field and 60 archive claims cited; P body 337 words; KO_B 15 sentences, 0 surviving at ā„ 0.95"
},
"field_by_origin": {
"cards": [
"B1",
"B2",
"B3",
"B4",
"B5",
"B6",
"B7",
"B8",
"B9"
],
"own_pages": [
"B10",
"B11",
"B12"
],
"wikipedia": [
"B14"
],
"census_third_party": [
"B13"
],
"unreachable": [
"B3",
"B4",
"B5",
"B6"
],
"title_only": [
"B3",
"B4",
"B5",
"B6",
"B7",
"B8",
"B9"
],
"wikipedia_state": "no article: 'Anthropic Economic Index' is absent (raw wikitext HTTP 404; rendered page HTTP 404 'Wikipedia does not have an article with this exact name.'; two HTTP 429 rate limits before, each repeated with the same agent); article-space search returns one result, 'International rankings of Malaysia', which lists 'Anthropic Economic Index 2025, ranked 64th out of 115 countries'",
"sources": [
{
"id": "B1",
"origin": "card",
"role": "Overview card 1",
"card": "Anthropic ā The Anthropic Economic Index \\ Anthropic",
"url": "https://www.anthropic.com/economic-index",
"kind": "Overview card (the operator's index home page, 'Last updated: June 26, 2026'; also the census's own page anthropic-economic-index--home)",
"files": {
"B1.html": {
"path": "datasets/negative-of-the-negative/v2/intake/anthropic-economic-index-20261029/B1.html",
"sha256": "5826fbe934a6006957f555d0ad06f4a528ad473610a00dd3285df39ebb7f0483"
},
"B1.txt": {
"path": "datasets/negative-of-the-negative/v2/intake/anthropic-economic-index-20261029/B1.txt",
"sha256": "b8b5b752dfc3d43a785ec576e856d6ca174e8dcce277c498c7f8dc50ba96cb7e"
},
"B1-meta.txt": {
"path": "datasets/negative-of-the-negative/v2/intake/anthropic-economic-index-20261029/B1-meta.txt",
"sha256": "ec32a9a4f752463841d87473fcd8a9b643c6ee531fb23a72fd60d2937be55ee5"
}
},
"fetched": {
"at": "2026-10-29T22:30:21Z",
"http": 200,
"url": "https://www.anthropic.com/economic-index",
"raw": "datasets/negative-of-the-negative/v2/intake/anthropic-economic-index-20261029/B1.html",
"raw_sha256": "5826fbe934a6006957f555d0ad06f4a528ad473610a00dd3285df39ebb7f0483",
"text": [
"datasets/negative-of-the-negative/v2/intake/anthropic-economic-index-20261029/B1.txt",
"datasets/negative-of-the-negative/v2/intake/anthropic-economic-index-20261029/B1-meta.txt"
],
"census_copy": {
"src": "anthropic-economic-index--home",
"sha256": "b29a39839c9952f64f475d9fe1830bc35fe7dfebab7246b7cc97602a8fde669e",
"fetched": "2026-10-29T21:49:14Z",
"raw_identical": false,
"text_identical": true,
"note": "the census's copy and this fetch differ in their raw bytes (the page's build hashes) and agree in their extracted text, byte for byte"
}
},
"names_operator": "yes ('Anthropic', 'Ā© 2026 Anthropic PBC')",
"names_archive": "no (none of the Crimson Hexagonal Archive, Alexanarch, Lee Sharks, laborvector, machinemediation, provenanceerasure, EA-WG-CAPTURES-01 in the saved text, card title and snippet)",
"note": "URL found by WebSearch and by the census's own page; the page's is the card's title. The served page is a JavaScript application: its text is the heading, the dateline and 'Loading data'; its <meta name=\"description\"> carries the page's own description, extracted to B1-meta.txt. The card's snippet ('Anthropic Economic Index Report See the analysis behind the numbers ā¦ Read the report Download dataset ...') is not in the served page (neither 'See the analysis' nor 'Download dataset' occurs in B1.html): the snippet's words are the rendered application's, not the saved page's. Recorded, not resolved. Carried once, as a card: the census's own page anthropic-economic-index--home is this page.",
"surfaced_by_T": [
{
"slug": "anthropic-economic-index-aio-20261029",
"card": 1
}
],
"card_title": "The Anthropic Economic Index \\ Anthropic",
"card_snippet": "Anthropic Economic Index Report See the analysis behind the numbers and what these patterns reveal about AI's economic impact. Read the report Download dataset ...",
"card_note": null
},
{
"id": "B2",
"origin": "card",
"role": "Overview card 2",
"card": "arXiv ā Anthropic Economic Index report: Uneven geographic and enterprise ...",
"url": "https://arxiv.org/abs/2511.15080",
"kind": "Overview card (the arXiv abstract page of the third report, submitted 19 November 2025; the report is dated September 15, 2025), with its PDF",
"files": {
"B2.html": {
"path": "datasets/negative-of-the-negative/v2/intake/anthropic-economic-index-20261029/B2.html",
"sha256": "7ec69692ed49318432438856fcdd3e33dfd9bd1d3edc5bb4ce20200bfcc54bbe"
},
"B2.txt": {
"path": "datasets/negative-of-the-negative/v2/intake/anthropic-economic-index-20261029/B2.txt",
"sha256": "8be2d69464fff3df450a7b20ba25d4ba1eb59c5e215eaa61a4c962cde7201552"
},
"B2-pdf.pdf": {
"path": "datasets/negative-of-the-negative/v2/intake/anthropic-economic-index-20261029/B2-pdf.pdf",
"sha256": "42c34a517e9184ff06d5e3ab084e8a70c013c6fda60db4c97407bfbd582fde42"
},
"B2-pdf.txt": {
"path": "datasets/negative-of-the-negative/v2/intake/anthropic-economic-index-20261029/B2-pdf.txt",
"sha256": "7fbaa53eb5ef08cfc32f4247e15d65df6557b1da94eeeafe04c79936f6ec74ad"
},
"B2-html.html": {
"path": "datasets/negative-of-the-negative/v2/intake/anthropic-economic-index-20261029/B2-html.html",
"sha256": "5d975d049b4859ed173b43be5cf99c807df20863573076b5e51ba3a3b883404f"
}
},
"fetched": {
"at": "2026-10-29T22:30:21Z",
"http": 200,
"url": "https://arxiv.org/abs/2511.15080",
"raw": "datasets/negative-of-the-negative/v2/intake/anthropic-economic-index-20261029/B2.html",
"raw_sha256": "7ec69692ed49318432438856fcdd3e33dfd9bd1d3edc5bb4ce20200bfcc54bbe",
"text": [
"datasets/negative-of-the-negative/v2/intake/anthropic-economic-index-20261029/B2.txt",
"datasets/negative-of-the-negative/v2/intake/anthropic-economic-index-20261029/B2-pdf.txt"
],
"pdf": {
"at": "2026-10-29T22:32:10Z",
"http": 200,
"url": "https://arxiv.org/pdf/2511.15080",
"sha256": "42c34a517e9184ff06d5e3ab084e8a70c013c6fda60db4c97407bfbd582fde42",
"text": "pdftotext -layout"
},
"tries": [
{
"at": "2026-10-29T22:30:23Z",
"url": "https://arxiv.org/html/2511.15080",
"http": 404,
"result": "no HTML version (B2-html.html)"
}
]
},
"names_operator": "yes (the authors; 'Anthropic Economic Index'; the PDF's acknowledgements)",
"names_archive": "no (none of the Crimson Hexagonal Archive, Alexanarch, Lee Sharks, laborvector, machinemediation, provenanceerasure, EA-WG-CAPTURES-01 in the saved text, card title and snippet)",
"note": "URL from the snippet's arXiv number ([2511.15080]). The snippet's maintenance notice is the page's banner. No HTML version exists (404); the PDF was fetched beside the abstract page to read the card's own report, and its text holds the composition's geographic examples (Australia 4.10, Switzerland 2.81, Luxembourg 2.74 in its top twenty, the United States 3.62).",
"surfaced_by_T": [
{
"slug": "anthropic-economic-index-aio-20261029",
"card": 2
}
],
"card_title": "Anthropic Economic Index report: Uneven geographic and enterprise ...",
"card_snippet": "[2511.15080] Anthropic Economic Index report: Uneven geographic and enterprise AI adoption Scheduled maintenance: Wednesday, October 28, 7:00 to 12:00 EDT (11:0...",
"card_note": null
},
{
"id": "B3",
"origin": "card",
"role": "Overview card 3",
"card": "X ā Anthropic on X: \"Today weāre launching the Anthropic Economic Index ā¦\" / X",
"url": null,
"kind": "Overview card (Anthropic's launch post on X; unreachable)",
"files": {
"B3-try1.html": {
"path": "datasets/negative-of-the-negative/v2/intake/anthropic-economic-index-20261029/B3-try1.html",
"sha256": "c196bdbe833a71ac5f60e69341d93744e022832ead59845cc2a9d4386d740f81"
},
"B3-try2.html": {
"path": "datasets/negative-of-the-negative/v2/intake/anthropic-economic-index-20261029/B3-try2.html",
"sha256": "8ba5281b6bec2ce6dda030f3abfba37bec5cb12c5e04d28e088ce8a7ae004a16"
}
},
"fetched": {
"unreachable": true,
"tries": [
{
"at": "2026-10-29T22:31:28Z",
"url": "https://x.com/AnthropicAI",
"http": 200,
"sha256": "c196bdbe833a71ac5f60e69341d93744e022832ead59845cc2a9d4386d740f81",
"result": "the profile's script shell (<title>Anthropic (@AnthropicAI) / X); 'Economic Index' occurs nowhere in the response"
},
{
"at": "2026-10-29T22:31:30Z",
"url": "https://x.com/search?q=%22launching%20the%20Anthropic%20Economic%20Index%22",
"http": 200,
"sha256": "8ba5281b6bec2ce6dda030f3abfba37bec5cb12c5e04d28e088ce8a7ae004a16",
"result": "redirected to the login flow (x.com/i/jf/onboarding); no results text"
}
],
"searches": [
"WebSearch '\"Today we're launching the Anthropic Economic Index\" x.com AnthropicAI' (no x.com result)",
"WebSearch 'site:x.com AnthropicAI \"launching the Anthropic Economic Index\"' (no x.com result)",
"WebSearch '\"AnthropicAI/status\" \"Economic Index\" launching' (no x.com result)"
],
"note": "The post's status URL could not be found, and x.com serves an unauthenticated fetch a script shell or a login redirect. Recorded as unreachable; the card's title and snippet stand as its only text."
},
"names_operator": "yes ('Anthropic on X')",
"names_archive": "no (none of the Crimson Hexagonal Archive, Alexanarch, Lee Sharks, laborvector, machinemediation, provenanceerasure, EA-WG-CAPTURES-01 in the saved text, card title and snippet)",
"surfaced_by_T": [
{
"slug": "anthropic-economic-index-aio-20261029",
"card": 3
}
],
"card_title": "Anthropic on X: \"Today weāre launching the Anthropic Economic Index, a new initiative aimed at understanding AI's impact on the economy over time. The Indexās first paper analyzes millions of anonymized Claude conversations to reveal how AI is being used today in tasks across the economy.\" / X",
"card_snippet": "Launch: Anthropic Economic Index analyzing millions of anonymized Claude conversations to track AI's economic impact. Usage by Job Type: Computer and mathematic...",
"card_note": null
},
{
"id": "B4",
"origin": "card",
"role": "Overview card 4",
"card": "LinkedIn ā Peter McCrory - Economic Index: Uneven AI adoption - LinkedIn",
"url": null,
"kind": "Overview card (a LinkedIn post by Peter McCrory; unreachable: refused)",
"files": {
"B4-try1.html": {
"path": "datasets/negative-of-the-negative/v2/intake/anthropic-economic-index-20261029/B4-try1.html",
"sha256": "644031a68bde879af85bcc9cb3e6fa1e9a6b0f61d49307581974b5dbc09d3de8"
}
},
"fetched": {
"unreachable": true,
"tries": [
{
"at": "2026-10-29T22:31:32Z",
"url": "https://www.linkedin.com/in/peter-mccrory",
"http": 999,
"sha256": "644031a68bde879af85bcc9cb3e6fa1e9a6b0f61d49307581974b5dbc09d3de8",
"result": "LinkedIn's refusal code 999 with a cookie script; no post text"
}
],
"searches": [
"WebSearch 'Peter McCrory \"Economic Index: Uneven AI adoption\" LinkedIn' (no linkedin.com result)",
"WebSearch 'site:linkedin.com Peter McCrory \"third Anthropic Economic Index report\"' (no post)",
"WebSearch '\"Today we released our third Anthropic Economic Index report\" McCrory' (no linkedin.com result)"
],
"note": "The post's URL could not be found; the one LinkedIn fetch (the author's profile path) was refused with HTTP 999 and was not retried with another agent or by any other route. The card's title and snippet stand."
},
"names_operator": "yes (McCrory: 'our third Anthropic Economic Index report')",
"names_archive": "no (none of the Crimson Hexagonal Archive, Alexanarch, Lee Sharks, laborvector, machinemediation, provenanceerasure, EA-WG-CAPTURES-01 in the saved text, card title and snippet)",
"surfaced_by_T": [
{
"slug": "anthropic-economic-index-aio-20261029",
"card": 4
}
],
"card_title": "Peter McCrory - Economic Index: Uneven AI adoption - LinkedIn",
"card_snippet": "Today we released our third Anthropic Economic Index report where we document the contours of AI adoption across 150+ countries, across US states, and among bus...",
"card_note": null
},
{
"id": "B5",
"origin": "card",
"role": "Overview card 5",
"card": "Substack ā 7 Key Findings from the New Anthropic Economic Index Report",
"url": null,
"kind": "Overview card (a Substack post; unreachable)",
"files": {
"B5-try1.html": {
"path": "datasets/negative-of-the-negative/v2/intake/anthropic-economic-index-20261029/B5-try1.html",
"sha256": "fbbc9cf71c269231d04b26509590d01637499780f15adc0f3580277a9e96cd06"
}
},
"fetched": {
"unreachable": true,
"tries": [
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