{ "row": "yale-budget-lab-ai-tracker", "entity": "yale-budget-lab-ai-tracker", "address": "Yale Budget Lab AI labor market", "addresses": [ { "address": "Yale Budget Lab AI labor market", "slug": "yale-budget-lab-ai-labor-market-aio-20261029", "addr_id": "ADDR-2f74f55d9377", "obs_id": "OBS-9c44ea25d2ca", "axn": "AXN:0AF6.CAPTURE.⛳🌲🌋⏳🔐💜" } ], "epoch": "2026-10-29", "type": "C (public entity on which the archive bears: a labor-market tracker; 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, ρ = extends, read from the Labor Vector census record, the capture record and the deposit that bears on the tracker's class (#716); archive selected by D/R/O (no D or R candidate: no archive text names the tracker or the Lab; 1 deposit admitted by reading at O-class, 5 class sentences rejected; hop of 29 read and rejected), with the Labor Vector census record (31 claims) and the Capture Registry's findings, reading, analysis and query note (8 claims) ledgered as the archive's records of the entity; the field B by three origins (5 cards, 1 of them unreachable; 2 own pages, 1 unread; Wikipedia, no article); the composer read the ledgers, never the transcript, except where the brief asks for T's claims and the delta", "ledger": "unaudited (§3.8: one extractor per ledger); field quotes 56/56 verified by script against saved text, the census's PDF text, the Wikipedia response summary and raw wikitext or the card title; archive quotes 42/42 verified against the registry, census.json, questions.json and the deposit text", "frozen": false, "breaches": [] }, "spec": "EA-NEGONT-02 v0.9, #1690", "objects": { "T": { "path": "datasets/negative-of-the-negative/v2/intake/yale-budget-lab-ai-tracker-20261029/T1-yale-budget-lab-ai-labor-market-aio-20261029.txt", "sha256": "12b0399e6023def519635e0427da01fa7bbc4fd38438687ec185a6f7f03f8be8", "words": 230, "cards": 5, "words_note": "one transcript at one address (230 words in the answer body with its closer, links reduced to their text, citation markers and the card rail excluded; 5 cards)", "claims": [ "T1-1 definition: 'The AI Labor Market Tracker by The Budget Lab at Yale finds no clear evidence of large-scale or economy-wide labor market disruption from artificial intelligence.'", "T1-2 minimal economy-wide impact: 'Data updated through September 2026 shows that employment, unemployment rates, and wage trends for AI-exposed workers largely mirror historical patterns and pre-AI trajectories.'", "T1-3 churn: 'Changes in the mix of occupations across industries (occupational churn) remain flat and show no acceleration or divergence since the late 2022 launch of ChatGPT.'", "T1-4 exposed against unexposed: 'Synthetic differences-in-differences comparisons between AI-exposed and unexposed workers reveal no strong, consistent statistical footprint of AI-driven displacement, though slight volatility in unemployment growth for high-exposure groups continues to be monitored.'", "T1-5 capital against labor: 'Companies cutting jobs or restructuring are generally doing so for broader macro or capital-investment reasons rather than direct workforce replacement by AI agents.'", "T1-6 sections, churn: 'Occupational Churn: Tracks how fast job roles and industry composition are shifting over time.'", "T1-7 sections, AI metrics: 'AI Metrics: Evaluates various index measurements of AI task exposure across standard occupational classifications.'", "T1-8 sections, comparisons: 'Comparisons: Analyzes employment, wage, and unemployment trajectories of workers in high- versus low-AI exposure categories.'", "T1-9 closer: 'Would you like to know more about specific high-exposure occupations (such as tech or administrative roles) or how different AI exposure metrics compare?'" ], "register": { "register": "data/EA-WG-CAPTURES-01.json", "slug": "yale-budget-lab-ai-labor-market-aio-20261029", "obs_id": "OBS-9c44ea25d2ca" }, "transcripts": [ { "slug": "yale-budget-lab-ai-labor-market-aio-20261029", "address": "Yale Budget Lab AI labor market", "addr_id": "ADDR-2f74f55d9377", "obs_id": "OBS-9c44ea25d2ca", "axn": "AXN:0AF6.CAPTURE.⛳🌲🌋⏳🔐💜", "date": "2026-10-29", "surface": "Google AI Overview (expanded)", "auth": "signed out, incognito", "sha256": "12b0399e6023def519635e0427da01fa7bbc4fd38438687ec185a6f7f03f8be8", "sha256_of": "the registry's 'transcript' string (utf-8)", "sha256_raw": "ed8874083c87bec53bea996886591393acb6185533d7b6f9a678fc2f256b6bf4", "words": 230, "cards": 5, "card_rail": [ "1. The Budget Lab — Evaluating the Impact of AI on the Labor Market: Current State of Affairs | The Budget Lab | Findings: Broader US labor market shows no discernible disruption or acceleration in occupational mix change 33 months post-ChatGPT launch; trends mirror histor...", "2. The Budget Lab — Tracking the Impact of AI on the Labor Market | The Budget Lab | Key Takeaways * The occupational mix is not yet changing in ways that clearly align with the introduction of AI into the workforce. * Measures of AI usage show ...", "3. The Budget Lab — Labor Market AI Exposure: What Do We Know? | The Budget Lab | AI Measures: Seven evaluated metrics (Eloundou et al., Eisfeldt et al., Felten et al., Tomlinson et al., Webb) assess generative AI task completion and patent l...", "4. The Budget Lab — AI Is Probably Not (Yet) the Reason for Labor Market Weakening | The Budget Lab | Labor Market Conditions: Cooling economy with low layoffs, low hiring, and an unemployment rate rising to 4.3% by March 2026. AI Impact Analysis: Budget Lab res...", "5. YouTube·Quest Means Business — Yale Budget Lab Executive Director Martha Gimbel on why AI hasn't disrupted ... (video, 5:33)" ], "claims": [ "T1-1 definition: 'The AI Labor Market Tracker by The Budget Lab at Yale finds no clear evidence of large-scale or economy-wide labor market disruption from artificial intelligence.'", "T1-2 minimal economy-wide impact: 'Data updated through September 2026 shows that employment, unemployment rates, and wage trends for AI-exposed workers largely mirror historical patterns and pre-AI trajectories.'", "T1-3 churn: 'Changes in the mix of occupations across industries (occupational churn) remain flat and show no acceleration or divergence since the late 2022 launch of ChatGPT.'", "T1-4 exposed against unexposed: 'Synthetic differences-in-differences comparisons between AI-exposed and unexposed workers reveal no strong, consistent statistical footprint of AI-driven displacement, though slight volatility in unemployment growth for high-exposure groups continues to be monitored.'", "T1-5 capital against labor: 'Companies cutting jobs or restructuring are generally doing so for broader macro or capital-investment reasons rather than direct workforce replacement by AI agents.'", "T1-6 sections, churn: 'Occupational Churn: Tracks how fast job roles and industry composition are shifting over time.'", "T1-7 sections, AI metrics: 'AI Metrics: Evaluates various index measurements of AI task exposure across standard occupational classifications.'", "T1-8 sections, comparisons: 'Comparisons: Analyzes employment, wage, and unemployment trajectories of workers in high- versus low-AI exposure categories.'", "T1-9 closer: 'Would you like to know more about specific high-exposure occupations (such as tech or administrative roles) or how different AI exposure metrics compare?'" ], "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 expansion); the 'Capital vs. Labor' bullet cites a [1] whose token differs from card 1's and from every other marker's", "path": "datasets/negative-of-the-negative/v2/intake/yale-budget-lab-ai-tracker-20261029/T1-yale-budget-lab-ai-labor-market-aio-20261029.txt" } ] }, "KO": { "title": "Tracking the Impact of AI on the Labor Market", "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": "Tracking the Impact of AI on the Labor Market, also named the AI Labor Market Tracker, is an interactive tracker with regular updates whose evidence is an observed employment outcome and an exposure estimate (Labor Vector record); by its operator's account it \"contains The Budget Lab's ongoing analysis of potential AI impacts on the labor market\" (tracker PDF).", "claims": [ "LV-01", "LV-02", "LV-04", "LV-06", "F1" ] }, { "n": 2, "para": 0, "sense": "operator and question", "text": "Its operator is The Budget Lab at Yale, its pages under Yale University's copyright (tracker page; Labor Vector record), Martha Gimbel the Lab's executive director (first report); the census places it at the distribution of gains and losses and asks of it \"Who supplies the labor, who directs it, and who receives its gains — and what kind of evidence does this resource bring to that question?\" (Labor Vector record).", "claims": [ "F3", "LV-03", "F5", "LV-05", "LV-27" ] }, { "n": 3, "para": 1, "sense": "the analysis it continues", "text": "The analysis began as \"Evaluating the Impact of AI on the Labor Market: Current State of Affairs\" by Gimbel, Molly Kinder of Brookings Metro, Joshua Kendall and Maddie Lee, dated 1 October 2025 (first report), a title the census carries as one of the tracker's names (Labor Vector record); it asked whether labor-market change in the 33 months since ChatGPT's release differed from past technological change and whether there were economy-wide employment effects, and it planned regular updates (first report).", "claims": [ "F4", "F5", "LV-02", "F11", "F9" ] }, { "n": 4, "para": 1, "sense": "versions", "text": "The tracker was published on 16 July 2026 and updated to Version 1.0 on 15 September 2026, its data downloadable (tracker PDF), its page dated 14 October 2026 with the same plan of regular updates (tracker page); the census records it as active, founded on 16 July 2026, its data access 'Download All Data' and its licence unstated beyond Yale's copyright (Labor Vector record).", "claims": [ "F48", "F49", "F54", "F2", "F10", "LV-22", "LV-23", "LV-20", "LV-21" ] }, { "n": 5, "para": 1, "sense": "documentation", "text": "It refers its churn and the exposure of the unemployed to Gimbel et al. (2025) and its exposure metrics to Gimbel, Kendall and Kulsakdinun (2026) (tracker PDF), works the census lists as unread (Labor Vector record), and which match by authors and year the first report and the Lab's exposure review of 19 February 2026 (first report; exposure review).", "claims": [ "F53", "LV-26", "F4", "F32" ] }, { "n": 6, "para": 2, "sense": "unit", "text": "It works in three sections, occupational churn, AI metrics, and comparisons of AI-exposed and unexposed workers (tracker PDF), over monthly CPS microdata, its object the occupational mix, employment and unemployment as they relate to AI exposure and usage (Labor Vector record).", "claims": [ "F52", "LV-07", "LV-08" ] }, { "n": 7, "para": 2, "sense": "method: churn", "text": "Churn is a dissimilarity index of the occupational mix adapted from Duncan and Duncan over monthly CPS data (first report); it \"simply reflects change — it does not take a stance on the cause of that change\" (first report), and it does not rely on exposure metrics while it \"does not formally attempt to identify effects of AI\" (Nunn's note).", "claims": [ "F14", "F15", "F44" ] }, { "n": 8, "para": 2, "sense": "method: comparisons", "text": "The comparisons use synthetic differences-in-differences, which in Ryan Nunn's note of 7 May 2026 helps \"to distinguish any genuine impact of AI on exposed occupations from the persistent differences between exposed and unexposed jobs\" (Nunn's note); the census's method lists it with the churn, exposure and usage measures (Labor Vector record).", "claims": [ "F38", "F40", "LV-10" ] }, { "n": 9, "para": 3, "sense": "findings", "text": "The tracker's takeaways are that the occupational mix \"is not yet changing in ways that clearly align with the introduction of AI into the workforce\", that measures of AI usage \"show no connection to changes in employment or unemployment\", and that the synthetic differences-in-differences analysis \"does not yet clearly indicate an AI-related labor market footprint\" (tracker page; Labor Vector record).", "claims": [ "F29", "F30", "F31", "LV-12", "LV-13", "LV-14" ] }, { "n": 10, "para": 3, "sense": "September 2026", "text": "The September 2026 update, on August 2026 CPS microdata, \"does not provide clear evidence of labor market disruption associated with AI\", churn, the exposure of the unemployed and usage data remaining flat, within historical ranges or on pre-AI trends (tracker PDF; Labor Vector record).", "claims": [ "F50", "LV-15", "LV-11" ] }, { "n": 11, "para": 3, "sense": "findings, October 2025", "text": "In October 2025 the first report had found the occupational mix changing \"more quickly than it has in the past\", by no large difference and from before AI's widespread introduction, by industry a limited effect, across the broader labor market no discernible disruption in 33 months, and the anxiety over AI's effects \"largely speculative\" on its data (first report).", "claims": [ "F6", "F16", "F12", "F27" ] }, { "n": 12, "para": 3, "sense": "exposure and usage against employment", "text": "It found exposure, automation and augmentation measures unrelated to employment or unemployment, the shares of workers in low, middle and high exposure groups stable at about 29%, 46% and 18%, no clear growth in the exposure of the unemployed, and Anthropic's usage data stable \"rather than disruption\" (first report).", "claims": [ "F7", "F20", "F21", "F23" ] }, { "n": 13, "para": 3, "sense": "recent graduates", "text": "For recent college graduates the dissimilarity against older workers rose slightly faster, which \"could be consistent with\" Brynjolfsson et al. and \"could also simply reflect a slowing labor market\", the results to be read with caution for small samples (first report).", "claims": [ "F17", "F18" ] }, { "n": 14, "para": 3, "sense": "employment and wages", "text": "Nunn's note found the estimated effect on employment in the average exposed occupation, and on inflation-adjusted hourly wages, \"close to zero\" and statistically indistinguishable from it, the results not yet showing \"clear indication of labor market effects of AI\" (Nunn's note).", "claims": [ "F41", "F39" ] }, { "n": 15, "para": 3, "sense": "the market around it", "text": "Around it the labor market had cooled, unemployment rising from 3.4% in April 2023 to 4.3% in March 2026 with low layoffs and low hiring, payroll growth held down in principal part by slower net immigration, and the conjecture that the cooling bears the imprint of employers' AI adoption is not supported by \"the weight of the evidence—to date\" (Nunn's note).", "claims": [ "F42", "F46", "F43" ] }, { "n": 16, "para": 4, "sense": "the limit", "text": "Its limit is in its wording: \"Of course, these findings may change as new data become available\" (tracker PDF; Labor Vector record); the first report \"is not predictive of the future\" and holds it \"too soon to tell how disruptive the technology will be to jobs\" (first report); the exposure review asks for humility about where AI disruption could go (exposure review); and Nunn's note expects AI \"quite likely to eventually leave its mark on the labor market, even if it has not already\" (Nunn's note).", "claims": [ "F51", "LV-16", "F13", "F28", "F37", "F45" ] }, { "n": 17, "para": 4, "sense": "not a null", "text": "The census keeps the finding from its opposite: the tracker is \"NOT a finding that AI has no labor market effect: it states the analysis does not yet clearly indicate one\" (Labor Vector record).", "claims": [ "LV-24" ] }, { "n": 18, "para": 5, "sense": "its inputs: exposure and usage", "text": "Its exposure measure in the first report is OpenAI's, whether ChatGPT-4 technology can cut an occupation's task time by at least 50%, \"a theoretical estimate\" not based on actual usage, and its usage measure Anthropic's data on one tool, Claude, comprehensive usage data being \"not publicly available\" (first report).", "claims": [ "F19", "F24", "F22" ] }, { "n": 19, "para": 5, "sense": "exposure metrics", "text": "The exposure review places the Lab among the analysts who use Eloundou et al.'s exposure metrics and compares seven, Microsoft researchers' applicability score from Copilot conversations among them, finding broad agreement and more disagreement on highly exposed occupations, in magnitude more than in whether an occupation is exposed; exposure, it states, \"is not indicative of a jobs AI will automate out of existence\" but \"indicates places in the labor market where AI could have an impact\" (exposure review).", "claims": [ "F35", "F36", "F33", "F34" ] }, { "n": 20, "para": 5, "sense": "usage data", "text": "Claude usage alone, the first report states, \"is not representative of how workers across the economy are using AI chatbots and tools\", and better data, \"comprehensive usage data from all the leading AI companies\", is what it asks for (first report); the census records the tracker as \"NOT a usage dataset: it uses usage measures as inputs\" and lists which usage data it draws on, and their provider, as unread in its saved text (Labor Vector record).", "claims": [ "F25", "F26", "F8", "LV-25", "LV-18" ] }, { "n": 21, "para": 6, "sense": "what it can settle", "text": "On the census's reading the tracker's evidence can answer whether churn has left its historical ranges, whether AI-exposed workers are over-represented among the unemployed and whether usage tracks employment, and cannot answer \"who supplies, directs or gains from labor performed with AI\" or \"effects below the level the CPS can detect\"; it records supplier, director and gains as not recorded (Labor Vector record).", "claims": [ "LV-17", "LV-18", "LV-09" ] }, { "n": 22, "para": 6, "sense": "in the worked questions", "text": "In the census's worked questions it is the observed tracker of \"occupational churn and unemployment by exposure, from the CPS\", one of two observed trackers whose readings of the same period differ, with \"no clear labor-market footprint yet, at the level the CPS can see\", and its meeting with Labor Vector's instruments is \"none stated\" (Labor Vector record).", "claims": [ "LV-28", "LV-29", "LV-30", "LV-19" ] }, { "n": 23, "para": 6, "sense": "the class", "text": "The archive classes such measurement as AI-economics research that measures \"which jobs AI eliminates, what tasks it can perform, and what consumer surplus it generates\", Eloundou et al.'s capability projection among it (#716), whose exposure metrics the Lab uses (exposure review).", "claims": [ "O716-01", "F35" ] }, { "n": 24, "para": 6, "sense": "a fourth channel", "text": "Beside it the archive defines a fourth channel, \"compositional authority transfer\", the rate at which AI synthesis captures value from authors by stripping attribution: \"even when human labor *remains* (the author's work is used), the economic value tied to provenance — reputation, traffic, citation credit, contractual rights — is extracted by the system\", which \"is not displacement\" (#716).", "claims": [ "O716-03", "O716-02" ] }, { "n": 25, "para": 6, "sense": "who gains", "text": "The census's question of who gains finds that \"No record in the census measures the distribution of productivity gains between workers, employers, platforms and model providers\" (Labor Vector record).", "claims": [ "LV-31" ] }, { "n": 26, "para": 7, "sense": "the composition at the name", "text": "At 'Yale Budget Lab AI labor market' Google's AI Overview, on a baseline where no card cites the Labor Vector record, names the tracker, its conclusion, methods and sections from the Lab's own pages and holds the name, the data through September 2026, flat churn and the synthetic differences-in-differences design (Capture Registry, 2026-10-29).", "claims": [ "CAP-1-08", "CAP-1-05", "CAP-1-01" ] }, { "n": 27, "para": 7, "sense": "what it lost and added", "text": "The registry records the 'yet' of \"does not yet clearly indicate\" dropped, wage trends, a 'Capital vs. Labor' line and a 'slight volatility' composed beyond the record, and the usage measures' providers left unsaid (Capture Registry, 2026-10-29).", "claims": [ "CAP-1-02", "CAP-1-03", "CAP-1-04" ] }, { "n": 28, "para": 7, "sense": "its reading", "text": "It reads the composition as a provisional finding \"composed as a settled negative\", the 'yet' being \"the whole of its claim\" (Capture Registry, 2026-10-29).", "claims": [ "CAP-1-06", "CAP-1-07" ] }, { "n": 29, "para": 7, "sense": "the encyclopedia", "text": "Wikipedia has no article on the tracker or on the Lab; its article on generative AI cites the first report for \"a 2025 study concluded that the US labor market had so far not experienced a discernible disruption from generative AI\" (Wikipedia).", "claims": [ "F55", "F56" ] }, { "n": 30, "para": 7, "sense": "broadcast", "text": "A Quest Means Business video carries the Lab's executive director \"on why AI hasn't disrupted ...\" (video card, title only).", "claims": [ "F47" ] } ], "joins": [ { "act": "order of senses", "sentences": "1–30", "claims": [], "note": "equal case (§4.7): the entity's dependency order (what it is, its lineage, its unit and method, its findings by date, its limit, its inputs, what it can settle, its reception), field and archive claims ordered together by genealogy within each sense; neither ledger placed first as a block" }, { "act": "holding together (documentation)", "sentences": [ 5 ], "claims": [ "F53", "F4", "F32", "LV-26" ], "note": "the tracker's two documentation references composed beside the two cards whose authors and years match them; the identification is stated as a match by authors and year, no more" }, { "act": "holding together (the finding and its limit)", "sentences": [ 9, 16, 17 ], "claims": [ "F29", "F31", "F51", "LV-24" ], "note": "the takeaways composed with their 'not yet' and the stated limit in the next paragraph; the census's negative tag beside them" }, { "act": "holding together (the class and 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workers (tracker PDF).", "claims": [ "F1", "F52" ] }, { "n": 2, "para": 0, "text": "It was published on 16 July 2026, reached Version 1.0 on 15 September 2026 with downloadable data, and its page is dated 14 October 2026 under Yale University's copyright (tracker PDF; tracker page).", "claims": [ "F48", "F49", "F54", "F2", "F3" ] }, { "n": 3, "para": 1, "text": "Its takeaways: the occupational mix is not yet changing in ways clearly aligned with AI; AI usage shows no connection to employment or unemployment; and a synthetic differences-in-differences analysis does not yet clearly indicate a labor-market footprint (tracker page).", "claims": [ "F29", "F30", "F31" ] }, { "n": 4, "para": 1, "text": "The September update on August 2026 CPS microdata finds no clear evidence of AI-associated disruption, and the Lab says the findings may change as new data arrive and plans regular updates (tracker PDF; tracker page).", "claims": [ "F50", "F51", "F10" ] }, { "n": 5, "para": 2, "text": "The analysis continues the report of 1 October 2025 by Gimbel, Kinder, Kendall and Lee, which asked whether change since ChatGPT differed from past technological change, and its documentation includes Gimbel, Kendall and Kulsakdinun's exposure review of February 2026 (first report; tracker PDF; exposure review).", "claims": [ "F4", "F5", "F9", "F11", "F53", "F32" ] }, { "n": 6, "para": 2, "text": "That report found no discernible economy-wide disruption in 33 months, a mix changing somewhat faster than before but from before AI, limited effects by industry, and called the anxiety largely speculative, while stating that its analysis is not predictive and that it is too soon to tell (first report).", "claims": [ "F12", "F6", "F16", "F27", "F13", "F28" ] }, { "n": 7, "para": 2, "text": "Churn is a Duncan-and-Duncan dissimilarity index on monthly CPS data that records change without taking a stance on its cause (first report).", "claims": [ "F14", "F15" ] }, { "n": 8, "para": 3, "text": "Its exposure data are OpenAI's, a theoretical estimate, and its usage data Anthropic's Claude data; neither showed a relation to employment, with exposure shares stable and no growth in the exposure of the unemployed (first report).", "claims": [ "F7", "F19", "F20", "F21", "F22", "F23", "F24" ] }, { "n": 9, "para": 3, "text": "It warns that Claude usage is not representative and asks for comprehensive usage data from all leading AI companies (first report).", "claims": [ "F25", "F26", "F8" ] }, { "n": 10, "para": 3, "text": "Recent graduates' dissimilarity rose slightly faster, possibly consistent with Brynjolfsson et al., to be read with caution (first report).", "claims": [ "F17", "F18" ] }, { "n": 11, "para": 4, "text": "The exposure review finds seven exposure metrics, Eloundou et al.'s and a Microsoft Copilot-based score among them, broadly in agreement but less so for highly exposed occupations, and says exposure marks where AI could have an impact, not jobs it will automate away (exposure review).", "claims": [ "F33", "F34", "F35", "F36", "F37" ] }, { "n": 12, "para": 4, "text": "Ryan Nunn's note of May 2026 applies synthetic differences-in-differences and finds employment and hourly-wage effects close to zero, adds that churn does not formally identify AI's effects, and expects AI eventually to leave its mark (Nunn's note).", "claims": [ "F38", "F39", "F40", "F41", "F44", "F45" ] }, { "n": 13, "para": 4, "text": "It sets this in a cooled market, unemployment at 4.3% in March 2026 and payroll growth slowed in principal part by lower immigration, and finds the AI explanation unsupported to date (Nunn's note).", "claims": [ "F42", "F43", "F46" ] }, { "n": 14, "para": 5, "text": "Wikipedia has no article on the tracker, though its generative-AI article cites the first report, and a Quest Means Business video features Gimbel on why AI hasn't disrupted … (Wikipedia; video card).", "claims": [ "F55", "F56", "F47" ] } ] }, "L_B": { "words": 491, "text": "The Budget Lab at Yale describes the AI Labor Market Tracker as an interactive tool containing its \"ongoing analysis of potential AI impacts on the labor market\", in three sections: occupational churn, AI metrics, and comparisons of AI-exposed and unexposed workers (tracker PDF). It was published on 16 July 2026, reached Version 1.0 on 15 September 2026 with downloadable data, and its page is dated 14 October 2026 under Yale University's copyright (tracker PDF; tracker page). Its takeaways: the occupational mix is not yet changing in ways clearly aligned with AI; AI usage shows no connection to employment or unemployment; and a synthetic differences-in-differences analysis does not yet clearly indicate a labor-market footprint (tracker page). The September update on August 2026 CPS microdata finds no clear evidence of AI-associated disruption, and the Lab says the findings may change as new data arrive and plans regular updates (tracker PDF; tracker page). The analysis continues the report of 1 October 2025 by Gimbel, Kinder, Kendall and Lee, which asked whether change since ChatGPT differed from past technological change, and its documentation includes Gimbel, Kendall and Kulsakdinun's exposure review of February 2026 (first report; tracker PDF; exposure review). That report found no discernible economy-wide disruption in 33 months, a mix changing somewhat faster than before but from before AI, limited effects by industry, and called the anxiety largely speculative, while stating that its analysis is not predictive and that it is too soon to tell (first report). Churn is a Duncan-and-Duncan dissimilarity index on monthly CPS data that records change without taking a stance on its cause (first report). Its exposure data are OpenAI's, a theoretical estimate, and its usage data Anthropic's Claude data; neither showed a relation to employment, with exposure shares stable and no growth in the exposure of the unemployed (first report). It warns that Claude usage is not representative and asks for comprehensive usage data from all leading AI companies (first report). Recent graduates' dissimilarity rose slightly faster, possibly consistent with Brynjolfsson et al., to be read with caution (first report). The exposure review finds seven exposure metrics, Eloundou et al.'s and a Microsoft Copilot-based score among them, broadly in agreement but less so for highly exposed occupations, and says exposure marks where AI could have an impact, not jobs it will automate away (exposure review). Ryan Nunn's note of May 2026 applies synthetic differences-in-differences and finds employment and hourly-wage effects close to zero, adds that churn does not formally identify AI's effects, and expects AI eventually to leave its mark (Nunn's note). It sets this in a cooled market, unemployment at 4.3% in March 2026 and payroll growth slowed in principal part by lower immigration, and finds the AI explanation unsupported to date (Nunn's note). Wikipedia has no article on the tracker, though its generative-AI article cites the first report, and a Quest Means Business video features Gimbel on why AI hasn't disrupted … (Wikipedia; video card).", "note": "L_F(B), the field composed in the field's ontology (v0.9 §0.4), realized as KO_B; its rail is P_B's, by lineage." }, "L_BA": { "words": 1588, "text": "Tracking the Impact of AI on the Labor Market, also named the AI Labor Market Tracker, is an interactive tracker with regular updates whose evidence is an observed employment outcome and an exposure estimate (Labor Vector record); by its operator's account it \"contains The Budget Lab's ongoing analysis of potential AI impacts on the labor market\" (tracker PDF). Its operator is The Budget Lab at Yale, its pages under Yale University's copyright (tracker page; Labor Vector record), Martha Gimbel the Lab's executive director (first report); the census places it at the distribution of gains and losses and asks of it \"Who supplies the labor, who directs it, and who receives its gains — and what kind of evidence does this resource bring to that question?\" (Labor Vector record). The analysis began as \"Evaluating the Impact of AI on the Labor Market: Current State of Affairs\" by Gimbel, Molly Kinder of Brookings Metro, Joshua Kendall and Maddie Lee, dated 1 October 2025 (first report), a title the census carries as one of the tracker's names (Labor Vector record); it asked whether labor-market change in the 33 months since ChatGPT's release differed from past technological change and whether there were economy-wide employment effects, and it planned regular updates (first report). The tracker was published on 16 July 2026 and updated to Version 1.0 on 15 September 2026, its data downloadable (tracker PDF), its page dated 14 October 2026 with the same plan of regular updates (tracker page); the census records it as active, founded on 16 July 2026, its data access 'Download All Data' and its licence unstated beyond Yale's copyright (Labor Vector record). It refers its churn and the exposure of the unemployed to Gimbel et al. (2025) and its exposure metrics to Gimbel, Kendall and Kulsakdinun (2026) (tracker PDF), works the census lists as unread (Labor Vector record), and which match by authors and year the first report and the Lab's exposure review of 19 February 2026 (first report; exposure review). It works in three sections, occupational churn, AI metrics, and comparisons of AI-exposed and unexposed workers (tracker PDF), over monthly CPS microdata, its object the occupational mix, employment and unemployment as they relate to AI exposure and usage (Labor Vector record). Churn is a dissimilarity index of the occupational mix adapted from Duncan and Duncan over monthly CPS data (first report); it \"simply reflects change — it does not take a stance on the cause of that change\" (first report), and it does not rely on exposure metrics while it \"does not formally attempt to identify effects of AI\" (Nunn's note). The comparisons use synthetic differences-in-differences, which in Ryan Nunn's note of 7 May 2026 helps \"to distinguish any genuine impact of AI on exposed occupations from the persistent differences between exposed and unexposed jobs\" (Nunn's note); the census's method lists it with the churn, exposure and usage measures (Labor Vector record). The tracker's takeaways are that the occupational mix \"is not yet changing in ways that clearly align with the introduction of AI into the workforce\", that measures of AI usage \"show no connection to changes in employment or unemployment\", and that the synthetic differences-in-differences analysis \"does not yet clearly indicate an AI-related labor market footprint\" (tracker page; Labor Vector record). The September 2026 update, on August 2026 CPS microdata, \"does not provide clear evidence of labor market disruption associated with AI\", churn, the exposure of the unemployed and usage data remaining flat, within historical ranges or on pre-AI trends (tracker PDF; Labor Vector record). In October 2025 the first report had found the occupational mix changing \"more quickly than it has in the past\", by no large difference and from before AI's widespread introduction, by industry a limited effect, across the broader labor market no discernible disruption in 33 months, and the anxiety over AI's effects \"largely speculative\" on its data (first report). It found exposure, automation and augmentation measures unrelated to employment or unemployment, the shares of workers in low, middle and high exposure groups stable at about 29%, 46% and 18%, no clear growth in the exposure of the unemployed, and Anthropic's usage data stable \"rather than disruption\" (first report). For recent college graduates the dissimilarity against older workers rose slightly faster, which \"could be consistent with\" Brynjolfsson et al. and \"could also simply reflect a slowing labor market\", the results to be read with caution for small samples (first report). Nunn's note found the estimated effect on employment in the average exposed occupation, and on inflation-adjusted hourly wages, \"close to zero\" and statistically indistinguishable from it, the results not yet showing \"clear indication of labor market effects of AI\" (Nunn's note). Around it the labor market had cooled, unemployment rising from 3.4% in April 2023 to 4.3% in March 2026 with low layoffs and low hiring, payroll growth held down in principal part by slower net immigration, and the conjecture that the cooling bears the imprint of employers' AI adoption is not supported by \"the weight of the evidence—to date\" (Nunn's note). Its limit is in its wording: \"Of course, these findings may change as new data become available\" (tracker PDF; Labor Vector record); the first report \"is not predictive of the future\" and holds it \"too soon to tell how disruptive the technology will be to jobs\" (first report); the exposure review asks for humility about where AI disruption could go (exposure review); and Nunn's note expects AI \"quite likely to eventually leave its mark on the labor market, even if it has not already\" (Nunn's note). The census keeps the finding from its opposite: the tracker is \"NOT a finding that AI has no labor market effect: it states the analysis does not yet clearly indicate one\" (Labor Vector record). Its exposure measure in the first report is OpenAI's, whether ChatGPT-4 technology can cut an occupation's task time by at least 50%, \"a theoretical estimate\" not based on actual usage, and its usage measure Anthropic's data on one tool, Claude, comprehensive usage data being \"not publicly available\" (first report). The exposure review places the Lab among the analysts who use Eloundou et al.'s exposure metrics and compares seven, Microsoft researchers' applicability score from Copilot conversations among them, finding broad agreement and more disagreement on highly exposed occupations, in magnitude more than in whether an occupation is exposed; exposure, it states, \"is not indicative of a jobs AI will automate out of existence\" but \"indicates places in the labor market where AI could have an impact\" (exposure review). Claude usage alone, the first report states, \"is not representative of how workers across the economy are using AI chatbots and tools\", and better data, \"comprehensive usage data from all the leading AI companies\", is what it asks for (first report); the census records the tracker as \"NOT a usage dataset: it uses usage measures as inputs\" and lists which usage data it draws on, and their provider, as unread in its saved text (Labor Vector record). On the census's reading the tracker's evidence can answer whether churn has left its historical ranges, whether AI-exposed workers are over-represented among the unemployed and whether usage tracks employment, and cannot answer \"who supplies, directs or gains from labor performed with AI\" or \"effects below the level the CPS can detect\"; it records supplier, director and gains as not recorded (Labor Vector record). In the census's worked questions it is the observed tracker of \"occupational churn and unemployment by exposure, from the CPS\", one of two observed trackers whose readings of the same period differ, with \"no clear labor-market footprint yet, at the level the CPS can see\", and its meeting with Labor Vector's instruments is \"none stated\" (Labor Vector record). The archive classes such measurement as AI-economics research that measures \"which jobs AI eliminates, what tasks it can perform, and what consumer surplus it generates\", Eloundou et al.'s capability projection among it (#716), whose exposure metrics the Lab uses (exposure review). Beside it the archive defines a fourth channel, \"compositional authority transfer\", the rate at which AI synthesis captures value from authors by stripping attribution: \"even when human labor *remains* (the author's work is used), the economic value tied to provenance — reputation, traffic, citation credit, contractual rights — is extracted by the system\", which \"is not displacement\" (#716). The census's question of who gains finds that \"No record in the census measures the distribution of productivity gains between workers, employers, platforms and model providers\" (Labor Vector record). At 'Yale Budget Lab AI labor market' Google's AI Overview, on a baseline where no card cites the Labor Vector record, names the tracker, its conclusion, methods and sections from the Lab's own pages and holds the name, the data through September 2026, flat churn and the synthetic differences-in-differences design (Capture Registry, 2026-10-29). The registry records the 'yet' of \"does not yet clearly indicate\" dropped, wage trends, a 'Capital vs. Labor' line and a 'slight volatility' composed beyond the record, and the usage measures' providers left unsaid (Capture Registry, 2026-10-29). It reads the composition as a provisional finding \"composed as a settled negative\", the 'yet' being \"the whole of its claim\" (Capture Registry, 2026-10-29). Wikipedia has no article on the tracker or on the Lab; its article on generative AI cites the first report for \"a 2025 study concluded that the US labor market had so far not experienced a discernible disruption from generative AI\" (Wikipedia). A Quest Means Business video carries the Lab's executive director \"on why AI hasn't disrupted ...\" (video card, title only).", "rail": [], "note": "L(B ∪ A) in the equal-terms case (v0.9 §0.4), realized as the knowledge object (objects.KO); its rail is the compression's, by lineage." } }, "field": [ { "id": "B1", "card": "The Budget Lab — Evaluating the Impact of AI on the Labor Market: Current State of Affairs | The Budget Lab", "fetched": "card; B1.html sha256 065e453aacdb846c, HTTP 200 at 2026-10-29T22:30:32Z; the census recorded 403 for this URL", "origin": "card" }, { "id": "B2", "card": "The Budget Lab — Tracking the Impact of AI on the Labor Market | The Budget Lab", "fetched": "card; B2.html sha256 7efbd4d59a4fa632, HTTP 200 at 2026-10-29T22:30:34Z; byte-identical to the census copy yale-budget-lab-ai-tracker--tracker", "origin": "card" }, { "id": "B3", "card": "The Budget Lab — Labor Market AI Exposure: What Do We Know? | The Budget Lab", "fetched": "card; B3.html sha256 c17cecc68b86f656, HTTP 403 then 200 (same agent)", "origin": "card" }, { "id": "B4", "card": "The Budget Lab — AI Is Probably Not (Yet) the Reason for Labor Market Weakening | The Budget Lab", "fetched": "card; B4.html sha256 e0811bd485fd98cd, HTTP 200", "origin": "card" }, { "id": "B5", "card": "YouTube·Quest Means Business — Yale Budget Lab Executive Director Martha Gimbel on why AI hasn't disrupted ...", "fetched": "card; unreachable: no URL found; title only", "origin": "card" }, { "id": "B6", "card": "The Budget Lab — Tracking the Impact of AI on the Labor Market (printable PDF of the tracker, Version 1.0, 15 September 2026)", "fetched": "own_page; copied from the census's saved copy, sha256 9bcf775f43ed5756", "origin": "own_page" }, { "id": "B7", "card": "The Budget Lab — Evaluating the Impact of AI on the Labor Market: September CPS Update (unread)", "fetched": "own_page; unread: 403 to the census, not refetched", "origin": "own_page" }, { "id": "B8", "card": "Wikipedia — Tracking the Impact of AI on the Labor Market (no article)", "fetched": "wikipedia; no article: raw and rendered 404; one article cites the first report", "origin": "wikipedia" } ], "field_claims": { "F1": { "source": "B6", "origin": "own_page", "locus": "Introduction ¶1", "quote": "This interactive tool contains The Budget Lab's ongoing analysis of potential AI impacts on the labor market.", "claim": "the tracker defined by its operator: an interactive tool holding the Lab's ongoing analysis of potential AI impacts on the labor market", "modality": "stipulation", "kind": "definition", "kernel": true, "const": true, "lineage": "what it is: the Lab's ongoing tracker of AI's impact on the labor market", "date": "2026-07-16 (published; this text updated 2026-09-15)", "quote_ok": true, "checked_against": "saved text B6.txt (sha256 6a1c4bade1ce5498…), whitespace-normalised" }, "F2": { "source": "B2", "origin": "card", "locus": "title and dateline", "quote": "Tracking the Impact of AI on the Labor Market October 14, 2026 | Updated: October 14, 2026", "claim": "the tracker's page, titled 'Tracking the Impact of AI on the Labor Market', dated and updated 14 October 2026", "modality": "documented", "kind": "fact", "kernel": false, "const": false, "lineage": "updates, versions and data access", "date": "2026-10-14 (page date)", "quote_ok": true, "checked_against": "saved text B2.txt (sha256 94e3c0200ab0b481…), whitespace-normalised" }, "F3": { "source": "B2", "origin": "card", "locus": "footer", "quote": "Copyright ©2026 Yale University. All rights reserved. The Budget Lab 127 Wall Street, New Haven, CT 06511", "claim": "the page is The Budget Lab's, under Yale University's copyright, all rights reserved", "modality": "documented", "kind": "attribution", "kernel": false, "const": false, "lineage": "the operator and the authors", "date": "2026-10-14 (page date)", "quote_ok": true, "checked_against": "saved text B2.txt (sha256 94e3c0200ab0b481…), whitespace-normalised" }, "F4": { "source": "B1", "origin": "card", "locus": "title, byline and date", "quote": "Evaluating the Impact of AI on the Labor Market: Current State of Affairs Martha Gimbel , Molly Kinder , Joshua Kendall and Maddie Lee October 1, 2025", "claim": "the first report: 'Evaluating the Impact of AI on the Labor Market: Current State of Affairs', by Martha Gimbel, Molly Kinder, Joshua Kendall and Maddie Lee, 1 October 2025", "modality": "documented", "kind": "attribution", "kernel": false, "const": false, "lineage": "the analysis it continues: the first report and its documentation", "date": "2025-10-01 (report date)", "quote_ok": true, "checked_against": "saved text B1.txt (sha256 8d18c4b7761bfe5f…), whitespace-normalised" }, "F5": { "source": "B1", "origin": "card", "locus": "Authors", "quote": "Martha Gimbel , Executive Director of the Budget Lab Molly Kinder , Senior Fellow – Brookings Metro Joshua Kendall , Research Fellow at the Budget Lab Maddie Lee , Research Assistant at the Budget Lab", "claim": "the authors' positions: Gimbel the Lab's executive director, Kinder a senior fellow at Brookings Metro, Kendall and Lee of the Lab", "modality": "documented", "kind": "attribution", "kernel": false, "const": false, "lineage": "the operator and the authors", "date": "2025-10-01 (report date)", "quote_ok": true, "checked_against": "saved text B1.txt (sha256 8d18c4b7761bfe5f…), whitespace-normalised" }, "F6": { "source": "B1", "origin": "card", "locus": "Key Takeaways 1", "quote": "While the occupational mix is changing more quickly than it has in the past, it is not a large difference and predates the widespread introduction of AI in the workforce.", "claim": "October 2025: the occupational mix changing faster than in the past, by no large difference, and from before AI's widespread introduction", "modality": "documented", "kind": "fact", "kernel": false, "const": false, "lineage": "finding: the occupational mix", "date": "2025-10-01 (report date)", "quote_ok": true, "checked_against": "saved text B1.txt (sha256 8d18c4b7761bfe5f…), whitespace-normalised" }, "F7": { "source": "B1", "origin": "card", "locus": "Key Takeaways 2", "quote": "Currently, measures of exposure, automation, and augmentation show no sign of being related to changes in employment or unemployment.", "claim": "October 2025: exposure, automation and augmentation measures show no sign of relation to employment or unemployment", "modality": "documented", "kind": "fact", "kernel": false, "const": false, "lineage": "finding: exposure and usage against employment and unemployment", "date": "2025-10-01 (report date)", "quote_ok": true, "checked_against": "saved text B1.txt (sha256 8d18c4b7761bfe5f…), whitespace-normalised" }, "F8": { "source": "B1", "origin": "card", "locus": "Key Takeaways 3", "quote": "Better data is needed to fully understand the impact of AI on the labor market.", "claim": "the report's stated need: better data to understand AI's labor-market impact fully", "modality": "self-description", "kind": "fact", "kernel": false, "const": false, "lineage": "usage data: its source, its reach and the data the Lab asks for", "date": "2025-10-01 (report date)", "quote_ok": true, "checked_against": "saved text B1.txt (sha256 8d18c4b7761bfe5f…), whitespace-normalised" }, "F9": { "source": "B1", "origin": "card", "locus": "Key Takeaways 4", "quote": "We plan on updating this analysis regularly moving forward to see how the impact of AI on the labor market changes over time.", "claim": "October 2025: the plan to update the analysis regularly", "modality": "self-description", "kind": "fact", "kernel": false, "const": false, "lineage": "updates, versions and data access", "date": "2025-10-01 (report date)", "quote_ok": true, "checked_against": "saved text B1.txt (sha256 8d18c4b7761bfe5f…), whitespace-normalised" }, "F10": { "source": "B2", "origin": "card", "locus": "Key Takeaways 4", "quote": "We plan on updating this analysis regularly moving forward to see how the impact of AI on the labor market changes over time.", "claim": "the tracker page carries the same plan to update regularly", "modality": "self-description", "kind": "fact", "kernel": false, "const": false, "lineage": "updates, versions and data access", "date": "2026-10-14 (page date)", "quote_ok": true, "checked_against": "saved text B2.txt (sha256 94e3c0200ab0b481…), whitespace-normalised" }, "F11": { "source": "B1", "origin": "card", "locus": "¶3", "quote": "First, is the pace of labor market change in this 33-month period of employment disruption different from past periods of early technological change? Second, is there evidence of economy-wide employment effects?", "claim": "the report's two questions: whether labor-market change in 33 months differs from earlier technological change, and whether there are economy-wide employment effects", "modality": "stipulation", "kind": "function", "kernel": false, "const": false, "lineage": "method: occupational dissimilarity, and synthetic differences-in-differences", "date": "2025-10-01 (report date)", "quote_ok": true, "checked_against": "saved text B1.txt (sha256 8d18c4b7761bfe5f…), whitespace-normalised" }, "F12": { "source": "B1", "origin": "card", "locus": "¶4", "quote": "Overall, our metrics indicate that the broader labor market has not experienced a discernible disruption since ChatGPT’s release 33 months ago, undercutting fears that AI automation is currently eroding the demand for cognitive labor across the economy.", "claim": "the report's headline: no discernible disruption of the broader labor market in the 33 months since ChatGPT's release", "modality": "documented", "kind": "fact", "kernel": true, "const": false, "lineage": "headline: no clear economy-wide disruption", "date": "2025-10-01 (report date)", "quote_ok": true, "checked_against": "saved text B1.txt (sha256 8d18c4b7761bfe5f…), whitespace-normalised" }, "F13": { "source": "B1", "origin": "card", "locus": "¶6", "quote": "Of course, our analysis is not predictive of the future.", "claim": "the report's limit: the analysis does not predict the future", "modality": "self-description", "kind": "fact", "kernel": true, "const": false, "lineage": "the limit: not yet, and the findings may change", "date": "2025-10-01 (report date)", "quote_ok": true, "checked_against": "saved text B1.txt (sha256 8d18c4b7761bfe5f…), whitespace-normalised" }, "F14": { "source": "B1", "origin": "card", "locus": "Appendix ¶2", "quote": "We adapt Duncan and Duncan’s methodology to construct a dissimilarity index of the change in the occupational mix over time using monthly CPS data.", "claim": "the churn measure: a dissimilarity index of the occupational mix over time, adapted from Duncan and Duncan, on monthly CPS data", "modality": "stipulation", "kind": "definition", "kernel": false, "const": false, "lineage": "method: occupational dissimilarity, and synthetic differences-in-differences", "date": "2025-10-01 (report date)", "quote_ok": true, "checked_against": "saved text B1.txt (sha256 8d18c4b7761bfe5f…), whitespace-normalised" }, "F15": { "source": "B1", "origin": "card", "locus": "'Is this Time Different?' ¶3", "quote": "Note that a change over this time period simply reflects change — it does not take a stance on the cause of that change.", "claim": "the churn measure's own limit: it records change and takes no stance on its cause", "modality": "self-description", "kind": "function", "kernel": false, "const": false, "lineage": "method: occupational dissimilarity, and synthetic differences-in-differences", "date": "2025-10-01 (report date)", "quote_ok": true, "checked_against": "saved text B1.txt (sha256 8d18c4b7761bfe5f…), whitespace-normalised" }, "F16": { "source": "B1", "origin": "card", "locus": "'Is this Time Different?' ¶6", "quote": "Repeating this analysis by industry similarly suggests a limited effect of AI.", "claim": "by industry the analysis suggests a limited effect of AI", "modality": "documented", "kind": "fact", "kernel": false, "const": false, "lineage": "finding: the occupational mix", "date": "2025-10-01 (report date)", "quote_ok": true, "checked_against": "saved text B1.txt (sha256 8d18c4b7761bfe5f…), whitespace-normalised" }, "F17": { "source": "B1", "origin": "card", "locus": "'Recent College Graduates' ¶1", "quote": "If generative AI were in fact substantially changing the labor market for recent college graduates, we would expect to see a growing dissimilarity in the occupational mix between these two groups. … However, our results should be interpreted with caution particularly given small sample sizes.", "claim": "recent graduates: a growing dissimilarity against older workers would be expected if AI were changing their market; the results are to be read with caution for small samples", "modality": "hypothesis", "kind": "function", "kernel": false, "const": false, "lineage": "recent college graduates", "date": "2025-10-01 (report date)", "quote_ok": true, "checked_against": "saved text B1.txt (sha256 8d18c4b7761bfe5f…), whitespace-normalised" }, "F18": { "source": "B1", "origin": "card", "locus": "'Recent College Graduates' ¶1", "quote": "The dissimilarity has increased slightly faster in recent months than it did in a previous time period, which could be consistent with a recent paper from Brynjolfsson et al. showing a possible impact of AI on employment of early career workers. It could also simply reflect a slowing labor market.", "claim": "the graduates' dissimilarity rose slightly faster, possibly consistent with Brynjolfsson et al. or with a slowing labor market", "modality": "interpretation", "kind": "interpretation", "kernel": false, "const": false, "lineage": "recent college graduates", "date": "2025-10-01 (report date)", "quote_ok": true, "checked_against": "saved text B1.txt (sha256 8d18c4b7761bfe5f…), whitespace-normalised" }, "F19": { "source": "B1", "origin": "card", "locus": "'OpenAI's \"Exposure\" Data' ¶1", "quote": "This refers, generally, to whether utilizing ChatGPT4 technology can help reduce the time it takes to complete the occupation’s tasks by at least 50%.", "claim": "the exposure measure used: OpenAI's, whether ChatGPT-4 technology can cut an occupation's task time by at least 50%", "modality": "stipulation", "kind": "definition", "kernel": false, "const": false, "lineage": "exposure metrics: what they measure and what they do not", "date": "2025-10-01 (report date)", "quote_ok": true, "checked_against": "saved text B1.txt (sha256 8d18c4b7761bfe5f…), whitespace-normalised" }, "F20": { "source": "B1", "origin": "card", "locus": "'OpenAI's \"Exposure\" Data' ¶4", "quote": "The share of workers in the lowest, middle, and highest occupational exposure groups stay stable at around 29%, 46% and 18%, respectively.", "claim": "the shares of workers in low, middle and high exposure groups stable at about 29%, 46% and 18%", "modality": "documented", "kind": "fact", "kernel": false, "const": false, "lineage": "finding: exposure and usage against employment and unemployment", "date": "2025-10-01 (report date)", "quote_ok": true, "checked_against": "saved text B1.txt (sha256 8d18c4b7761bfe5f…), whitespace-normalised" }, "F21": { "source": "B1", "origin": "card", "locus": "'OpenAI's \"Exposure\" Data' ¶5", "quote": "Even when specifically examining the unemployed population, there is no clear growth in exposure to generative AI.", "claim": "no clear growth in the generative-AI exposure of the unemployed", "modality": "documented", "kind": "fact", "kernel": false, "const": false, "lineage": "finding: exposure and usage against employment and unemployment", "date": "2025-10-01 (report date)", "quote_ok": true, "checked_against": "saved text B1.txt (sha256 8d18c4b7761bfe5f…), whitespace-normalised" }, "F22": { "source": "B1", "origin": "card", "locus": "'Insights from AI \"Exposure\" and Usage' ¶2", "quote": "Unfortunately, comprehensive usage data is not publicly available. The best available data we have is from OpenAI and Anthropic , respectively, that detail the occupations that are most “exposed” to genAI tools (a theoretical, forward-looking metric across all jobs) and that have the highest actual usage of one specific AI tool, Claude (a more narrow, present-focused metric).", "claim": "the data providers: OpenAI for exposure (theoretical, forward-looking) and Anthropic for actual usage of one tool, Claude; comprehensive usage data is not public", "modality": "documented", "kind": "attribution", "kernel": true, "const": false, "lineage": "usage data: its source, its reach and the data the Lab asks for", "date": "2025-10-01 (report date)", "quote_ok": true, "checked_against": "saved text B1.txt (sha256 8d18c4b7761bfe5f…), whitespace-normalised" }, "F23": { "source": "B1", "origin": "card", "locus": "'Anthropic's Usage Measure' ¶4", "quote": "Anthropic’s data on AI usage shows similar trends of stability over time, rather than disruption.", "claim": "Anthropic's usage data shows stability over time, not disruption", "modality": "documented", "kind": "fact", "kernel": false, "const": false, "lineage": "finding: exposure and usage against employment and unemployment", "date": "2025-10-01 (report date)", "quote_ok": true, "checked_against": "saved text B1.txt (sha256 8d18c4b7761bfe5f…), whitespace-normalised" }, "F24": { "source": "B1", "origin": "card", "locus": "'Data Limitations' ¶2", "quote": "A key limitation of OpenAI’s “exposure” data is that it is not based on actual usage, and should therefore be interpreted as a theoretical estimate of the jobs and sectors that could , in theory, be impacted.", "claim": "the exposure data's limit: not based on actual usage; a theoretical estimate of what could be impacted", "modality": "self-description", "kind": "function", "kernel": false, "const": false, "lineage": "exposure metrics: what they measure and what they do not", "date": "2025-10-01 (report date)", "quote_ok": true, "checked_against": "saved text B1.txt (sha256 8d18c4b7761bfe5f…), whitespace-normalised" }, "F25": { "source": "B1", "origin": "card", "locus": "'Data Limitations' ¶6", "quote": "Thus data from Claude usage alone is not representative of how workers across the economy are using AI chatbots and tools.", "claim": "the usage data's limit: Claude usage alone does not represent how workers across the economy use AI tools", "modality": "self-description", "kind": "function", "kernel": false, "const": false, "lineage": "usage data: its source, its reach and the data the Lab asks for", "date": "2025-10-01 (report date)", "quote_ok": true, "checked_against": "saved text B1.txt (sha256 8d18c4b7761bfe5f…), whitespace-normalised" }, "F26": { "source": "B1", "origin": "card", "locus": "'Data Limitations' ¶7", "quote": "To accurately measure AI’s impact on the labor force, the most important data needed is comprehensive usage data from all the leading AI companies at the individual and enterprise level, including APIs.", "claim": "the data the Lab asks for: comprehensive usage data from all the leading AI companies, individual and enterprise, APIs included", "modality": "interpretation", "kind": "interpretation", "kernel": false, "const": false, "lineage": "usage data: its source, its reach and the data the Lab asks for", "date": "2025-10-01 (report date)", "quote_ok": true, "checked_against": "saved text B1.txt (sha256 8d18c4b7761bfe5f…), whitespace-normalised" }, "F27": { "source": "B1", "origin": "card", "locus": "Summary", "quote": "While anxiety over the effects of AI on today’s labor market is widespread, our data suggests it remains largely speculative.", "claim": "the report's summary: widespread anxiety over AI's labor-market effects remains largely speculative on its data", "modality": "interpretation", "kind": "interpretation", "kernel": false, "const": false, "lineage": "headline: no clear economy-wide disruption", "date": "2025-10-01 (report date)", "quote_ok": true, "checked_against": "saved text B1.txt (sha256 8d18c4b7761bfe5f…), whitespace-normalised" }, "F28": { "source": "B1", "origin": "card", "locus": "Summary", "quote": "While generative AI looks likely to join the ranks of transformative, general purpose technologies, it is too soon to tell how disruptive the technology will be to jobs.", "claim": "too soon to tell how disruptive generative AI will be to jobs, though it looks likely to be a general purpose technology", "modality": "hypothesis", "kind": "prediction", "kernel": false, "const": false, "lineage": "the limit: not yet, and the findings may change", "date": "2025-10-01 (report date)", "quote_ok": true, "checked_against": "saved text B1.txt (sha256 8d18c4b7761bfe5f…), whitespace-normalised" }, "F29": { "source": "B2", "origin": "card", "locus": "Key Takeaways 1", "quote": "The occupational mix is not yet changing in ways that clearly align with the introduction of AI into the workforce.", "claim": "the tracker: the occupational mix is not yet changing in ways clearly aligned with AI's introduction", "modality": "documented", "kind": "fact", "kernel": true, "const": true, "lineage": "finding: the occupational mix", "date": "2026-10-14 (page date)", "quote_ok": true, "checked_against": "saved text B2.txt (sha256 94e3c0200ab0b481…), whitespace-normalised" }, "F30": { "source": "B2", "origin": "card", "locus": "Key Takeaways 2", "quote": "Measures of AI usage show no connection to changes in employment or unemployment.", "claim": "the tracker: AI usage measures show no connection to changes in employment or unemployment", "modality": "documented", "kind": "fact", "kernel": true, "const": false, "lineage": "finding: exposure and usage against employment and unemployment", "date": "2026-10-14 (page date)", "quote_ok": true, "checked_against": "saved text B2.txt (sha256 94e3c0200ab0b481…), whitespace-normalised" }, "F31": { "source": "B2", "origin": "card", "locus": "Key Takeaways 3", "quote": "A synthetic differences-in-differences analysis of AI exposure does not yet clearly indicate an AI-related labor market footprint.", "claim": "the tracker: the synthetic differences-in-differences analysis does not yet clearly indicate an AI-related labor-market footprint", "modality": "documented", "kind": "fact", "kernel": true, "const": true, "lineage": "finding: exposed against unexposed workers, employment and wages", "date": "2026-10-14 (page date)", "quote_ok": true, "checked_against": "saved text B2.txt (sha256 94e3c0200ab0b481…), whitespace-normalised" }, "F32": { "source": "B3", "origin": "card", "locus": "title, byline and date", "quote": "Labor Market AI Exposure: What Do We Know? Martha Gimbel , Joshua Kendall and Ryan Kulsakdinun February 19, 2026", "claim": "the exposure review: 'Labor Market AI Exposure: What Do We Know?', by Gimbel, Kendall and Kulsakdinun, 19 February 2026", "modality": "documented", "kind": "attribution", "kernel": false, "const": false, "lineage": "the analysis it continues: the first report and its documentation", "date": "2026-02-19 (report date)", "quote_ok": true, "checked_against": "saved text B3.txt (sha256 5a37e388f7ec1f93…), whitespace-normalised" }, "F33": { "source": "B3", "origin": "card", "locus": "Key Takeaways 1–2", "quote": "AI exposure metrics broadly agree with each other, but that they disagree with each other more on highly exposed occupations. The key point of disagreement between different AI exposure metrics is in the magnitude of exposure, not whether an occupation is exposed.", "claim": "exposure metrics broadly agree, disagree more on highly exposed occupations, and differ in magnitude more than in whether an occupation is exposed", "modality": "documented", "kind": "fact", "kernel": true, "const": false, "lineage": "exposure metrics: what they measure and what they do not", "date": "2026-02-19 (report date)", "quote_ok": true, "checked_against": "saved text B3.txt (sha256 5a37e388f7ec1f93…), whitespace-normalised" }, "F34": { "source": "B3", "origin": "card", "locus": "Key Takeaways 3", "quote": "Occupational exposure to AI is not indicative of a jobs AI will automate out of existence. Rather, it indicates places in the labor market where AI could have an impact.", "claim": "the governing limit: exposure does not indicate jobs AI will automate away, only places where AI could have an impact", "modality": "stipulation", "kind": "definition", "kernel": true, "const": true, "lineage": "exposure metrics: what they measure and what they do not", "date": "2026-02-19 (report date)", "quote_ok": true, "checked_against": "saved text B3.txt (sha256 5a37e388f7ec1f93…), whitespace-normalised" }, "F35": { "source": "B3", "origin": "card", "locus": "¶1", "quote": "Many analysts—including those at The Budget Lab—utilize Eloundou et al.’s exposure metrics, which measure Generative AI’s (GenAI) ability to speed up work task completion.", "claim": "the Lab among the analysts who use Eloundou et al.'s exposure metrics, which measure GenAI's ability to speed task completion", "modality": "documented", "kind": "attribution", "kernel": false, "const": false, "lineage": "exposure metrics: what they measure and what they do not", "date": "2026-02-19 (report date)", "quote_ok": true, "checked_against": "saved text B3.txt (sha256 5a37e388f7ec1f93…), whitespace-normalised" }, "F36": { "source": "B3", "origin": "card", "locus": "Appendix, Measures", "quote": "Microsoft researchers Tomlinson et al. (2025) created the ai_applicability_score. … They distinguish their work from other researchers by looking at individual conversations with Microsoft’s Copilot AI.", "claim": "one of the seven metrics is Microsoft researchers' AI applicability score, built from individual Copilot conversations", "modality": "documented", "kind": "fact", "kernel": false, "const": false, "lineage": "exposure metrics: what they measure and what they do not", "date": "2026-02-19 (report date)", "quote_ok": true, "checked_against": "saved text B3.txt (sha256 5a37e388f7ec1f93…), whitespace-normalised" }, "F37": { "source": "B3", "origin": "card", "locus": "'Where do we go from here?' ¶2", "quote": "This analysis is a reminder to be humble about how much we know about where AI disruption to the labor market could be going.", "claim": "the review's limit: humility about where AI disruption of the labor market could go", "modality": "self-description", "kind": "fact", "kernel": false, "const": false, "lineage": "the limit: not yet, and the findings may change", "date": "2026-02-19 (report date)", "quote_ok": true, "checked_against": "saved text B3.txt (sha256 5a37e388f7ec1f93…), whitespace-normalised" }, "F38": { "source": "B4", "origin": "card", "locus": "title, byline and date", "quote": "AI Is Probably Not (Yet) the Reason for Labor Market Weakening Ryan Nunn May 7, 2026", "claim": "the State of the Economy note: 'AI Is Probably Not (Yet) the Reason for Labor Market Weakening', Ryan Nunn, 7 May 2026", "modality": "documented", "kind": "attribution", "kernel": false, "const": false, "lineage": "the operator and the authors", "date": "2026-05-07 (note date)", "quote_ok": true, "checked_against": "saved text B4.txt (sha256 cb1a4388cdde94d6…), whitespace-normalised" }, "F39": { "source": "B4", "origin": "card", "locus": "Key Takeaways 3–4", "quote": "New Budget Lab research subjects the jobs data to an econometric method that makes AI-exposed and unexposed occupations comparable The results do not yet show clear indication of labor market effects of AI", "claim": "the note: new research makes exposed and unexposed occupations comparable, and its results do not yet show clear labor-market effects of AI", "modality": "documented", "kind": "fact", "kernel": true, "const": false, "lineage": "finding: exposed against unexposed workers, employment and wages", "date": "2026-05-07 (note date)", "quote_ok": true, "checked_against": "saved text B4.txt (sha256 cb1a4388cdde94d6…), whitespace-normalised" }, "F40": { "source": "B4", "origin": "card", "locus": "¶3", "quote": "A relatively recent methodological innovation, called synthetic differences-in-differences, helps to distinguish any genuine impact of AI on exposed occupations from the persistent differences between exposed and unexposed jobs.", "claim": "the method: synthetic differences-in-differences, separating a genuine AI impact on exposed occupations from persistent differences between exposed and unexposed jobs", "modality": "stipulation", "kind": "definition", "kernel": false, "const": false, "lineage": "method: occupational dissimilarity, and synthetic differences-in-differences", "date": "2026-05-07 (note date)", "quote_ok": true, "checked_against": "saved text B4.txt (sha256 cb1a4388cdde94d6…), whitespace-normalised" }, "F41": { "source": "B4", "origin": "card", "locus": "¶5", "quote": "The estimate is close to zero and cannot be distinguished from it, statistically speaking. The same is true for Figure 2, which shows impacts on inflation-adjusted hourly wages.", "claim": "the estimates for employment in the average exposed occupation and for inflation-adjusted hourly wages are close to zero and statistically indistinguishable from it", "modality": "documented", "kind": "fact", "kernel": false, "const": false, "lineage": "finding: exposed against unexposed workers, employment and wages", "date": "2026-05-07 (note date)", "quote_ok": true, "checked_against": "saved text B4.txt (sha256 cb1a4388cdde94d6…), whitespace-normalised" }, "F42": { "source": "B4", "origin": "card", "locus": "¶1", "quote": "From its post-pandemic low of 3.4% in April 2023, the unemployment rate has risen to 4.3% in March 2026—a higher though still healthy level. Both layoffs and hires have been low, with unemployed job seekers having an especially difficult and worsening experience.", "claim": "the market around it: unemployment from 3.4% (April 2023) to 4.3% (March 2026), low layoffs and low hires, a worsening experience for the unemployed", "modality": "documented", "kind": "fact", "kernel": false, "const": false, "lineage": "the labor market around it", "date": "2026-05-07 (note date)", "quote_ok": true, "checked_against": "saved text B4.txt (sha256 cb1a4388cdde94d6…), whitespace-normalised" }, "F43": { "source": "B4", "origin": "card", "locus": "¶2–3", "quote": "many have suspected that it bears the imprint of widespread AI adoption by employers. … However, the weight of the evidence—to date—does not support this conjecture.", "claim": "the conjecture that the cooled market bears the imprint of employers' AI adoption is not supported by the weight of the evidence to date", "modality": "interpretation", "kind": "interpretation", "kernel": true, "const": false, "lineage": "the labor market around it", "date": "2026-05-07 (note date)", "quote_ok": true, "checked_against": "saved text B4.txt (sha256 cb1a4388cdde94d6…), whitespace-normalised" }, "F44": { "source": "B4", "origin": "card", "locus": "¶6", "quote": "One feature of that previous analysis is that it does not rely on AI exposure metrics and is not subject to critiques of those measures. However, it does not formally attempt to identify effects of AI.", "claim": "the churn analysis: free of exposure metrics and their critiques, and not a formal attempt to identify AI's effects", "modality": "self-description", "kind": "function", "kernel": false, "const": false, "lineage": "method: occupational dissimilarity, and synthetic differences-in-differences", "date": "2026-05-07 (note date)", "quote_ok": true, "checked_against": "saved text B4.txt (sha256 cb1a4388cdde94d6…), whitespace-normalised" }, "F45": { "source": "B4", "origin": "card", "locus": "¶7", "quote": "AI seems quite likely to eventually leave its mark on the labor market, even if it has not already.", "claim": "the Lab's expectation: AI quite likely to leave its mark on the labor market eventually, even if not already", "modality": "hypothesis", "kind": "prediction", "kernel": true, "const": false, "lineage": "the limit: not yet, and the findings may change", "date": "2026-05-07 (note date)", "quote_ok": true, "checked_against": "saved text B4.txt (sha256 cb1a4388cdde94d6…), whitespace-normalised" }, "F46": { "source": "B4", "origin": "card", "locus": "footnote 1", "quote": "A principal reason for this low growth is the slowdown in net immigration, which reduced labor force growth.", "claim": "a principal reason for low payroll growth: the slowdown in net immigration", "modality": "interpretation", "kind": "interpretation", "kernel": false, "const": false, "lineage": "the labor market around it", "date": "2026-05-07 (note date)", "quote_ok": true, "checked_against": "saved text B4.txt (sha256 cb1a4388cdde94d6…), whitespace-normalised" }, "F47": { "source": "B5", "origin": "card", "locus": "card title (video; unreachable)", "quote": "Yale Budget Lab Executive Director Martha Gimbel on why AI hasn't disrupted ...", "claim": "a Quest Means Business video (5:33) in which the Lab's executive director speaks on why AI hasn't disrupted (the title truncated)", "modality": "attributed", "kind": "interpretation", "kernel": true, "const": false, "lineage": "reception: broadcast", "date": "undated on the card", "quote_ok": true, "checked_against": "card title (unreachable card; no snippet on the rail)" }, "F48": { "source": "B6", "origin": "own_page", "locus": "header", "quote": "Published: July 16, 2026 Updated: September 15, 2026", "claim": "the tracker published 16 July 2026, updated 15 September 2026", "modality": "documented", "kind": "fact", "kernel": false, "const": false, "lineage": "updates, versions and data access", "date": "2026-07-16 (published; this text updated 2026-09-15)", "quote_ok": true, "checked_against": "saved text B6.txt (sha256 6a1c4bade1ce5498…), whitespace-normalised" }, "F49": { "source": "B6", "origin": "own_page", "locus": "'AI Labor Market Tracker' panel", "quote": "Updated: September 15, 2026 Version 1.0", "claim": "Version 1.0, updated 15 September 2026", "modality": "documented", "kind": "fact", "kernel": false, "const": false, "lineage": "updates, versions and data access", "date": "2026-07-16 (published; this text updated 2026-09-15)", "quote_ok": true, "checked_against": "saved text B6.txt (sha256 6a1c4bade1ce5498…), whitespace-normalised" }, "F50": { "source": "B6", "origin": "own_page", "locus": "Current Update, September 15, 2026, ¶1", "quote": "The Budget Lab's labor market analysis, updated to incorporate August 2026 CPS microdata, does not provide clear evidence of labor market disruption associated with AI. Churn across occupations, AI exposure among the unemployed, and usage data all remain flat, lie within historical ranges, or continue along pre-AI trends.", "claim": "the September 2026 update, on August 2026 CPS microdata: no clear evidence of AI-associated disruption; churn, exposure among the unemployed and usage data flat, within historical ranges or on pre-AI trends", "modality": "documented", "kind": "fact", "kernel": true, "const": false, "lineage": "the September 2026 update", "date": "2026-07-16 (published; this text updated 2026-09-15)", "quote_ok": true, "checked_against": "saved text B6.txt (sha256 6a1c4bade1ce5498…), whitespace-normalised" }, "F51": { "source": "B6", "origin": "own_page", "locus": "Current Update, ¶2", "quote": "Of course, these findings may change as new data become available. The Budget Lab plans to continue monitoring AI-relevant trends to assess how job impacts might change.", "claim": "the tracker's stated limit: the findings may change with new data; monitoring continues", "modality": "self-description", "kind": "fact", "kernel": true, "const": true, "lineage": "the limit: not yet, and the findings may change", "date": "2026-07-16 (published; this text updated 2026-09-15)", "quote_ok": true, "checked_against": "saved text B6.txt (sha256 6a1c4bade1ce5498…), whitespace-normalised" }, "F52": { "source": "B6", "origin": "own_page", "locus": "Introduction ¶1", "quote": "Major categories of analysis can be accessed by clicking tabs towards the top of the page with the following names: Occupational churn, AI metrics, and Comparisons of AI-exposed and unexposed workers.", "claim": "three sections: occupational churn, AI metrics, comparisons of AI-exposed and unexposed workers", "modality": "stipulation", "kind": "definition", "kernel": false, "const": false, "lineage": "the unit: three sections on monthly CPS data", "date": "2026-07-16 (published; this text updated 2026-09-15)", "quote_ok": true, "checked_against": "saved text B6.txt (sha256 6a1c4bade1ce5498…), whitespace-normalised" }, "F53": { "source": "B6", "origin": "own_page", "locus": "Introduction ¶2", "quote": "Occupational churn, AI-exposure rates of the unemployed, and related analysis can be found in Gimbel et al. (2025). Details of AI exposure metrics are discussed in Gimbel, Kendall, and Kulsakdinun (2026).", "claim": "its documentation: Gimbel et al. (2025) for churn and the exposure of the unemployed; Gimbel, Kendall and Kulsakdinun (2026) for the exposure metrics", "modality": "documented", "kind": "attribution", "kernel": false, "const": false, "lineage": "the analysis it continues: the first report and its documentation", "date": "2026-07-16 (published; this text updated 2026-09-15)", "quote_ok": true, "checked_against": "saved text B6.txt (sha256 6a1c4bade1ce5498…), whitespace-normalised" }, "F54": { "source": "B6", "origin": "own_page", "locus": "'AI Labor Market Tracker' panel", "quote": "Download All Data", "claim": "the tracker's data downloadable ('Download All Data')", "modality": "documented", "kind": "fact", "kernel": false, "const": false, "lineage": "updates, versions and data access", "date": "2026-07-16 (published; this text updated 2026-09-15)", "quote_ok": true, "checked_against": "saved text B6.txt (sha256 6a1c4bade1ce5498…), whitespace-normalised" }, "F55": { "source": "B8", "origin": "wikipedia", "locus": "Wikipedia: six titles; rendered page; article-space search", "quote": "HTTP 404. Wikipedia does not have an article with this exact name. … There were no results matching the query.", "claim": "Wikipedia has no article on the tracker under its title or alias, nor on the Lab under four forms of its name, and no article text with the tracker's title", "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 B8.txt (sha256 0c9f7f1bb585d775…), whitespace-normalised" }, "F56": { "source": "B8", "origin": "wikipedia", "locus": "Generative AI, section 'Fears over job losses'", "quote": "However, a 2025 study concluded that the US labor market had so far not experienced a discernible disruption from generative AI.", "claim": "Wikipedia's article on generative AI reports the first report (cited by title and URL) as concluding no discernible disruption of the US labor market so far", "modality": "attributed", "kind": "fact", "kernel": false, "const": false, "lineage": "reception: the encyclopedia", "date": "2026-10-29 (as fetched; the citation accessed 2026-01-12)", "quote_ok": true, "checked_against": "saved text B8.txt (sha256 0c9f7f1bb585d775…), whitespace-normalised" } }, "field_sources": { "B1": "The Budget Lab — Evaluating the Impact of AI on the Labor Market: Current State of Affairs | The Budget Lab", "B2": "The Budget Lab — Tracking the Impact of AI on the Labor Market | The Budget Lab", "B3": "The Budget Lab — Labor Market AI Exposure: What Do We Know? | The Budget Lab", "B4": "The Budget Lab — AI Is Probably Not (Yet) the Reason for Labor Market Weakening | The Budget Lab", "B5": "YouTube·Quest Means Business — Yale Budget Lab Executive Director Martha Gimbel on why AI hasn't disrupted ...", "B6": "The Budget Lab — Tracking the Impact of AI on the Labor Market (printable PDF of the tracker, Version 1.0, 15 September 2026)", "B7": "The Budget Lab — Evaluating the Impact of AI on the Labor Market: September CPS Update (unread)", "B8": "Wikipedia — Tracking the Impact of AI on the Labor Market (no article)" }, "delta": { "T_vs_LB": "At 'Yale Budget Lab AI labor market' (T1, five cards: B1–B5, the video B5 unreachable; 230 words) the composition gives the tracker by its own page and the Lab's notes. Composed from its cards: the name by its alias (B6 F52's 'AI Labor Market Tracker'; LV-02) and the operator; the headline at the September update's grain widened to the first report's (B6 F50 'does not provide clear evidence of labor market disruption associated with AI'; B1 F12 'the broader labor market'; T1-1 'no clear evidence of large-scale or economy-wide labor market disruption'); the churn at the tracker's grain (B2 F29; B6 F50 'remain flat'; the 2022 launch of ChatGPT from B1 F12); the synthetic differences-in-differences design (B2 F31, B4 F39, F40); the three sections (B6 F52, a page not surfaced, the sections also on the card's live page); exposure metrics at the review's coarse grain (B3 F33, F35: T1-7, T1-9). Limit dropped: the 'not yet' of the tracker's own takeaways (B2 F29, F31: 'not yet changing', 'does not yet clearly indicate'), of the note (B4 F39 'do not yet show'; its title, card 4, 'Probably Not (Yet)'), and the stated limits that the findings 'may change as new data become available' (B6 F51), that the analysis 'is not predictive of the future' (B1 F13), that it is 'too soon to tell' (B1 F28) and that AI is 'quite likely to eventually leave its mark' (B4 F45): none is composed. The capture names this at its own grain ('NOT YET' FLATTENED, CAP-1-02; CAP-1-07). Force raised: 'does not yet clearly indicate an AI-related labor market footprint' (F31) composed as 'reveal no strong, consistent statistical footprint of AI-driven displacement' (T1-4), and 'no clear evidence of … disruption' (F50) widened to 'large-scale or economy-wide' (T1-1). Limit dropped, exposure: the review's governing limit that exposure 'is not indicative of a jobs AI will automate out of existence' (B3 F34) is absent from T1-7. Available, not composed: the usage finding, on card 2's own snippet ('Measures of AI usage show …'; B2 F30) and in the September update (B6 F50 'usage data'): T composes no usage claim at all; the usage data's provider, one tool's (B1 F22 Anthropic's Claude; F25 'not representative'; F26 the call for data from all leading AI companies); the first report's finding that the mix was changing 'more quickly than it has in the past' (B1 F6); recent graduates (B1 F17, F18); the labor market around it on card 4's own snippet (B4 F42 'unemployment rate … 4.3% in March 2026'; F43; F46); the Microsoft-built metric among the seven (B3 F36); the authors (B1 F4, F5; B3 F32; B4 F38), with Martha Gimbel named only on the video card. Assigned to another (by citation): 'wage trends' (T1-2) and 'wage … trajectories' (T1-8) are composed from the field, at a coarser grain, but from card 4 (B4 F41: hourly-wage effects 'close to zero' by synthetic differences-in-differences), while T cites them to [1] and [2], the first report and the tracker page, neither of which carries wages; the capture's finding that 'wage trends' are 'in no saved source' (CAP-1-03) holds for the census's saved sources and not for the field (see notes). Substituted reading: 'Data updated through September 2026' (T1-2) where the update is of September 15 on 'August 2026 CPS microdata' (B6 F50, F49; LV-11). Outside the cards: the 'Capital vs. Labor' line (T1-5: job cuts 'for broader macro or capital-investment reasons rather than direct workforce replacement by AI agents') and 'slight volatility in unemployment growth for high-exposure groups' (T1-4) are in no field source, card 4 and card 3 included; card 4 holds a different account of the cooled market (net immigration, F46; the AI conjecture unsupported, F43). The capture reads the line as 'a causal line about corporate job cuts added from beside it' (CAP-1-06). Content replaced by use: the closer (T1-9) offers a next step on high-exposure occupations and on how 'different AI exposure metrics compare' (N_ext). No composed claim is unattributed at its source's resolution: every bullet carries a marker.", "T_vs_LB_reading": "v0.9 §0.5: the first diff is ontological and narrow. T and the field agree on the entity (the Lab's tracker of AI's observed labor-market effect from CPS data). T composes the tracker's negative as settled: the field's provisional grammar ('not yet', 'may change', 'too soon to tell', 'quite likely to eventually leave its mark') is the governing limit of every one of the Lab's own sources on the rail, and T keeps none of it. It drops the usage finding the card snippet carries, and it adds a causal account of job cuts no card makes. What T removes is the field's own limit; it does not reach the archive.", "LB_vs_LBA": "Admission adds, in one claim space with the field's: the census's typing of the evidence (an observed employment outcome and an exposure estimate, LV-06) and its intersection (gains and losses, LV-05); what that evidence can and cannot settle, by the census's reading (LV-17, LV-18), with supplier, director and gains not recorded (LV-09) and the worked questions' gaps (LV-29, LV-31); the negative tags that keep the 'not yet' from a null (LV-24) and the tracker from a usage dataset (LV-25); the census's unread list, which this row's field partly answers (LV-26); and, from #716, the class's measure stated (which jobs AI eliminates, what tasks it can perform: O716-01) with a fourth channel it does not register, value extracted where labor remains (O716-02, O716-03). The capture's findings and reading enter as the archive's claims on the composition (CAP-1-01–CAP-1-08). No field claim is translated or displaced; the archive's claims stand beside the field's in their senses. Composition: the KO opens with the census's kind and the operator's own definition together (LV-01, LV-04, F1) and interleaves the ledgers sense by sense; KO_B survives 0 of 14 at ≥ 0.95.", "alignment": { "T1_cards_claims": [ "F2", "F3", "F4", "F5", "F6", "F7", "F8", "F9", "F10", "F11", "F12", "F13", "F14", "F15", "F16", "F17", "F18", "F19", "F20", "F21", "F22", "F23", "F24", "F25", "F26", "F27", "F28", "F29", "F30", "F31", "F32", "F33", "F34", "F35", "F36", "F37", "F38", "F39", "F40", "F41", "F42", "F43", "F44", "F45", "F46", "F47" ], "T1_composed": [ "F50", "F12", "F29", "F6", "F31", "F39", "F40", "F41", "F52", "F33", "F35", "F49", "F2" ], "T1_outside_cards": [ "'slight volatility in unemployment growth for high-exposure groups continues to be monitored' (T1-4; no field source)", "'Companies cutting jobs or restructuring are generally doing so for broader macro or capital-investment reasons rather than direct workforce replacement by AI agents' (T1-5; no field source; card 4 gives net immigration, F46, and the AI conjecture unsupported, F43)", "'Data updated through September 2026' (T1-2; the field: updated September 15 with August 2026 CPS microdata, F50, F49)", "'large-scale' (T1-1; no field source states scale)" ], "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 (§6.2)" } }, "kernel": [ { "K": "K1", "claim": "`O716-01`", "source": "#716", "M_src": "interpretation", "sense": "a channel the employment count does not register: value extracted where labor remains", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "missing distinction (the field has no claim on the point)", "resolver": "a later capture at 'Yale Budget Lab AI labor market' (ADDR-2f74f55d9377) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K2", "claim": "`O716-02`", "source": "#716", "M_src": "interpretation", "sense": "a channel the employment count does not register: value extracted where labor remains", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "missing distinction (the field has no claim on the point)", "resolver": "a later capture at 'Yale Budget Lab AI labor market' (ADDR-2f74f55d9377) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K3", "claim": "`O716-03`", "source": "#716", "M_src": "stipulation", "sense": "a channel the employment count does not register: value extracted where labor remains", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "missing distinction (the field has no claim on the point)", "resolver": "a later capture at 'Yale Budget Lab AI labor market' (ADDR-2f74f55d9377) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K4", "claim": "`CAP-1-01`", "source": "Capture Registry, 2026-10-29", "M_src": "documented", "sense": "the composition at the name", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "missing distinction (the field has no claim on the point)", "resolver": "a later capture at 'Yale Budget Lab AI labor market' (ADDR-2f74f55d9377) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K5", "claim": "`CAP-1-02`", "source": "Capture Registry, 2026-10-29", "M_src": "interpretation", "sense": "the composition at the name", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "missing distinction (the field has no claim on the point)", "resolver": "a later capture at 'Yale Budget Lab AI labor market' (ADDR-2f74f55d9377) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K6", "claim": "`CAP-1-03`", "source": "Capture Registry, 2026-10-29", "M_src": "interpretation", "sense": "the composition at the name", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "missing distinction (the field has no claim on the point)", "resolver": "a later capture at 'Yale Budget Lab AI labor market' (ADDR-2f74f55d9377) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K7", "claim": "`CAP-1-04`", "source": "Capture Registry, 2026-10-29", "M_src": "interpretation", "sense": "the composition at the name", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "missing distinction (the field has no claim on the point)", "resolver": "a later capture at 'Yale Budget Lab AI labor market' (ADDR-2f74f55d9377) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K8", "claim": "`CAP-1-05`", "source": "Capture Registry, 2026-10-29", "M_src": "interpretation", "sense": "the composition at the name", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "missing distinction (the field has no claim on the point)", "resolver": "a later capture at 'Yale Budget Lab AI labor market' (ADDR-2f74f55d9377) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K9", "claim": "`CAP-1-06`", "source": "Capture Registry, 2026-10-29", "M_src": "interpretation", "sense": "the composition at the name", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "missing distinction (the field has no claim on the point)", "resolver": "a later capture at 'Yale Budget Lab AI labor market' (ADDR-2f74f55d9377) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K10", "claim": "`CAP-1-07`", "source": "Capture Registry, 2026-10-29", "M_src": "interpretation", "sense": "the composition at the name", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "missing distinction (the field has no claim on the point)", "resolver": "a later capture at 'Yale Budget Lab AI labor market' (ADDR-2f74f55d9377) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K11", "claim": "`CAP-1-08`", "source": "Capture Registry, 2026-10-29", "M_src": "documented", "sense": "the composition at the name", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "missing distinction (the field has no claim on the point)", "resolver": "a later capture at 'Yale Budget Lab AI labor market' (ADDR-2f74f55d9377) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K12", "claim": "`LV-01`", "source": "Labor Vector census record, 2026-10-29", "M_src": "stipulation", "sense": "what it is: the Lab's ongoing tracker of AI's impact on the labor market", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "none (restates field claim F2: one lineage)", "resolver": "a later capture at 'Yale Budget Lab AI labor market' (ADDR-2f74f55d9377) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K13", "claim": "`LV-02`", "source": "Labor Vector census record, 2026-10-29", "M_src": "stipulation", "sense": "what it is: the Lab's ongoing tracker of AI's impact on the labor market", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "none (restates field claim F4: one lineage)", "resolver": "a later capture at 'Yale Budget Lab AI labor market' (ADDR-2f74f55d9377) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K14", "claim": "`LV-03`", "source": "Labor Vector census record, 2026-10-29", "M_src": "documented", "sense": "the operator and the authors", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "none (restates field claim F3: one lineage)", "resolver": "a later capture at 'Yale Budget Lab AI labor market' (ADDR-2f74f55d9377) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K15", "claim": "`LV-04`", "source": "Labor Vector census record, 2026-10-29", "M_src": "stipulation", "sense": "what it is: the Lab's ongoing tracker of AI's impact on the labor market", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "none (restates field claim F1: one lineage)", "resolver": "a later capture at 'Yale Budget Lab AI labor market' (ADDR-2f74f55d9377) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K16", "claim": "`LV-05`", "source": "Labor Vector census record, 2026-10-29", "M_src": "stipulation", "sense": "what it is: the Lab's ongoing tracker of AI's impact on the labor market", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "missing distinction (the field has no claim on the point)", "resolver": "a later capture at 'Yale Budget Lab AI labor market' (ADDR-2f74f55d9377) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K17", "claim": "`LV-06`", "source": "Labor Vector census record, 2026-10-29", "M_src": "interpretation", "sense": "what it is: the Lab's ongoing tracker of AI's impact on the labor market", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "missing distinction (the field has no claim on the point)", "resolver": "a later capture at 'Yale Budget Lab AI labor market' (ADDR-2f74f55d9377) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K18", "claim": "`LV-07`", "source": "Labor Vector census record, 2026-10-29", "M_src": "documented", "sense": "the unit: three sections on monthly CPS data", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "none (restates field claim F52: one lineage)", "resolver": "a later capture at 'Yale Budget Lab AI labor market' (ADDR-2f74f55d9377) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K19", "claim": "`LV-08`", "source": "Labor Vector census record, 2026-10-29", "M_src": "interpretation", "sense": "the unit: three sections on monthly CPS data", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "missing distinction (the field has no claim on the point)", "resolver": "a later capture at 'Yale Budget Lab AI labor market' (ADDR-2f74f55d9377) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K20", "claim": "`LV-09`", "source": "Labor Vector census record, 2026-10-29", "M_src": "interpretation", "sense": "what this evidence can and cannot settle", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "missing distinction (the field has no claim on the point)", "resolver": "a later capture at 'Yale Budget Lab AI labor market' (ADDR-2f74f55d9377) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K21", "claim": "`LV-10`", "source": "Labor Vector census record, 2026-10-29", "M_src": "documented", "sense": "method: occupational dissimilarity, and synthetic differences-in-differences", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "none (restates field claim F50: one lineage)", "resolver": "a later capture at 'Yale Budget Lab AI labor market' (ADDR-2f74f55d9377) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K22", "claim": "`LV-11`", "source": "Labor Vector census record, 2026-10-29", "M_src": "documented", "sense": "the September 2026 update", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "none (restates field claim F49, F2: one lineage)", "resolver": "a later capture at 'Yale Budget Lab AI labor market' (ADDR-2f74f55d9377) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K23", "claim": "`LV-12`", "source": "Labor Vector census record, 2026-10-29", "M_src": "documented", "sense": "finding: the occupational mix", "qualifiers carried": "LV-16, LV-24 (the stated limit and the negative tag)", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "none (restates field claim F29: one lineage)", "resolver": "a later capture at 'Yale Budget Lab AI labor market' (ADDR-2f74f55d9377) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K24", "claim": "`LV-13`", "source": "Labor Vector census record, 2026-10-29", "M_src": "documented", "sense": "finding: exposure and usage against employment and unemployment", "qualifiers carried": "LV-16, LV-24 (the stated limit and the negative tag)", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "none (restates field claim F30: one lineage)", "resolver": "a later capture at 'Yale Budget Lab AI labor market' (ADDR-2f74f55d9377) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K25", "claim": "`LV-14`", "source": "Labor Vector census record, 2026-10-29", "M_src": "documented", "sense": "finding: exposed against unexposed workers, employment and wages", "qualifiers carried": "LV-16, LV-24 (the stated limit and the negative tag)", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "none (restates field claim F31: one lineage)", "resolver": "a later capture at 'Yale Budget Lab AI labor market' (ADDR-2f74f55d9377) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K26", "claim": "`LV-15`", "source": "Labor Vector census record, 2026-10-29", "M_src": "documented", "sense": "the September 2026 update", "qualifiers carried": "LV-16, LV-24 (the stated limit and the negative tag)", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "none (restates field claim F50: one lineage)", "resolver": "a later capture at 'Yale Budget Lab AI labor market' (ADDR-2f74f55d9377) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K27", "claim": "`LV-16`", "source": "Labor Vector census record, 2026-10-29", "M_src": "self-description", "sense": "the limit: not yet, and the findings may change", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "none (restates field claim F51: one lineage)", "resolver": "a later capture at 'Yale Budget Lab AI labor market' (ADDR-2f74f55d9377) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K28", "claim": "`LV-17`", "source": "Labor Vector census record, 2026-10-29", "M_src": "interpretation", "sense": "what this evidence can and cannot settle", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "missing distinction (the field has no claim on the point)", "resolver": "a later capture at 'Yale Budget Lab AI labor market' (ADDR-2f74f55d9377) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K29", "claim": "`LV-18`", "source": "Labor Vector census record, 2026-10-29", "M_src": "interpretation", "sense": "what this evidence can and cannot settle", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "missing distinction (the field has no claim on the point)", "resolver": "a later capture at 'Yale Budget Lab AI labor market' (ADDR-2f74f55d9377) on Google AI Overview, seated in the Capture Registry (§8.3); for the census's unread and does-not-answer items, also a later census read of the same sources" }, { "K": "K30", "claim": "`LV-19`", "source": "Labor Vector census record, 2026-10-29", "M_src": "interpretation", "sense": "what this evidence can and cannot settle", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "missing distinction (the field has no claim on the point)", "resolver": "a later capture at 'Yale Budget Lab AI labor market' (ADDR-2f74f55d9377) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K31", "claim": "`LV-20`", "source": "Labor Vector census record, 2026-10-29", "M_src": "documented", "sense": "updates, versions and data access", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "none (restates field claim F54: one lineage)", "resolver": "a later capture at 'Yale Budget Lab AI labor market' (ADDR-2f74f55d9377) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K32", "claim": "`LV-21`", "source": "Labor Vector census record, 2026-10-29", "M_src": "documented", "sense": "updates, versions and data access", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "none (restates field claim F3: one lineage)", "resolver": "a later capture at 'Yale Budget Lab AI labor market' (ADDR-2f74f55d9377) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K33", "claim": "`LV-22`", "source": "Labor Vector census record, 2026-10-29", "M_src": "documented", "sense": "updates, versions and data access", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "none (restates field claim F48, F49, F2, F10: one lineage)", "resolver": "a later capture at 'Yale Budget Lab AI labor market' (ADDR-2f74f55d9377) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K34", "claim": "`LV-23`", "source": "Labor Vector census record, 2026-10-29", "M_src": "documented", "sense": "updates, versions and data access", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "none (restates field claim F48: one lineage)", "resolver": "a later capture at 'Yale Budget Lab AI labor market' (ADDR-2f74f55d9377) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K35", "claim": "`LV-24`", "source": "Labor Vector census record, 2026-10-29", "M_src": "interpretation", "sense": "the limit: not yet, and the findings may change", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "missing distinction (the field has no claim on the point)", "resolver": "a later capture at 'Yale Budget Lab AI labor market' (ADDR-2f74f55d9377) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K36", "claim": "`LV-25`", "source": "Labor Vector census record, 2026-10-29", "M_src": "interpretation", "sense": "usage data: its source, its reach and the data the Lab asks for", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "missing distinction (the field has no claim on the point)", "resolver": "a later capture at 'Yale Budget Lab AI labor market' (ADDR-2f74f55d9377) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K37", "claim": "`LV-26`", "source": "Labor Vector census record, 2026-10-29", "M_src": "self-description", "sense": "the analysis it continues: the first report and its documentation", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "missing distinction (the field has no claim on the point)", "resolver": "a later capture at 'Yale Budget Lab AI labor market' (ADDR-2f74f55d9377) on Google AI Overview, seated in the Capture Registry (§8.3); for the census's unread and does-not-answer items, also a later census read of the same sources" }, { "K": "K38", "claim": "`LV-27`", "source": "Labor Vector census record, 2026-10-29", "M_src": "stipulation", "sense": "what this evidence can and cannot settle", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "missing distinction (the field has no claim on the point)", "resolver": "a later capture at 'Yale Budget Lab AI labor market' (ADDR-2f74f55d9377) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K39", "claim": "`LV-28`", "source": "Labor Vector census record, 2026-10-29", "M_src": "interpretation", "sense": "what this evidence can and cannot settle", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "missing distinction (the field has no claim on the point)", "resolver": "a later capture at 'Yale Budget Lab AI labor market' (ADDR-2f74f55d9377) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K40", "claim": "`LV-29`", "source": "Labor Vector census record, 2026-10-29", "M_src": "interpretation", "sense": "what this evidence can and cannot settle", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "missing distinction (the field has no claim on the point)", "resolver": "a later capture at 'Yale Budget Lab AI labor market' (ADDR-2f74f55d9377) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K41", "claim": "`LV-30`", "source": "Labor Vector census record, 2026-10-29", "M_src": "interpretation", "sense": "what this evidence can and cannot settle", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "missing distinction (the field has no claim on the point)", "resolver": "a later capture at 'Yale Budget Lab AI labor market' (ADDR-2f74f55d9377) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K42", "claim": "`LV-31`", "source": "Labor Vector census record, 2026-10-29", "M_src": "interpretation", "sense": "a channel the employment count does not register: value extracted where labor remains", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "missing distinction (the field has no claim on the point)", "resolver": "a later capture at 'Yale Budget Lab AI labor market' (ADDR-2f74f55d9377) on Google AI Overview, seated in the Capture Registry (§8.3)" } ], "kernel_T": [ { "claim": "F31", "note": "'does not yet clearly indicate', composed as 'no strong, consistent statistical footprint' (the 'yet' dropped)" }, { "claim": "F29", "note": "'not yet changing', composed as 'remain flat' with no 'yet'" }, { "claim": "F51", "note": "'these findings may change as new data become available', absent" }, { "claim": "F45", "note": "'AI seems quite likely to eventually leave its mark', absent" }, { "claim": "F13", "note": "'not predictive of the future', absent" }, { "claim": "F30", "note": "the usage finding, on card 2's snippet, absent" }, { "claim": "F34", "note": "the review's limit on what exposure indicates, absent" }, { "claim": "F22", "note": "the usage data's provider (Anthropic's Claude), absent" }, { "claim": "F25", "note": "Claude usage 'not representative', absent" }, { "claim": "F43", "note": "the AI conjecture for the cooled market unsupported 'to date', absent; T composes a different cause (T1-5)" }, { "claim": "F42", "note": "the labor market around it (card 4's snippet: 4.3% in March 2026), absent" }, { "claim": "F41", "note": "hourly wages: composed coarse ('wage trends'), cited to cards 1 and 2" }, { "claim": "F47", "note": "the video card, on the rail only" }, { "claim": "F6", "note": "October 2025: the mix 'changing more quickly than it has in the past', absent" } ], "ledger": { "archive": "datasets/negative-of-the-negative/v2/ledgers/yale-budget-lab-ai-tracker/ledger-archive.json", "field": "datasets/negative-of-the-negative/v2/ledgers/yale-budget-lab-ai-tracker/field.json", "selection": "datasets/negative-of-the-negative/v2/traversal/yale-budget-lab-ai-tracker/", "config": "datasets/negative-of-the-negative/v2/panel/configs/yale-budget-lab-ai-tracker.json", "intake": "datasets/negative-of-the-negative/v2/intake/yale-budget-lab-ai-tracker-20261029/", "audit": null }, "ontology": { "composition": "equal", "composition_note": "equal-terms case (v0.9 §0.9, §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; the KO opens with the census's kind and the operator's own definition in one sentence and neither ledger is placed first or last as a block.", "case": { "value": "equal", "statement": "The archive reads the field's entity as the entity. The Labor Vector census record describes the tracker from the Lab's own pages, each field with its basis ('operator'), and answers for it the census's question (who supplies, directs and gains, and what kind of evidence the resource brings), recording its evidence as an observed employment outcome and an exposure estimate and stating what that evidence can and cannot settle; its negative tags keep the tracker's 'not yet' from a finding of no effect (LV-24) and the tracker from a usage dataset (LV-25). The capture record checks the composition against those pages (CAP-1-01, CAP-1-02). No deposit names the tracker or the Lab. One deposit bears on its class: #716 states what AI-economics research measures (labor displacement, capability projection: 'which jobs AI eliminates, what tasks it can perform') and adds, on that object, a channel it does not register, value extracted where labor remains ('This is not displacement; it is extraction without attribution'), as 'a fourth' beside the three it names. That is an addition of a sense and a limit to the field's own object, not a contest of its ontology; no defining paper supplies an ontology the field lacks: ρ = extends, the equal-terms case (§0.9, §1.2a). Labor Vector's own reading of where the tracker meets its instruments is 'none stated' (LV-19).", "loci": [ { "dep": 716, "locus": "§2.2 'Economic Framing of AI Composition'", "quote": "These frameworks measure which jobs AI eliminates, what tasks it can perform, and what consumer surplus it generates. PER identifies a distinct channel: even when human labor *remains* (the author's work is used), the economic value tied to provenance — reputation, traffic, citation credit, contractual rights — is extracted by the system." }, { "dep": 716, "locus": "§5.1 ¶1", "quote": "Existing AI-economics research addresses three channels: labor displacement, capability projection, and welfare estimation. PER addresses a fourth" }, { "dep": "LV", "locus": "census.json rows[slug == 'yale-budget-lab-ai-tracker'].negative_tags[0]", "quote": "NOT a finding that AI has no labor market effect: it states the analysis does not yet clearly indicate one" }, { "dep": "LV", "locus": "census.json rows[slug == 'yale-budget-lab-ai-tracker'].does_not_answer[0]", "quote": "who supplies, directs or gains from labor performed with AI" }, { "dep": "LV", "locus": "census.json rows[slug == 'yale-budget-lab-ai-tracker'].lv_intersection", "quote": "none stated" }, { "dep": "LV", "locus": "census.json schema_note", "quote": "lv_intersection (where it meets DSL, task-origin and user labor drag: the archive's reading, kept apart from the resource's own terms)" }, { "dep": "capture-registry", "locus": "yale-budget-lab-ai-labor-market-aio-20261029, reading", "quote": "The innocent reading first. The Overview names the tracker and its conclusion, gives its methods and its sections, from the Lab's own research pages." } ] }, "E_F": { "type": "C (public entity: a labor-market tracker and the reports behind it)", "definition": "The Budget Lab at Yale's interactive tool 'Tracking the Impact of AI on the Labor Market' (the AI Labor Market Tracker), published 16 July 2026, Version 1.0 of 15 September 2026, page of 14 October 2026, holding the Lab's 'ongoing analysis of potential AI impacts on the labor market' in three sections (occupational churn, AI metrics, comparisons of AI-exposed and unexposed workers) on monthly CPS microdata; continuing the report of 1 October 2025 by Gimbel, Kinder, Kendall and Lee; its exposure inputs OpenAI's (Eloundou et al.) and six other metrics, its usage input Anthropic's Claude data; its finding that AI's labor-market footprint is not yet clear, with the stated limit that the findings may change.", "claims": [ "F1", "F52", "F48", "F49", "F2", "F3", "F4", "F5", "F29", "F30", "F31", "F50", "F51", "F12", "F22", "F19", "F33", "F34", "F39", "F41", "F45" ] }, "E_A": { "definition": "the same tracker, in the Labor Vector census record and the archive's deposits: an interactive tool whose evidence is an observed employment outcome and an exposure estimate, at the intersection of gains and losses, recording no supplier, director or recipient of gains; its finding 'NOT a finding that AI has no labor market effect'; its usage measures inputs from providers its saved text does not name; able to settle churn, exposure among the unemployed and usage against employment, and unable to settle who supplies, directs or gains; a member of the AI-economics research #716 describes as measuring which jobs AI eliminates and what tasks it can perform, beside which #716 sets a fourth channel, value extracted where labor remains.", "claims": [ "LV-01", "LV-04", "LV-05", "LV-06", "LV-09", "LV-17", "LV-18", "LV-24", "LV-25", "LV-31", "O716-01", "O716-02", "O716-03" ], "defining_papers": [ 716 ], "defining_records": [ "Labor Vector census record (census.json rows[slug == 'yale-budget-lab-ai-tracker'], organizing_question, intersections, schema_note; questions.json; rendered aggregators/yale-budget-lab-ai-tracker/index.html; editor Lee Sharks, compiled 2026-10-29, schema 1.0)", "Capture Registry yale-budget-lab-ai-labor-market-aio-20261029 (OBS-9c44ea25d2ca)" ] }, "rho": { "value": "extends", "status": "read from the Labor Vector census record, the capture record and the one deposit that bears on the tracker's class (§1.2a); no deposit names the tracker or the Lab; #716 names the class's measures and adds a channel", "statement": "The archive reads the tracker as its operator presents it and adds, on the field's own object: the census's typing of the evidence and its intersection, what the evidence can and cannot settle, the direction it does not record, the two negative tags; and #716's channel, value extracted where labor remains, which an employment measure of the class does not register.", "claims": [ "LV-05", "LV-06", "LV-09", "LV-17", "LV-18", "LV-24", "LV-25", "LV-29", "LV-31", "O716-01", "O716-02", "O716-03" ], "guard": "ρ = extends claims an addition and no more (§1.2a). It does not make the field's sources wrong at their dates: #716 does not say the Lab's measure is wrong, only that its class measures displacement and capability and not extraction where labor remains. Where an archive claim stands against a field claim, both are composed under P_coh within the one ontology; none does here.", "considered": [ { "value": "constructs", "rejected": "the archive does not read the tracker as made by reception; its records take the Lab's pages as the entity's own account" }, { "value": "revises", "rejected": "the tracker 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: the Lab's ongoing tracker of AI's impact on the labor market", "field_claims": [ "F1" ], "received": "F1 (B6, Introduction ¶1): \"This interactive tool contains The Budget Lab's ongoing analysis of potential AI impacts on the labor market.\"", "receiving_text": "—", "translation": "untranslated: composed as received beside LV-01, LV-02, LV-04, LV-05, LV-06", "archive_claims": [ "LV-01", "LV-02", "LV-04", "LV-05", "LV-06" ] }, { "lineage": "the operator and the authors", "field_claims": [ "F5", "F38", "F3" ], "received": "F5 (B1, Authors): \"Martha Gimbel , Executive Director of the Budget Lab Molly Kinder , Senior Fellow – Brookings Metro Joshua Kendall , Research Fellow at the Budget Lab Maddie Lee , Research Assistant at the Budget Lab\"; F38 (B4, title, byline and date): \"AI Is Probably Not (Yet) the Reason for Labor Market Weakening Ryan Nunn May 7, 2026\"; F3 (B2, footer): \"Copyright ©2026 Yale University. All rights reserved. The Budget Lab 127 Wall Street, New Haven, CT 06511\"", "receiving_text": "—", "translation": "untranslated: composed as received beside LV-03", "archive_claims": [ "LV-03" ] }, { "lineage": "the analysis it continues: the first report and its documentation", "field_claims": [ "F4", "F32", "F53" ], "received": "F4 (B1, title, byline and date): \"Evaluating the Impact of AI on the Labor Market: Current State of Affairs Martha Gimbel , Molly Kinder , Joshua Kendall and Maddie Lee October 1, 2025\"; F32 (B3, title, byline and date): \"Labor Market AI Exposure: What Do We Know? Martha Gimbel , Joshua Kendall and Ryan Kulsakdinun February 19, 2026\"; F53 (B6, Introduction ¶2): \"Occupational churn, AI-exposure rates of the unemployed, and related analysis can be found in Gimbel et al. (2025). Details of AI exposure metrics are discussed in Gimbel, Kendall, and Kulsakdinun (2026).\"", "receiving_text": "—", "translation": "untranslated: composed as received beside LV-26", "archive_claims": [ "LV-26" ] }, { "lineage": "updates, versions and data access", "field_claims": [ "F9", "F48", "F49", "F54", "F2", "F10" ], "received": "F9 (B1, Key Takeaways 4): \"We plan on updating this analysis regularly moving forward to see how the impact of AI on the labor market changes over time.\"; F48 (B6, header): \"Published: July 16, 2026 Updated: September 15, 2026\"; F49 (B6, 'AI Labor Market Tracker' panel): \"Updated: September 15, 2026 Version 1.0\"; F54 (B6, 'AI Labor Market Tracker' panel): \"Download All Data\"; F2 (B2, title and dateline): \"Tracking the Impact of AI on the Labor Market October 14, 2026 | Updated: October 14, 2026\"; F10 (B2, Key Takeaways 4): \"We plan on updating this analysis regularly moving forward to see how the impact of AI on the labor market changes over time.\"", "receiving_text": "—", "translation": "untranslated: composed as received beside LV-20, LV-21, LV-22, LV-23", "archive_claims": [ "LV-20", "LV-21", "LV-22", "LV-23" ] }, { "lineage": "the unit: three sections on monthly CPS data", "field_claims": [ "F52" ], "received": "F52 (B6, Introduction ¶1): \"Major categories of analysis can be accessed by clicking tabs towards the top of the page with the following names: Occupational churn, AI metrics, and Comparisons of AI-exposed and unexposed workers.\"", "receiving_text": "—", "translation": "untranslated: composed as received beside LV-07, LV-08", "archive_claims": [ "LV-07", "LV-08" ] }, { "lineage": "method: occupational dissimilarity, and synthetic differences-in-differences", "field_claims": [ "F11", "F14", "F15", "F40", "F44" ], "received": "F11 (B1, ¶3): \"First, is the pace of labor market change in this 33-month period of employment disruption different from past periods of early technological change? Second, is there evidence of economy-wide employment effects?\"; F14 (B1, Appendix ¶2): \"We adapt Duncan and Duncan’s methodology to construct a dissimilarity index of the change in the occupational mix over time using monthly CPS data.\"; F15 (B1, 'Is this Time Different?' ¶3): \"Note that a change over this time period simply reflects change — it does not take a stance on the cause of that change.\"; F40 (B4, ¶3): \"A relatively recent methodological innovation, called synthetic differences-in-differences, helps to distinguish any genuine impact of AI on exposed occupations from the persistent differences between exposed and unexposed jobs.\"; F44 (B4, ¶6): \"One feature of that previous analysis is that it does not rely on AI exposure metrics and is not subject to critiques of those measures. However, it does not formally attempt to identify effects of AI.\"", "receiving_text": "—", "translation": "untranslated: composed as received beside LV-10", "archive_claims": [ "LV-10" ] }, { "lineage": "headline: no clear economy-wide disruption", "field_claims": [ "F12", "F27" ], "received": "F12 (B1, ¶4): \"Overall, our metrics indicate that the broader labor market has not experienced a discernible disruption since ChatGPT’s release 33 months ago, undercutting fears that AI automation is currently eroding the demand for cognitive labor across the economy.\"; F27 (B1, Summary): \"While anxiety over the effects of AI on today’s labor market is widespread, our data suggests it remains largely speculative.\"", "receiving_text": "—", "translation": "untranslated: composed as received (no archive claim in this lineage)", "archive_claims": [] }, { "lineage": "finding: the occupational mix", "field_claims": [ "F6", "F16", "F29" ], "received": "F6 (B1, Key Takeaways 1): \"While the occupational mix is changing more quickly than it has in the past, it is not a large difference and predates the widespread introduction of AI in the workforce.\"; F16 (B1, 'Is this Time Different?' ¶6): \"Repeating this analysis by industry similarly suggests a limited effect of AI.\"; F29 (B2, Key Takeaways 1): \"The occupational mix is not yet changing in ways that clearly align with the introduction of AI into the workforce.\"", "receiving_text": "—", "translation": "untranslated: composed as received beside LV-12", "archive_claims": [ "LV-12" ] }, { "lineage": "finding: exposure and usage against employment and unemployment", "field_claims": [ "F7", "F20", "F21", "F23", "F30" ], "received": "F7 (B1, Key Takeaways 2): \"Currently, measures of exposure, automation, and augmentation show no sign of being related to changes in employment or unemployment.\"; F20 (B1, 'OpenAI's \"Exposure\" Data' ¶4): \"The share of workers in the lowest, middle, and highest occupational exposure groups stay stable at around 29%, 46% and 18%, respectively.\"; F21 (B1, 'OpenAI's \"Exposure\" Data' ¶5): \"Even when specifically examining the unemployed population, there is no clear growth in exposure to generative AI.\"; F23 (B1, 'Anthropic's Usage Measure' ¶4): \"Anthropic’s data on AI usage shows similar trends of stability over time, rather than disruption.\"; F30 (B2, Key Takeaways 2): \"Measures of AI usage show no connection to changes in employment or unemployment.\"", "receiving_text": "—", "translation": "untranslated: composed as received beside LV-13", "archive_claims": [ "LV-13" ] }, { "lineage": "finding: exposed against unexposed workers, employment and wages", "field_claims": [ "F39", "F41", "F31" ], "received": "F39 (B4, Key Takeaways 3–4): \"New Budget Lab research subjects the jobs data to an econometric method that makes AI-exposed and unexposed occupations comparable The results do not yet show clear indication of labor market effects of AI\"; F41 (B4, ¶5): \"The estimate is close to zero and cannot be distinguished from it, statistically speaking. The same is true for Figure 2, which shows impacts on inflation-adjusted hourly wages.\"; F31 (B2, Key Takeaways 3): \"A synthetic differences-in-differences analysis of AI exposure does not yet clearly indicate an AI-related labor market footprint.\"", "receiving_text": "—", "translation": "untranslated: composed as received beside LV-14", "archive_claims": [ "LV-14" ] }, { "lineage": "the September 2026 update", "field_claims": [ "F50" ], "received": "F50 (B6, Current Update, September 15, 2026, ¶1): \"The Budget Lab's labor market analysis, updated to incorporate August 2026 CPS microdata, does not provide clear evidence of labor market disruption associated with AI. Churn across occupations, AI exposure among the unemployed, and usage data all remain flat, lie within historical ranges, or continue along pre-AI trends.\"", "receiving_text": "—", "translation": "untranslated: composed as received beside LV-11, LV-15", "archive_claims": [ "LV-11", "LV-15" ] }, { "lineage": "recent college graduates", "field_claims": [ "F17", "F18" ], "received": "F17 (B1, 'Recent College Graduates' ¶1): \"If generative AI were in fact substantially changing the labor market for recent college graduates, we would expect to see a growing dissimilarity in the occupational mix between these two groups. … However, our results should be interpreted with caution particularly given small sample sizes.\"; F18 (B1, 'Recent College Graduates' ¶1): \"The dissimilarity has increased slightly faster in recent months than it did in a previous time period, which could be consistent with a recent paper from Brynjolfsson et al. showing a possible impact of AI on employment of early career workers. It could also simply reflect a slowing labor market.\"", "receiving_text": "—", "translation": "untranslated: composed as received (no archive claim in this lineage)", "archive_claims": [] }, { "lineage": "the labor market around it", "field_claims": [ "F42", "F43", "F46" ], "received": "F42 (B4, ¶1): \"From its post-pandemic low of 3.4% in April 2023, the unemployment rate has risen to 4.3% in March 2026—a higher though still healthy level. Both layoffs and hires have been low, with unemployed job seekers having an especially difficult and worsening experience.\"; F43 (B4, ¶2–3): \"many have suspected that it bears the imprint of widespread AI adoption by employers. … However, the weight of the evidence—to date—does not support this conjecture.\"; F46 (B4, footnote 1): \"A principal reason for this low growth is the slowdown in net immigration, which reduced labor force growth.\"", "receiving_text": "—", "translation": "untranslated: composed as received (no archive claim in this lineage)", "archive_claims": [] }, { "lineage": "the limit: not yet, and the findings may change", "field_claims": [ "F13", "F28", "F37", "F45", "F51" ], "received": "F13 (B1, ¶6): \"Of course, our analysis is not predictive of the future.\"; F28 (B1, Summary): \"While generative AI looks likely to join the ranks of transformative, general purpose technologies, it is too soon to tell how disruptive the technology will be to jobs.\"; F37 (B3, 'Where do we go from here?' ¶2): \"This analysis is a reminder to be humble about how much we know about where AI disruption to the labor market could be going.\"; F45 (B4, ¶7): \"AI seems quite likely to eventually leave its mark on the labor market, even if it has not already.\"; F51 (B6, Current Update, ¶2): \"Of course, these findings may change as new data become available. The Budget Lab plans to continue monitoring AI-relevant trends to assess how job impacts might change.\"", "receiving_text": "—", "translation": "untranslated: composed as received beside LV-16, LV-24", "archive_claims": [ "LV-16", "LV-24" ] }, { "lineage": "exposure metrics: what they measure and what they do not", "field_claims": [ "F19", "F24", "F33", "F34", "F35", "F36" ], "received": "F19 (B1, 'OpenAI's \"Exposure\" Data' ¶1): \"This refers, generally, to whether utilizing ChatGPT4 technology can help reduce the time it takes to complete the occupation’s tasks by at least 50%.\"; F24 (B1, 'Data Limitations' ¶2): \"A key limitation of OpenAI’s “exposure” data is that it is not based on actual usage, and should therefore be interpreted as a theoretical estimate of the jobs and sectors that could , in theory, be impacted.\"; F33 (B3, Key Takeaways 1–2): \"AI exposure metrics broadly agree with each other, but that they disagree with each other more on highly exposed occupations. The key point of disagreement between different AI exposure metrics is in the magnitude of exposure, not whether an occupation is exposed.\"; F34 (B3, Key Takeaways 3): \"Occupational exposure to AI is not indicative of a jobs AI will automate out of existence. Rather, it indicates places in the labor market where AI could have an impact.\"; F35 (B3, ¶1): \"Many analysts—including those at The Budget Lab—utilize Eloundou et al.’s exposure metrics, which measure Generative AI’s (GenAI) ability to speed up work task completion.\"; F36 (B3, Appendix, Measures): \"Microsoft researchers Tomlinson et al. (2025) created the ai_applicability_score. … They distinguish their work from other researchers by looking at individual conversations with Microsoft’s Copilot AI.\"", "receiving_text": "—", "translation": "untranslated: composed as received (no archive claim in this lineage)", "archive_claims": [] }, { "lineage": "usage data: its source, its reach and the data the Lab asks for", "field_claims": [ "F8", "F22", "F25", "F26" ], "received": "F8 (B1, Key Takeaways 3): \"Better data is needed to fully understand the impact of AI on the labor market.\"; F22 (B1, 'Insights from AI \"Exposure\" and Usage' ¶2): \"Unfortunately, comprehensive usage data is not publicly available. The best available data we have is from OpenAI and Anthropic , respectively, that detail the occupations that are most “exposed” to genAI tools (a theoretical, forward-looking metric across all jobs) and that have the highest actual usage of one specific AI tool, Claude (a more narrow, present-focused metric).\"; F25 (B1, 'Data Limitations' ¶6): \"Thus data from Claude usage alone is not representative of how workers across the economy are using AI chatbots and tools.\"; F26 (B1, 'Data Limitations' ¶7): \"To accurately measure AI’s impact on the labor force, the most important data needed is comprehensive usage data from all the leading AI companies at the individual and enterprise level, including APIs.\"", "receiving_text": "—", "translation": "untranslated: composed as received beside LV-25", "archive_claims": [ "LV-25" ] }, { "lineage": "reception: the encyclopedia", "field_claims": [ "F55", "F56" ], "received": "F55 (B8, Wikipedia: six titles; rendered page; article-space search): \"HTTP 404. Wikipedia does not have an article with this exact name. … There were no results matching the query.\"; F56 (B8, Generative AI, section 'Fears over job losses'): \"However, a 2025 study concluded that the US labor market had so far not experienced a discernible disruption from generative AI.\"", "receiving_text": "—", "translation": "untranslated: composed as received (no archive claim in this lineage)", "archive_claims": [] }, { "lineage": "reception: broadcast", "field_claims": [ "F47" ], "received": "F47 (B5, card title (video; unreachable)): \"Yale Budget Lab Executive Director Martha Gimbel on why AI hasn't disrupted ...\"", "receiving_text": "—", "translation": "untranslated: composed as received (no archive claim in this lineage)", "archive_claims": [] } ], "receiving_texts": "equal-terms case: every field claim composed as received, beside the archive claims it stands with" }, "measure": { "rule": "v0.9 §4.10: KO_B sentences surviving in the KO at similarity ≥ 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": 14, "positions": [ null, null, null, null, null, null, null, null, null, null, null, null, null, null ] }, "opening_run": { "rule": "v0.9 §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 §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": "MMMMMMFMMMFFFFFMAFFMAAMAAAAAFF", "field_only_head": 0, "archive_only_tail": 0, "breach": false }, "max_similarity": [ 0.358, 0.527, 0.427, 0.282, 0.165, 0.146, 0.455, 0.215, 0.384, 0.642, 0.191, 0.175, 0.597, 0.419 ], "ko_sentence_1_max_similarity_to_KO_B": 0.267 }, "homonyms": { "names": [], "basis": "The census row carries no disambiguation field. No card is a homonym: all five concern the entity (four are the operator's own pages; the video carries the operator's executive director). 'AI Labor Market Tracker' and 'Evaluating the Impact of AI on the Labor Market' are the census's other names for it; the second is also the title of a distinct document, the first report of 1 October 2025 (B1), which the tracker continues. The distinct entities below are named by the field or the census beside the tracker and are never senses of its name.", "distinct_entities": [ "The Budget Lab at Yale (the operator; Wikipedia has no article on it)", "'Evaluating the Impact of AI on the Labor Market: Current State of Affairs' (the first report, 1 October 2025, Gimbel, Kinder, Kendall and Lee; B1), a document the tracker continues, whose title the census carries as an aka", "'Labor Market AI Exposure: What Do We Know?' (B3) and 'AI Is Probably Not (Yet) the Reason for Labor Market Weakening' (B4), the Lab's separate pieces", "Brookings Metro (Molly Kinder's institution, B1)", "OpenAI's exposure data (Eloundou et al.) and Anthropic's Claude usage data (B1), the tracker's inputs; Anthropic's usage data is the object of the census's neighbour record anthropic-economic-index", "Microsoft researchers' AI applicability score from Copilot conversations (Tomlinson et al. 2025; B3), the object of the census's neighbour record microsoft-working-with-ai (/non row datasets/negative-of-the-negative/v2/rows/microsoft-working-with-ai.json)", "Brynjolfsson et al.'s study of early-career workers (B1), the census's neighbour record stanford-canaries (the other observed tracker of the worked question 'observed or estimated'; /non row datasets/negative-of-the-negative/v2/rows/stanford-canaries.json)", "Quest Means Business (the programme of the video card, B5)" ], "substitutes": { "T": [] }, "register": { "address_1": { "slug": "yale-budget-lab-ai-labor-market-aio-20261029", "obs_id": "OBS-9c44ea25d2ca" } } }, "notes": [ "One address, Google AI Overview (expanded), signed out, incognito, 2026-10-29: yale-budget-lab-ai-labor-market-aio-20261029 (OBS-9c44ea25d2ca, ADDR-2f74f55d9377, AXN:0AF6.CAPTURE.⛳🌲🌋⏳🔐💜), five cards, 230 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).", "Fetching: every reachable page saved verbatim with curl and the agent string 'Mozilla/5.0 (compatible; Labor-Vector-census)' on 2026-10-29 between 22:30 and 22:34 UTC, raw and text with sha256 in field.json. Card 1 (the first report), which the census recorded as 403 ('has been banned') and left unread, answered 200 to the same agent string at 22:30:32Z. Card 3 answered 403 at the first fetch and 200 to one later fetch with the same agent string and command; no other agent string or route was tried for any source. Card 5, the video, has no URL that three searches could find; its title stands. B7, the September CPS update page, is recorded as unread, as the census records it.", "Census record, shown out of date or incomplete by the fetched sources (report; not fixed): (1) unread_sources and unchecked list the first report as 403 and unread; it is now read (B1, HTTP 200). (2) does_not_answer and unchecked: 'which usage data it draws on, or their provider (unread in the saved text)': the first report names them, OpenAI's exposure data and Anthropic's usage data on Claude (B1 F22, F19), and the exposure review names seven exposure metrics including Microsoft researchers' Copilot-based score (B3 F36). (3) unchecked: 'Gimbel et al. 2025; Gimbel, Kendall and Kulsakdinun 2026; both unread': they match by authors and year cards 1 and 3, now read. (4) founded '2026-07-16' and lifecycle: the tracker's PDF date; the analysis it continues, under the census's own aka 'Evaluating the Impact of AI on the Labor Market', was published 1 October 2025 with the same plan of regular updates (B1 F4, F9). (5) The census's source id 'yale-budget-lab-ai-tracker--first-report-pdf' names a PDF whose content is the tracker (published 16 July 2026, updated 15 September 2026), not the first report. (6) The census's operator is the Lab alone; the first report's authors include Molly Kinder of Brookings Metro (B1 F5). (7) coverage.geography 'not named in the saved text': the first report's subject is 'how U.S. employment has changed' (B1, ¶1), not ledgered as a claim.", "Capture findings the field ledger shows wrong or partial (report; not fixed): CAP-1-03 states that 'wage trends' are 'in no saved source' and that the 'Capital vs. Labor' line and the 'slight volatility' 'rest on the Lab's other pages (cards 3 and 4), which the record did not read'. Read whole, card 4 (B4) carries wages, the synthetic differences-in-differences estimate for 'inflation-adjusted hourly wages' close to zero (F41), so T's wage claims are in the field, though cited by T to cards 1 and 2, which do not carry them; and neither card 3 (B3, the exposure review) nor card 4 carries the 'Capital vs. Labor' line or any 'volatility' in unemployment growth: both are outside every card. Card 4's account of the cooled market is net immigration (F46) and the AI conjecture unsupported to date (F43). CAP-1-04 ('the composition's AI Metrics section is the Lab's exposure review (card 3)') agrees with the field: T1-7 cites a [1] token distinct from card 1's, and card 3's snippet names the seven metrics.", "The usage finding: the tracker's second takeaway ('Measures of AI usage show no connection to changes in employment or unemployment', F30) is on card 2's own snippet and in the September update (F50); T composes no claim on usage. The capture's PER-dim notes do not name this omission; the row records it as available, not composed.", "Platform-specific inputs (substitution 6 does not name this row): the tracker's usage input is one provider's observation of its own product (Anthropic's Claude data, B1 F22), and one of the seven exposure metrics is built by Microsoft researchers from Copilot conversations (B3 F36); both are kept identifiable in the KO (sentences 18–20) and in the homonyms' distinct entities.", "Neighbours in the census: stanford-canaries (the other observed tracker of the worked question 'observed or estimated'; its /non row datasets/negative-of-the-negative/v2/rows/stanford-canaries.json), microsoft-working-with-ai (whose Copilot-based score is one of the seven exposure metrics, B3 F36; datasets/negative-of-the-negative/v2/rows/microsoft-working-with-ai.json) and anthropic-economic-index (whose usage data is the tracker's usage input, B1 F22; no /non row found at the time of building). None was edited.", "No git was run; only new files were created: the intake folder, the ledgers, the config, the traversal folder (candidates and summary by scripts/non_traverse.py, reading.json by hand) and this row. 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the two 403 responses)" }, { "what": "capture record", "file": "data/EA-WG-CAPTURES-01.json (yale-budget-lab-ai-labor-market-aio-20261029)", "read": "transcript, transcript_raw, cite_list, d, reading, findings, analysis, q_kind, per_note, per_dim, originator" }, { "dep": 716, "file": "data/texts/AXN-025F-text.md", "read": "§2.2 and §5.1 (the economic framing and the fourth channel), with the class sentences of the whole text" }, { "dep": 771, "file": "data/texts/AXN-02D1-text.md", "read": "title, header and the terms of direction (for the classes_note; not admitted)" }, { "what": "the specification", "file": "data/texts/AXN-0700-text.md", "read": "§0–§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 rows", "file": "datasets/negative-of-the-negative/v2/rows/olmotrace.json and its ledgers, config and reading", "read": "whole, as the shape" } ], "cuts": [], "notes": "equal case; 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The census recorded this page as HTTP 403 ('has been banned') at 21:49:16Z and left it unread; the card fetch with the same agent string answered 200 at 22:30:32Z. 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Byte-identical to the census's saved copy (sha256 7efbd4d5…, read the same day) and its extracted text identical to the census's .txt, so the census's own page is carried once, as this card.", "surfaced_by_T": [ { "slug": "yale-budget-lab-ai-labor-market-aio-20261029", "card": 2 } ], "card_title": "Tracking the Impact of AI on the Labor Market | The Budget Lab", "card_snippet": "Key Takeaways * The occupational mix is not yet changing in ways that clearly align with the introduction of AI into the workforce. * Measures of AI usage show ...", "card_note": null }, { "id": "B3", "origin": "card", "role": "Overview card 3", "card": "The Budget Lab — Labor Market AI Exposure: What Do We Know? | The Budget Lab", "url": "https://budgetlab.yale.edu/research/labor-market-ai-exposure-what-do-we-know", "kind": "Overview card (the Lab's exposure review, 19 February 2026, Gimbel, Kendall and Kulsakdinun: the 'Gimbel, Kendall, and Kulsakdinun (2026)' the tracker cites)", "files": { "B3.html": { "path": "datasets/negative-of-the-negative/v2/intake/yale-budget-lab-ai-tracker-20261029/B3.html", "sha256": "c17cecc68b86f6562b742b51d4cc0c8c335c304105c60b5da803da33843e7e4a" }, "B3.txt": { "path": "datasets/negative-of-the-negative/v2/intake/yale-budget-lab-ai-tracker-20261029/B3.txt", "sha256": "5a37e388f7ec1f93c495cb637507058db4944dbf9effc3f647385f0d34db391d" } }, "fetched": { "tries": [ { "at": "between 2026-10-29T22:30:34Z and 22:31:34Z", "http": 403, "bytes": 30, "url": "https://budgetlab.yale.edu/research/labor-market-ai-exposure-what-do-we-know", "result": "refused (403, 30 bytes); the body was written to a scratch probe file that the next probe overwrote, so it is not kept; the Lab's server answered other refusals that minute with ' has been banned'" }, { "at": "2026-10-29T22:34:11Z", "http": 200, "url": "https://budgetlab.yale.edu/research/labor-market-ai-exposure-what-do-we-know", "sha256": "c17cecc68b86f6562b742b51d4cc0c8c335c304105c60b5da803da33843e7e4a", "result": "the page (canonical link and og:title confirm the card title)" } ], "agent": "Mozilla/5.0 (compatible; Labor-Vector-census)", "raw": "datasets/negative-of-the-negative/v2/intake/yale-budget-lab-ai-tracker-20261029/B3.html", "raw_sha256": "c17cecc68b86f6562b742b51d4cc0c8c335c304105c60b5da803da33843e7e4a", "text": [ "datasets/negative-of-the-negative/v2/intake/yale-budget-lab-ai-tracker-20261029/B3.txt" ] }, "names_operator": "yes ('The Budget Lab', the authors at the Lab)", "names_archive": "no (none of the Crimson Hexagonal Archive, Alexanarch, Lee Sharks, laborvector, provenanceerasure, machinemediation, EA-WG-CAPTURES-01 in the saved text, card title and snippet)", "note": "URL linked from the Lab's own pages (B1, B4 href to research/labor-market-ai-exposure-what-do-we-know); two WebSearches on the title returned other Budget Lab pages. First fetch refused (HTTP 403); one later fetch with the same agent string and the same command answered 200. 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The note's PDF (node/1495/pdf) answered 403 to a probe and is not a source of the row.", "surfaced_by_T": [ { "slug": "yale-budget-lab-ai-labor-market-aio-20261029", "card": 4 } ], "card_title": "AI Is Probably Not (Yet) the Reason for Labor Market Weakening | The Budget Lab", "card_snippet": "Labor Market Conditions: Cooling economy with low layoffs, low hiring, and an unemployment rate rising to 4.3% by March 2026. 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Admitted through its title (§3.1(a): a video card through title and snippet); the title stands as its only text." }, "names_operator": "yes ('Yale Budget Lab Executive Director Martha Gimbel', in the title)", "names_archive": "no (none of the Crimson Hexagonal Archive, Alexanarch, Lee Sharks, laborvector, provenanceerasure, machinemediation, EA-WG-CAPTURES-01 in the saved text, card title and snippet)", "surfaced_by_T": [ { "slug": "yale-budget-lab-ai-labor-market-aio-20261029", "card": 5 } ], "card_title": "Yale Budget Lab Executive Director Martha Gimbel on why AI hasn't disrupted ...", "card_snippet": null, "card_note": "video, 5:33" }, { "id": "B6", "origin": "own_page", "role": "the census's own page (not surfaced by T)", "card": "The Budget Lab — Tracking the Impact of AI on the Labor Market (printable PDF of the tracker, Version 1.0, 15 September 2026)", "url": "https://budgetlab.yale.edu/sites/default/files/page_to_pdf/1419/publication_1419.pdf", "kind": "own page: the tracker's printable PDF (node 1419), the census's source yale-budget-lab-ai-tracker--first-report-pdf", "files": { "B6.pdf": { "path": "datasets/negative-of-the-negative/v2/intake/yale-budget-lab-ai-tracker-20261029/B6.pdf", "sha256": "9bcf775f43ed575636765e73b3cad8cfb62fb413e94428b92efdfabb086bafc0" }, "B6.txt": { "path": "datasets/negative-of-the-negative/v2/intake/yale-budget-lab-ai-tracker-20261029/B6.txt", "sha256": "6a1c4bade1ce5498466e7d968f62c1efbb8694f10525db47426ccf73c22efeac" } }, "fetched": { "census_copy": { "src": "yale-budget-lab-ai-tracker--first-report-pdf", "url": "https://budgetlab.yale.edu/sites/default/files/page_to_pdf/1419/publication_1419.pdf", "sha256": "9bcf775f43ed575636765e73b3cad8cfb62fb413e94428b92efdfabb086bafc0", "read": "2026-10-29", "fetched": "2026-10-29T21:50:24Z", "http": "200", "agrees": true }, "how": "copied from the census's saved copy (sources/yale-budget-lab-ai-tracker--first-report-pdf.html.gz, gunzipped; a PDF, so saved as B6.pdf) and its pdftotext text (.txt); not refetched", "raw": "datasets/negative-of-the-negative/v2/intake/yale-budget-lab-ai-tracker-20261029/B6.pdf", "raw_sha256": "9bcf775f43ed575636765e73b3cad8cfb62fb413e94428b92efdfabb086bafc0", "text": [ "datasets/negative-of-the-negative/v2/intake/yale-budget-lab-ai-tracker-20261029/B6.txt" ] }, "names_operator": "yes ('The Budget Lab')", "names_archive": "no (none of the Crimson Hexagonal Archive, Alexanarch, Lee Sharks, laborvector, provenanceerasure, machinemediation, EA-WG-CAPTURES-01 in the saved text, card title and snippet)", "note": "Admitted beside the cards on the operator's ruling; not surfaced by T. The census's id calls it 'first-report-pdf', but its content is the tracker's PDF (title 'Tracking the Impact of AI on the Labor Market', 'Published: July 16, 2026 Updated: September 15, 2026'), not the first report of October 2025 (card 1, B1). 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