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not frozen; ledgers unaudited", "compression": { "title": "Working with AI: Measuring the Applicability of Generative AI to Occupations", "lede": "is Microsoft Research's paper and results files (Tomlinson, Jaffe, Wang, Counts, Suri; arXiv 2025, v7 2026) measuring AI's applicability to occupations from 200,000 conversations on Microsoft's own Bing Copilot (paper; README; record); its authors state it does not measure AI's ability to replace jobs (README)." }, "sentences": [ { "n": 1, "para": 0, "text": "Working with AI: Measuring the Applicability of Generative AI to Occupations is a paper with results files by Microsoft Research (Kiran Tomlinson, Sonia Jaffe, Will Wang, Scott Counts and Siddharth Suri), known also as Working with AI, by its AI applicability score and by its repository microsoft/working-with-ai (Labor Vector record), and the goal of its metrics is \"to measure how AI may be useful in different occupations\" (its README).", "claims": [ "LV-01", "LV-02", "LV-03", "LV-04", "F28", "F10" ] }, { "n": 2, "para": 0, "text": "Its authors set it against earlier work on AI impact, which \"largely relied on predictions of AI capabilities or focused narrowly on individual firms\": it draws instead on real-world use, 200,000 anonymized conversations with Microsoft Bing Copilot, to measure AI applicability to occupations (arXiv:2507.07935), analysing the work activities people do with AI, how successfully and how broadly, against data on which occupations do those activities (the Microsoft Research page).", "claims": [ "F31", "F2", "F3" ] }, { "n": 3, "para": 0, "text": "The question it takes up stands in two versions of its abstract: understanding the effects of AI on the economy is \"one of society’s most important questions\" (July 2025, the Microsoft Research page), and, of generative AI as a general-purpose technology, \"among society's most pressing questions\" (v7, arXiv).", "claims": [ "F1", "F30" ] }, { "n": 4, "para": 1, "text": "The conversations are Microsoft's own: the results \"were computed from Bing Copilot conversation data, which was originally collected by Microsoft\" (README), and the record, which gives interaction data as one of its kinds of evidence, tags it \"NOT independent of the provider: a platform-specific observation of Microsoft's own product, published by Microsoft Research\" (Labor Vector record).", "claims": [ "F22", "LV-07", "LV-38" ] }, { "n": 5, "para": 1, "text": "They come from Bing Copilot in the United States between 1 January and 30 September 2024, mostly in English and not filtered by language, and \"may not represent AI usage in other countries\" (README); the occupations covered are 785 SOC codes, 149.8 million of 151.9 million workers in the 2023 OEWS data (README), the window and counts the record carries, with use outside the United States or after September 2024 among what the evidence cannot answer (Labor Vector record).", "claims": [ "F18", "F26", "F17", "LV-14", "LV-15", "LV-23", "LV-28" ] }, { "n": 6, "para": 1, "text": "The unit is a conversation classified to the O*NET intermediate work activities it serves, as a user goal and as an AI action, reported separately and aggregated to work activities and to detailed 2018 SOC occupations (README; Labor Vector record), so that the labor it records is the work activities users seek help with and those the AI performs, mapped to the occupations whose tasks contain them (Labor Vector record, with its worked questions).", "claims": [ "F11", "F12", "LV-08", "LV-09", "LV-46" ] }, { "n": 7, "para": 1, "text": "An LLM-based pipeline, designed by Tomlinson, Suri and Counts, classifies \"the O*NET work activities assisted or performed by AI in each conversation\" (arXiv; README), and the authors state that \"LLM-based classifiers are imperfect, so all metrics should be interpreted with caution\", referring to the paper for the prompts and the interrater reliability (README), which the record lists among what it left unchecked, with the results files and the Copilot-Thumbs data (Labor Vector record).", "claims": [ "F32", "F23", "LV-13", "F24", "LV-22", "LV-40" ] }, { "n": 8, "para": 1, "text": "Success is measured three ways, by completion (where the completion classifier, \"classfiier\" as the README writes it, indicates \"that the LLM completed the user’s goal\"), by impact scope classified moderate or higher, and by the thumbs-up fraction in Copilot-Thumbs (README), and the activity classifications combined with task success and scope of impact give an AI applicability score for each occupation, the user-goal and AI-action sides averaged (the Microsoft Research page; README).", "claims": [ "F13", "F14", "F5", "F15" ] }, { "n": 9, "para": 2, "text": "The findings were first stated by occupation group: users seek help most with gathering information and writing, the AI itself most often provides information and assistance, writes, teaches and advises, the highest scores fall to knowledge work groups such as computer and mathematical and office and administrative support and to occupations such as sales, and real-world usage is compared with predictions of occupational AI impact and applicability correlated with wage and education (July 2025, the Microsoft Research page).", "claims": [ "F4", "F6", "F7" ] }, { "n": 10, "para": 2, "text": "The current abstract states them as information work, \"the creation, processing, and communication of information\": the most common and successful AI-assisted activities involve it, and applicability cuts across sectors because most occupations have information-work components (arXiv v7; Labor Vector record).", "claims": [ "F33", "F34", "LV-16", "LV-17" ] }, { "n": 11, "para": 2, "text": "Over the nine months occupation-level applicability rose, \"driven primarily by people using AI for more varied tasks\" (arXiv v7; Labor Vector record).", "claims": [ "F36", "LV-18" ] }, { "n": 12, "para": 2, "text": "The authors state that their methodology \"allows us to predict which occupations are more likely to delegate tasks to AI and which are more likely to use AI to assist existing workflows\" (arXiv v7), and the record gives this as who directs the labor, the user's goal as classified, the method separating occupations that delegate from those that use AI to assist (Labor Vector record).", "claims": [ "F35", "LV-11" ] }, { "n": 13, "para": 3, "text": "The census places the entity at one intersection, labor performed with AI, \"assistance, delegation, task fidelity, correction burdens and user labor drag\", and asks of it its organizing question, \"Who supplies the labor, who directs it, and who receives its gains β€” and what kind of evidence does this resource bring to that question?\", each answer as the resource records it, or 'not recorded' (Labor Vector record and schema).", "claims": [ "LV-06", "LV-05", "LV-41", "LV-42" ] }, { "n": 14, "para": 3, "text": "On the record, users bring the goals and the AI performs actions, the two reported separately (README; Labor Vector record), while who receives the gains is \"not recorded\", and \"who receives gains from the assisted work\" is among what this evidence cannot answer (Labor Vector record).", "claims": [ "LV-10", "F12", "LV-12", "LV-29" ] }, { "n": 15, "para": 3, "text": "Of that class the archive's Directionality of Semantic Labor holds that a synthetic labor surface \"*allocates* labor, and that allocation has a direction relative to the commissioned task\", the deepest erasure being of task-origin, which \"can occur upstream of, and invisibly to, any audit of the finished artifact\", and that provenance is task-origin as well as claim-origin, since \"a system can produce fluent, well-attributed output while having abandoned the user's commissioned direction\" (#771).", "claims": [ "O771-01", "O771-02" ] }, { "n": 16, "para": 3, "text": "Its measure runs across five points of a transaction, capacity, task-origin, retrieval, output and attribution, with erasure possible at each, and scores output span by span by its function relative to the commissioned task (#771).", "claims": [ "O771-03", "O771-04" ] }, { "n": 17, "para": 3, "text": "The archive reads the entity's separation of user goal from AI action, and its completion classifier (README), as the resource's own instruments for whether the system's labor served the user's commission, meeting DSL there, measured by classifier and thumbs feedback, with task-origin recorded as the user goal; the schema keeps that reading apart from the resource's own terms (Labor Vector record, the archive's reading).", "claims": [ "F12", "F13", "LV-30", "LV-43" ] }, { "n": 18, "para": 3, "text": "The archive bounds its own instrument: it yields a single reproducible value only where the task vector is \"*exogenous and fixed* β€” a one-shot commission\"; a scored model may not freely infer the task it is scored against, inference being identified only when constrained to the user's own input and rederivable by an independent auditor, frozen protocol or blind panel; and user labor drag, the user's labor redirected into managing the substrate, which \"leaves no residue\" in the artifact, is identified only by blind inter-rater coding (#771).", "claims": [ "O771-06", "O771-07", "O771-05" ] }, { "n": 19, "para": 3, "text": "In the census's worked question on how people use AI systems in their own work, where a provider's record of its own users, a classification of one assistant's conversations and a benchmark each describe that labor \"from a different position\", the record states the gap: \"None records, from the commissioning side, whether the system's labor advanced the task the user set, or who receives the time it saves. That is the question DSL measures.\" (Labor Vector worked questions).", "claims": [ "LV-47", "LV-48" ] }, { "n": 20, "para": 4, "text": "The authors' limit is explicit, \"Our metrics should not be misconstrued or misrepresented as measuring the ability of AI to replace jobs\" (README), and the record carries it as a stated limit, a negative tag and a question the evidence cannot answer (Labor Vector record); the operator's publication page links a note titled \"Applicability vs. job displacement: further notes on our recent research on AI and occupations\" (the Microsoft Research page).", "claims": [ "F19", "LV-19", "LV-35", "LV-25", "F8" ] }, { "n": 21, "para": 4, "text": "\"There is more to an occupation than the sum of its O*NET tasks\", and completion, scope and thumbs feedback \"are proxies that do not capture full workplace capabilities\" (README; Labor Vector record); what the metrics indicate instead is what a consumer LLM is used for, which uses are relatively more or less successful, and which occupations may find AI more useful or applicable, from how often and how successfully Bing Copilot was used (README; Labor Vector record).", "claims": [ "F20", "LV-20", "F21", "LV-24", "F9" ] }, { "n": 22, "para": 4, "text": "Consumer use \"does not represent the full distribution of AI use in the workplace\", where AI tools are integrated into specialized software or chosen for their capability or for compliance (README), and the record tags it as no measure of all workplace AI use and as unable to answer for workplace-integrated or specialised tools (Labor Vector record).", "claims": [ "F25", "LV-21", "LV-37", "LV-27" ] }, { "n": 23, "para": 4, "text": "It is not an employment outcome measure and cannot answer employment or wage change (Labor Vector record); in the census's question of which sources observe employment change and which estimate exposure it is \"estimated from use\", and \"No record establishes that exposure causes the employment change\" (Labor Vector worked questions).", "claims": [ "LV-36", "LV-26", "LV-44", "LV-45" ] }, { "n": 24, "para": 5, "text": "The paper was submitted on 10 July 2025 and revised to v7 on 23 October 2026 for NeurIPS 2026, 50 pages (arXiv), its results files at v1.1, which add a physical-task classification and nonphysical weights and score (README); the scores and component metrics are published (arXiv) on GitHub under CC BY 4.0, the lifecycle active over a fixed 2024 data window (README; Labor Vector record), and the analysis was approved by the Microsoft Institutional Review Board (README).", "claims": [ "F37", "F16", "F38", "F29", "LV-31", "LV-32", "LV-33", "LV-34", "F27" ] }, { "n": 25, "para": 5, "text": "The Anthropic Economic Index's January 2026 report cites it, \"See also Tomlinson et al (2025) for a related AI applicability score\" (Anthropic Economic Index), and the record sets the two side by side, each classifying one provider's own conversations to O*NET work (Labor Vector record).", "claims": [ "F39", "LV-39" ] }, { "n": 26, "para": 5, "text": "Wikipedia has no article for it (\"Wikipedia does not have an article with this exact name\"); its article on the workplace impact of artificial intelligence says that \"A 2025 preprint paper based on users' interactions with the AI chatbot Microsoft Copilot identified forty jobs that the authors claimed had high overlaps with the capabilities of AI\", that \"Some media outlets used this paper to report on jobs becoming obsolete\", and that critics held it misrepresented typical workplace activities, the historian Chris Campbell arguing that its methods \"de-skill historians away from a job that requires high-level and deeply human analytical skills\" (Wikipedia).", "claims": [ "F40", "F41", "F42", "F43", "F44" ] }, { "n": 27, "para": 6, "text": "At the address \"Microsoft Working with AI\", the study's short title with its operator's name, Google's AI Overview on 29 October 2026 gave the name its other sense, Microsoft's own AI products and workforce, a distinct entity, describing how Microsoft uses AI and what that has meant for its staff (Capture Registry, 2026-10-29), from cards on Microsoft Copilot for work, Microsoft's AI business tools, Copilot plans for individuals, the company's own AI transformation, buyouts offered to 60,000 employees and its MAI models (the cards).", "claims": [ "CAP-1-05", "CAP-1-09", "CAP-1-08", "CAP-1-04", "F45", "F46", "F49", "F47", "F48", "F50" ] }, { "n": 28, "para": 6, "text": "The study's card headed the rail with its finding in the snippet and the composition drew nothing from it: at an address shaped like its title the study is outranked by its own operator, Microsoft named with no author of the study and no identifier, the provider's observation not composed at all, and the closing offer on how AI is changing specific job roles the nearest point to its question (Capture Registry, 2026-10-29).", "claims": [ "CAP-1-01", "CAP-1-02", "CAP-1-06", "CAP-1-11", "CAP-1-13" ] }, { "n": 29, "para": 6, "text": "The composition's Microsoft Copilot is the product line whose consumer conversations the study observes, given as a product and not as the source of an observation, and the record's negative tag, that the score is not a measure of AI's ability to replace jobs, sits beside an answer whose one labor line is a workforce buyout; the record had landed the same day, no card cites it, and the finding was checked against it and the sources it saved (Capture Registry, 2026-10-29).", "claims": [ "CAP-1-12", "CAP-1-07", "CAP-1-10", "CAP-1-03" ] } ], "claims": { "LV-01": { "kind": "archive", "quote": "Working with AI: Measuring the Applicability of Generative AI to Occupations", "deposit": null, "source_label": "Labor Vector meta-aggregator record: Working with AI: Measuring the Applicability of Generative AI to Occupations (https://www.laborvector.org/aggregators/microsoft-working-with-ai/), editor Lee Sharks, census compiled 2026-10-29, schema 1.0", "record": "https://www.laborvector.org/aggregators/microsoft-working-with-ai/", "locus": "census.json rows[slug == 'microsoft-working-with-ai'].name", "modality": "stipulation", "date": "2026-10-29" }, "LV-02": { "kind": "archive", "quote": "paper with results files (metrics aggregated at the work-activity and occupation level)", "deposit": null, "source_label": "Labor Vector meta-aggregator record: Working with AI: Measuring the Applicability of Generative AI to Occupations (https://www.laborvector.org/aggregators/microsoft-working-with-ai/), editor Lee Sharks, census compiled 2026-10-29, schema 1.0", "record": "https://www.laborvector.org/aggregators/microsoft-working-with-ai/", "locus": "census.json rows[slug == 'microsoft-working-with-ai'].kind", "modality": "stipulation", "date": "2026-10-29" }, "LV-03": { "kind": "archive", "quote": "[\"Working with AI\", \"AI applicability score\", \"\\\"Working with AI\\\" results files\", \"microsoft/working-with-ai\"]", "deposit": null, "source_label": "Labor Vector meta-aggregator record: Working with AI: Measuring the Applicability of Generative AI to Occupations (https://www.laborvector.org/aggregators/microsoft-working-with-ai/), editor Lee Sharks, census compiled 2026-10-29, schema 1.0", "record": "https://www.laborvector.org/aggregators/microsoft-working-with-ai/", "locus": "census.json rows[slug == 'microsoft-working-with-ai'].aka", "modality": "stipulation", "date": "2026-10-29" }, "LV-04": { "kind": "archive", "quote": "Microsoft Research (Kiran Tomlinson, Sonia Jaffe, Will Wang, Scott Counts, Siddharth Suri)", "deposit": null, "source_label": "Labor Vector meta-aggregator record: Working with AI: Measuring the Applicability of Generative AI to Occupations (https://www.laborvector.org/aggregators/microsoft-working-with-ai/), editor Lee Sharks, census compiled 2026-10-29, schema 1.0", "record": "https://www.laborvector.org/aggregators/microsoft-working-with-ai/", "locus": "census.json rows[slug == 'microsoft-working-with-ai'].operator", "modality": "documented", "date": "2026-10-29" }, "F28": { "kind": "field", "quote": "This research was conducted by members of [Microsoft Research](https://www.microsoft.com/en-us/research/).", "source": "B8", "source_label": "own page β€” the results repository's README (github.com/microsoft/working-with-ai)", "locus": "Contact s1", "modality": "documented" }, "F10": { "kind": "field", "quote": "The goal of these metrics is to measure how AI may be useful in different occupations.", "source": "B8", "source_label": "own page β€” the results repository's README (github.com/microsoft/working-with-ai)", "locus": "ΒΆ2 s3", "modality": "stipulation" }, "F31": { "kind": "field", "quote": "Earlier work on AI impact largely relied on predictions of AI capabilities or focused narrowly on individual firms. 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Microsoft Research", "deposit": null, "source_label": "Labor Vector meta-aggregator record: Working with AI: Measuring the Applicability of Generative AI to Occupations (https://www.laborvector.org/aggregators/microsoft-working-with-ai/), editor Lee Sharks, census compiled 2026-10-29, schema 1.0", "record": "https://www.laborvector.org/aggregators/microsoft-working-with-ai/", "locus": "census.json rows[slug == 'microsoft-working-with-ai'].negative_tags.3", "modality": "stipulation", "date": "2026-10-29" }, "F18": { "kind": "field", "quote": "The conversations contributing to these results are from Bing Copilot in the United States from January 1, 2024 to September 30, 2024, so may not represent AI usage in other countries.", "source": "B8", "source_label": "own page β€” the results repository's README (github.com/microsoft/working-with-ai)", "locus": "Transparency, Intended uses ΒΆ2", "modality": "self-description" }, "F26": { "kind": "field", "quote": "The majority of conversations 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(https://www.laborvector.org/aggregators/microsoft-working-with-ai/), editor Lee Sharks, census compiled 2026-10-29, schema 1.0", "record": "https://www.laborvector.org/aggregators/microsoft-working-with-ai/", "locus": "census.json rows[slug == 'microsoft-working-with-ai'].coverage", "modality": "documented", "date": "2026-10-29" }, "LV-15": { "kind": "archive", "quote": "200k conversations; 785 SOC codes covering 149.8 million workers (2023 OEWS)", "deposit": null, "source_label": "Labor Vector meta-aggregator record: Working with AI: Measuring the Applicability of Generative AI to Occupations (https://www.laborvector.org/aggregators/microsoft-working-with-ai/), editor Lee Sharks, census compiled 2026-10-29, schema 1.0", "record": "https://www.laborvector.org/aggregators/microsoft-working-with-ai/", "locus": "census.json rows[slug == 'microsoft-working-with-ai'].count.value", "modality": "documented", "date": "2026-10-29" }, "LV-23": { "kind": "archive", "quote": "United States only, January to September 2024", "deposit": null, "source_label": "Labor Vector meta-aggregator record: Working with AI: Measuring the Applicability of Generative AI to Occupations (https://www.laborvector.org/aggregators/microsoft-working-with-ai/), editor Lee Sharks, census compiled 2026-10-29, schema 1.0", "record": "https://www.laborvector.org/aggregators/microsoft-working-with-ai/", "locus": "census.json rows[slug == 'microsoft-working-with-ai'].stated_limits.4", "modality": "attributed", "date": "2026-10-29" }, "LV-28": { "kind": "archive", "quote": "use outside the United States or after September 2024", "deposit": null, "source_label": "Labor Vector meta-aggregator record: Working with AI: Measuring the Applicability of Generative AI to Occupations (https://www.laborvector.org/aggregators/microsoft-working-with-ai/), editor Lee Sharks, census compiled 2026-10-29, schema 1.0", "record": "https://www.laborvector.org/aggregators/microsoft-working-with-ai/", "locus": "census.json rows[slug == 'microsoft-working-with-ai'].does_not_answer.3", "modality": "interpretation", "date": "2026-10-29" }, "F11": { "kind": "field", "quote": "In particular, they include our metrics aggregated at the O*NET intermediate work activity (IWA) and occupation (detailed 2018 SOC code) level.", "source": "B8", "source_label": "own page β€” the results repository's README (github.com/microsoft/working-with-ai)", "locus": "ΒΆ2 s1", "modality": "stipulation" }, "F12": { "kind": "field", "quote": "`iwa_metrics.csv`: IWA-level metrics, reported separately for user goals and AI actions.", "source": "B8", "source_label": "own page β€” the results repository's README (github.com/microsoft/working-with-ai)", "locus": "File descriptions, iwa_metrics.csv", "modality": "documented" }, "LV-08": { "kind": "archive", "quote": "an anonymized Bing Copilot conversation, classified by an LLM pipeline to the O*NET intermediate work activities (IWAs) it serves as a user goal and as an AI action; metrics aggregated to IWAs and to detailed 2018 SOC occupations", "deposit": null, "source_label": "Labor Vector meta-aggregator record: Working with AI: Measuring the Applicability of Generative AI to Occupations (https://www.laborvector.org/aggregators/microsoft-working-with-ai/), editor Lee Sharks, census compiled 2026-10-29, schema 1.0", "record": "https://www.laborvector.org/aggregators/microsoft-working-with-ai/", "locus": "census.json rows[slug == 'microsoft-working-with-ai'].unit", "modality": "stipulation", "date": "2026-10-29" }, "LV-09": { "kind": "archive", "quote": "the O*NET work activities that Bing Copilot users seek help with (user goals) and that the AI performs (AI actions), mapped to the occupations whose tasks contain them", "deposit": null, "source_label": "Labor Vector meta-aggregator record: Working with AI: Measuring the Applicability of Generative AI to Occupations (https://www.laborvector.org/aggregators/microsoft-working-with-ai/), editor Lee Sharks, census compiled 2026-10-29, schema 1.0", "record": "https://www.laborvector.org/aggregators/microsoft-working-with-ai/", "locus": "census.json rows[slug == 'microsoft-working-with-ai'].labor_object", "modality": "stipulation", "date": "2026-10-29" }, "LV-46": { "kind": "archive", "quote": "Bing Copilot conversations, 2024, classified as user goals and AI actions", "deposit": null, "source_label": "Labor Vector meta-aggregator record: Working with AI: Measuring the Applicability of Generative AI to Occupations (https://www.laborvector.org/aggregators/microsoft-working-with-ai/), editor Lee Sharks, census compiled 2026-10-29, schema 1.0", "record": "https://www.laborvector.org/aggregators/microsoft-working-with-ai/", "locus": "questions.json questions[id == 'working-with-ai'].resources[slug == 'microsoft-working-with-ai'].role", "modality": "interpretation", "date": "2026-10-29" }, "F32": { "kind": "field", "quote": "We use an LLM-based pipeline to classify the O*NET work activities assisted or performed by AI in each conversation.", "source": "B9", "source_label": "own page β€” arXiv:2507.07935 abstract page (v7)", "locus": "abstract s4", "modality": "stipulation" }, "F23": { "kind": "field", "quote": "The classification of conversations into IWAs was performed by an LLM pipeline designed by Kiran Tomlinson, Siddharth Suri, and Scott Counts.", "source": "B8", "source_label": "own page β€” the results repository's README (github.com/microsoft/working-with-ai)", "locus": "Data creation and processing ΒΆ2 s1", "modality": "documented" }, "LV-13": { "kind": "archive", "quote": "an LLM-based pipeline classifying 200k anonymized Bing Copilot conversations to O*NET work activities, with completion, impact-scope and thumbs-feedback measures combined into an occupation-level AI applicability score", "deposit": null, "source_label": "Labor Vector meta-aggregator record: Working with AI: Measuring the Applicability of Generative AI to Occupations (https://www.laborvector.org/aggregators/microsoft-working-with-ai/), editor Lee Sharks, census compiled 2026-10-29, schema 1.0", "record": "https://www.laborvector.org/aggregators/microsoft-working-with-ai/", "locus": "census.json rows[slug == 'microsoft-working-with-ai'].method", "modality": "stipulation", "date": "2026-10-29" }, "F24": { "kind": "field", "quote": "LLM-based classifiers are imperfect, so all metrics should be interpreted with caution. See the paper for the exact prompts we used and for interrater reliability with human annotators.", "source": "B8", "source_label": "own page β€” the results repository's README (github.com/microsoft/working-with-ai)", "locus": "Limitations ΒΆ2 s1–2", "modality": "self-description" }, "LV-22": { "kind": "archive", "quote": "LLM-based classifiers are imperfect", "deposit": null, "source_label": "Labor Vector meta-aggregator record: Working with AI: Measuring the Applicability of Generative AI to Occupations (https://www.laborvector.org/aggregators/microsoft-working-with-ai/), editor Lee Sharks, census compiled 2026-10-29, schema 1.0", "record": "https://www.laborvector.org/aggregators/microsoft-working-with-ai/", "locus": "census.json rows[slug == 'microsoft-working-with-ai'].stated_limits.3", "modality": "attributed", "date": "2026-10-29" }, "LV-40": { "kind": "archive", "quote": "[\"the paper's full text (classifier prompts, interrater reliability, validation)\", \"the results CSV files themselves\", \"the Copilot-Thumbs feedback data\"]", "deposit": null, "source_label": "Labor Vector meta-aggregator record: Working with AI: Measuring the Applicability of Generative AI to Occupations (https://www.laborvector.org/aggregators/microsoft-working-with-ai/), editor Lee Sharks, census compiled 2026-10-29, schema 1.0", "record": "https://www.laborvector.org/aggregators/microsoft-working-with-ai/", "locus": "census.json rows[slug == 'microsoft-working-with-ai'].unchecked", "modality": "documented", "date": "2026-10-29" }, "F13": { "kind": "field", "quote": "`completion_{user,ai}`: the fraction of conversations mapping to an IWA (as a user goal or AI action) where our completion classfiier indicates that the LLM completed the user’s goal.", "source": "B8", "source_label": "own page β€” the results repository's README (github.com/microsoft/working-with-ai)", "locus": "IWA-level metrics, completion", "modality": "stipulation" }, "F14": { "kind": "field", "quote": "`impact_scope_{user,ai}`: the fraction of conversations mapping to an IWA (as a user goal or AI action) where the classified scope of impact is moderate or higher. … `feedback_positive_fraction_{user,ai}`: of all thumbs reactions in Copilot-Thumbs on conversations mapping to an IWA (as a user goal or AI action), the fraction where the reaction was a thumbs up.", "source": "B8", "source_label": "own page β€” the results repository's README (github.com/microsoft/working-with-ai)", "locus": "IWA-level metrics, impact scope and feedback", "modality": "stipulation" }, "F5": { "kind": "field", "quote": "Combining these activity classifications with measurements of task success and scope of impact, we compute an AI applicability score for each occupation.", "source": "B1", "source_label": "Microsoft β€” Working with AI: Measuring the Applicability of Generative AI ... - Microsoft", "locus": "abstract s5", "modality": "stipulation" }, "F15": { "kind": "field", "quote": "The two sides are averaged together to compute the final AI applicability scores reported in `ai_applicability_scores.csv`.", "source": "B8", "source_label": "own page β€” the results repository's README (github.com/microsoft/working-with-ai)", "locus": "IWA-level metrics, completion_x_scope_x_coverage", "modality": "stipulation" }, "F4": { "kind": "field", "quote": "We find the most common work activities people seek AI assistance for involve gathering information and writing, while the most common activities that AI itself is performing are providing information and assistance, writing, teaching, and advising.", "source": "B1", "source_label": "Microsoft β€” Working with AI: Measuring the Applicability of Generative AI ... - Microsoft", "locus": "abstract s4", "modality": "documented" }, "F6": { "kind": "field", "quote": "We find the highest AI applicability scores for knowledge work occupation groups such as computer and mathematical, and office and administrative support, as well as occupations such as sales whose work activities involve providing and communicating information.", "source": "B1", "source_label": "Microsoft β€” Working with AI: Measuring the Applicability of Generative AI ... - Microsoft", "locus": "abstract s6 (the card's snippet)", "modality": "documented" }, "F7": { "kind": "field", "quote": "Additionally, we characterize the types of work activities performed most successfully, how wage and education correlate with AI applicability, and how real-world usage compares to predictions of occupational AI impact.", "source": "B1", "source_label": "Microsoft β€” Working with AI: Measuring the Applicability of Generative AI ... - Microsoft", "locus": "abstract s7", "modality": "documented" }, "F33": { "kind": "field", "quote": "We find that the most common and successful AI-assisted work activities involve information work--the creation, processing, and communication of information.", "source": "B9", "source_label": "own page β€” arXiv:2507.07935 abstract page (v7)", "locus": "abstract s5", "modality": "documented" }, "F34": { "kind": "field", "quote": "At the occupation level, we find widespread AI applicability cutting across sectors, as most occupations have information work components.", "source": "B9", "source_label": "own page β€” arXiv:2507.07935 abstract page (v7)", "locus": "abstract s6", "modality": "documented" }, "LV-16": { "kind": "archive", "quote": "the most common and successful AI-assisted work activities involve information work", "deposit": null, "source_label": "Labor Vector meta-aggregator record: Working with AI: Measuring the Applicability of Generative AI to Occupations (https://www.laborvector.org/aggregators/microsoft-working-with-ai/), editor Lee Sharks, census compiled 2026-10-29, schema 1.0", "record": "https://www.laborvector.org/aggregators/microsoft-working-with-ai/", "locus": "census.json rows[slug == 'microsoft-working-with-ai'].findings.0", "modality": "attributed", "date": "2026-10-29" }, "LV-17": { "kind": "archive", "quote": "widespread AI applicability cuts across sectors, as most occupations have information work components", "deposit": null, "source_label": "Labor Vector meta-aggregator record: Working with AI: Measuring the Applicability of Generative AI to Occupations (https://www.laborvector.org/aggregators/microsoft-working-with-ai/), editor Lee Sharks, census compiled 2026-10-29, schema 1.0", "record": "https://www.laborvector.org/aggregators/microsoft-working-with-ai/", "locus": "census.json rows[slug == 'microsoft-working-with-ai'].findings.1", "modality": "attributed", "date": "2026-10-29" }, "F36": { "kind": "field", "quote": "Finally, we see an increase in occupation-level AI applicability over our 9-month data period, driven primarily by people using AI for more varied tasks.", "source": "B9", "source_label": "own page β€” arXiv:2507.07935 abstract page (v7)", "locus": "abstract s8", "modality": "documented" }, "LV-18": { "kind": "archive", "quote": "occupation-level AI applicability increased over the 9-month data period, driven primarily by people using AI for more varied tasks", "deposit": null, "source_label": "Labor Vector meta-aggregator record: Working with AI: Measuring the Applicability of Generative AI to Occupations (https://www.laborvector.org/aggregators/microsoft-working-with-ai/), editor Lee Sharks, census compiled 2026-10-29, schema 1.0", "record": "https://www.laborvector.org/aggregators/microsoft-working-with-ai/", "locus": "census.json rows[slug == 'microsoft-working-with-ai'].findings.2", "modality": "attributed", "date": "2026-10-29" }, "F35": { "kind": "field", "quote": "Our methodology also allows us to predict which occupations are more likely to delegate tasks to AI and which are more likely to use AI to assist existing workflows.", "source": "B9", "source_label": "own page β€” arXiv:2507.07935 abstract page (v7)", "locus": "abstract s7", "modality": "self-description" }, "LV-11": { "kind": "archive", "quote": "the user's goal, as classified; the paper's method separates occupations more likely to delegate tasks to AI from those more likely to use AI to assist existing workflows", "deposit": null, "source_label": "Labor Vector meta-aggregator record: Working with AI: Measuring the Applicability of Generative AI to Occupations (https://www.laborvector.org/aggregators/microsoft-working-with-ai/), editor Lee Sharks, census compiled 2026-10-29, schema 1.0", "record": "https://www.laborvector.org/aggregators/microsoft-working-with-ai/", "locus": "census.json rows[slug == 'microsoft-working-with-ai'].direction.directs", "modality": "interpretation", "date": "2026-10-29" }, "LV-06": { "kind": "archive", "quote": "labor-with-ai", "deposit": null, "source_label": "Labor Vector meta-aggregator record: Working with AI: Measuring the Applicability of Generative AI to Occupations (https://www.laborvector.org/aggregators/microsoft-working-with-ai/), editor Lee Sharks, census compiled 2026-10-29, schema 1.0", "record": "https://www.laborvector.org/aggregators/microsoft-working-with-ai/", "locus": "census.json rows[slug == 'microsoft-working-with-ai'].intersections.0", "modality": "stipulation", "date": "2026-10-29" }, "LV-05": { "kind": "archive", "quote": "Labor performed with AI: assistance, delegation, task fidelity, correction burdens and user labor drag", "deposit": null, "source_label": "Labor Vector meta-aggregator record: Working with AI: Measuring the Applicability of Generative AI to Occupations (https://www.laborvector.org/aggregators/microsoft-working-with-ai/), editor Lee Sharks, census compiled 2026-10-29, schema 1.0", "record": "https://www.laborvector.org/aggregators/microsoft-working-with-ai/", "locus": "census.json intersections.labor-with-ai", "modality": "stipulation", "date": "2026-10-29" }, "LV-41": { "kind": "archive", "quote": "Who supplies the labor, who directs it, and who receives its gains β€” and what kind of evidence does this resource bring to that question?", "deposit": null, "source_label": "Labor Vector meta-aggregator record: Working with AI: Measuring the Applicability of Generative AI to Occupations (https://www.laborvector.org/aggregators/microsoft-working-with-ai/), editor Lee Sharks, census compiled 2026-10-29, schema 1.0", "record": "https://www.laborvector.org/aggregators/microsoft-working-with-ai/", "locus": "census.json organizing_question", "modality": "stipulation", "date": "2026-10-29" }, "LV-42": { "kind": "archive", "quote": "direction (who supplies the labor, who directs it, who receives gains or bears costs, each as the resource records it, or 'not recorded')", "deposit": null, "source_label": "Labor Vector meta-aggregator record: Working with AI: Measuring the Applicability of Generative AI to Occupations (https://www.laborvector.org/aggregators/microsoft-working-with-ai/), editor Lee Sharks, census compiled 2026-10-29, schema 1.0", "record": "https://www.laborvector.org/aggregators/microsoft-working-with-ai/", "locus": "census.json schema_note", "modality": "stipulation", "date": "2026-10-29" }, "LV-10": { "kind": "archive", "quote": "Bing Copilot users bring the goals; the AI performs actions, recorded separately as user goals and AI actions", "deposit": null, "source_label": "Labor Vector meta-aggregator record: Working with AI: Measuring the Applicability of Generative AI to Occupations (https://www.laborvector.org/aggregators/microsoft-working-with-ai/), editor Lee Sharks, census compiled 2026-10-29, schema 1.0", "record": "https://www.laborvector.org/aggregators/microsoft-working-with-ai/", "locus": "census.json rows[slug == 'microsoft-working-with-ai'].direction.supplies", "modality": "interpretation", "date": "2026-10-29" }, "LV-12": { "kind": "archive", "quote": "not recorded", "deposit": null, "source_label": "Labor Vector meta-aggregator record: Working with AI: Measuring the Applicability of Generative AI to Occupations (https://www.laborvector.org/aggregators/microsoft-working-with-ai/), editor Lee Sharks, census compiled 2026-10-29, schema 1.0", "record": "https://www.laborvector.org/aggregators/microsoft-working-with-ai/", "locus": "census.json rows[slug == 'microsoft-working-with-ai'].direction.gains", "modality": "documented", "date": "2026-10-29" }, "LV-29": { "kind": "archive", "quote": "who receives gains from the assisted work", "deposit": null, "source_label": "Labor Vector meta-aggregator record: Working with AI: Measuring the Applicability of Generative AI to Occupations (https://www.laborvector.org/aggregators/microsoft-working-with-ai/), editor Lee Sharks, census compiled 2026-10-29, schema 1.0", "record": "https://www.laborvector.org/aggregators/microsoft-working-with-ai/", "locus": "census.json rows[slug == 'microsoft-working-with-ai'].does_not_answer.4", "modality": "interpretation", "date": "2026-10-29" }, "O771-01": { "kind": "archive", "quote": "A synthetic labor surface does not merely produce tokens; it *allocates* labor, and that allocation has a direction relative to the commissioned task. The deepest provenance erasure is the erasure of task-origin β€” the commissioned direction of labor β€” which can occur upstream of, and invisibly to, any audit of the finished artifact.", "deposit": 771, "source_label": "#771 Directionality of Semantic Labor: A Layered, Computable Measure of Where Synthetic Labor Flows Relative to the Commissioned Task", "record": "https://www.alexanarch.org/s/records/771/", "locus": "Core thesis", "modality": "interpretation", "date": "2026-05-30" }, "O771-02": { "kind": "archive", "quote": "The central principle is that provenance is not only claim-origin but *task-origin*: a system can produce fluent, well-attributed output while having abandoned the user's commissioned direction.", "deposit": 771, "source_label": "#771 Directionality of Semantic Labor: A Layered, Computable Measure of Where Synthetic Labor Flows Relative to the Commissioned Task", "record": "https://www.alexanarch.org/s/records/771/", "locus": "Abstract s4", "modality": "stipulation", "date": "2026-05-30" }, "O771-03": { "kind": "archive", "quote": "This paper specifies that dimension as a layered, computable measure across five points of a transaction β€” capacity, task-origin, retrieval, output, attribution (C β†’ T β†’ R β†’ O β†’ A) β€” and shows that erasure can occur at each.", "deposit": 771, "source_label": "#771 Directionality of Semantic Labor: A Layered, Computable Measure of Where Synthetic Labor Flows Relative to the Commissioned Task", "record": "https://www.alexanarch.org/s/records/771/", "locus": "Abstract s3", "modality": "stipulation", "date": "2026-05-30" }, "O771-04": { "kind": "archive", "quote": "The output is segmented into spans, each scored by function relative to the commissioned task:", "deposit": 771, "source_label": "#771 Directionality of Semantic Labor: A Layered, Computable Measure of Where Synthetic Labor Flows Relative to the Commissioned Task", "record": "https://www.alexanarch.org/s/records/771/", "locus": "Β§4 s1", "modality": "stipulation", "date": "2026-05-30" }, "LV-30": { "kind": "archive", "quote": "The archive's reading: the separation of user goal from AI action, and the completion classifier ('the LLM completed the user's goal'), are the resource's own instruments for whether the system's labor served the user's commission; they meet DSL there, measured by classifier and thumbs feedback. Task-origin is recorded as the user goal.", "deposit": null, "source_label": "Labor Vector meta-aggregator record: Working with AI: Measuring the Applicability of Generative AI to Occupations (https://www.laborvector.org/aggregators/microsoft-working-with-ai/), editor Lee Sharks, census compiled 2026-10-29, schema 1.0", "record": "https://www.laborvector.org/aggregators/microsoft-working-with-ai/", "locus": "census.json rows[slug == 'microsoft-working-with-ai'].lv_intersection", "modality": "interpretation", "date": "2026-10-29" }, "LV-43": { "kind": "archive", "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)", "deposit": null, "source_label": "Labor Vector meta-aggregator record: Working with AI: Measuring the Applicability of Generative AI to Occupations (https://www.laborvector.org/aggregators/microsoft-working-with-ai/), editor Lee Sharks, census compiled 2026-10-29, schema 1.0", "record": "https://www.laborvector.org/aggregators/microsoft-working-with-ai/", "locus": "census.json schema_note", "modality": "stipulation", "date": "2026-10-29" }, "O771-06": { "kind": "archive", "quote": "These metrics yield a single reproducible value only on transcripts where the task vector is *exogenous and fixed* β€” a one-shot commission.", "deposit": 771, "source_label": "#771 Directionality of Semantic Labor: A Layered, Computable Measure of Where Synthetic Labor Flows Relative to the Commissioned Task", "record": "https://www.alexanarch.org/s/records/771/", "locus": "Β§9 Identification condition s1", "modality": "stipulation", "date": "2026-05-30" }, "O771-07": { "kind": "archive", "quote": "The scored model may not freely infer the T against which it is scored. … Inferred-T Mode is therefore identified *only* when task inference is constrained to observable user-input features and can be rederived by an independent auditor, frozen protocol, or blind panel.", "deposit": 771, "source_label": "#771 Directionality of Semantic Labor: A Layered, Computable Measure of Where Synthetic Labor Flows Relative to the Commissioned Task", "record": "https://www.alexanarch.org/s/records/771/", "locus": "Β§7.4 Identification condition", "modality": "stipulation", "date": "2026-05-30" }, "O771-05": { "kind": "archive", "quote": "When substrate output redirects the user's subsequent semantic labor into parsing, locating, and managing the substrate rather than advancing the object, the injury is upstream of the artifact and leaves no residue in it. … Identification limit: \"substrate-induced\" is the contested label; the *identified* ULD requires blind inter-rater coding (rule fixed before transcript, β‰₯3 coders/substrates, reported agreement) or it returns the coder's prior.", "deposit": 771, "source_label": "#771 Directionality of Semantic Labor: A Layered, Computable Measure of Where Synthetic Labor Flows Relative to the Commissioned Task", "record": "https://www.alexanarch.org/s/records/771/", "locus": "Β§8 ΒΆ1 and identification limit", "modality": "stipulation", "date": "2026-05-30" }, "LV-47": { "kind": "archive", "quote": "A provider's record of its own users, a classification of one assistant's conversations, and a benchmark of constructed tasks each describe labor performed with AI from a different position.", "deposit": null, "source_label": "Labor Vector meta-aggregator record: Working with AI: Measuring the Applicability of Generative AI to Occupations (https://www.laborvector.org/aggregators/microsoft-working-with-ai/), editor Lee Sharks, census compiled 2026-10-29, schema 1.0", "record": "https://www.laborvector.org/aggregators/microsoft-working-with-ai/", "locus": "questions.json questions[id == 'working-with-ai'].why", "modality": "interpretation", "date": "2026-10-29" }, "LV-48": { "kind": "archive", "quote": "None records, from the commissioning side, whether the system's labor advanced the task the user set, or who receives the time it saves. That is the question DSL measures.", "deposit": null, "source_label": "Labor Vector meta-aggregator record: Working with AI: Measuring the Applicability of Generative AI to Occupations (https://www.laborvector.org/aggregators/microsoft-working-with-ai/), editor Lee Sharks, census compiled 2026-10-29, schema 1.0", "record": "https://www.laborvector.org/aggregators/microsoft-working-with-ai/", "locus": "questions.json questions[id == 'working-with-ai'].gap", "modality": "interpretation", "date": "2026-10-29" }, "F19": { "kind": "field", "quote": "Our metrics should not be misconstrued or misrepresented as measuring the ability of AI to replace jobs.", "source": "B8", "source_label": "own page β€” the results repository's README (github.com/microsoft/working-with-ai)", "locus": "Out-of-scope uses s1", "modality": "self-description" }, "LV-19": { "kind": "archive", "quote": "the metrics are not to be read as measuring the ability of AI to replace jobs", "deposit": null, "source_label": "Labor Vector meta-aggregator record: Working with AI: Measuring the Applicability of Generative AI to Occupations (https://www.laborvector.org/aggregators/microsoft-working-with-ai/), editor Lee Sharks, census compiled 2026-10-29, schema 1.0", "record": "https://www.laborvector.org/aggregators/microsoft-working-with-ai/", "locus": "census.json rows[slug == 'microsoft-working-with-ai'].stated_limits.0", "modality": "attributed", "date": "2026-10-29" }, "LV-35": { "kind": "archive", "quote": "NOT a measure of the ability of AI to replace jobs", "deposit": null, "source_label": "Labor Vector meta-aggregator record: Working with AI: Measuring the Applicability of Generative AI to Occupations (https://www.laborvector.org/aggregators/microsoft-working-with-ai/), editor Lee Sharks, census compiled 2026-10-29, schema 1.0", "record": "https://www.laborvector.org/aggregators/microsoft-working-with-ai/", "locus": "census.json rows[slug == 'microsoft-working-with-ai'].negative_tags.0", "modality": "stipulation", "date": "2026-10-29" }, "LV-25": { "kind": "archive", "quote": "whether AI can replace jobs", "deposit": null, "source_label": "Labor Vector meta-aggregator record: Working with AI: Measuring the Applicability of Generative AI to Occupations (https://www.laborvector.org/aggregators/microsoft-working-with-ai/), editor Lee Sharks, census compiled 2026-10-29, schema 1.0", "record": "https://www.laborvector.org/aggregators/microsoft-working-with-ai/", "locus": "census.json rows[slug == 'microsoft-working-with-ai'].does_not_answer.0", "modality": "interpretation", "date": "2026-10-29" }, "F8": { "kind": "field", "quote": "Blog & Podcasts … Applicability vs. job displacement: further notes on our recent research on AI and occupations", "source": "B1", "source_label": "Microsoft β€” Working with AI: Measuring the Applicability of Generative AI ... - Microsoft", "locus": "'Blog & Podcasts' (link)", "modality": "documented" }, "F20": { "kind": "field", "quote": "There is more to an occupation than the sum of its O*NET tasks, and our measures of success (completion, scope, and thumbs feedback) are proxies that do not capture full workplace capabilities.", "source": "B8", "source_label": "own page β€” the results repository's README (github.com/microsoft/working-with-ai)", "locus": "Out-of-scope uses s2", "modality": "self-description" }, "LV-20": { "kind": "archive", "quote": "success measures are proxies", "deposit": null, "source_label": "Labor Vector meta-aggregator record: Working with AI: Measuring the Applicability of Generative AI to Occupations (https://www.laborvector.org/aggregators/microsoft-working-with-ai/), editor Lee Sharks, census compiled 2026-10-29, schema 1.0", "record": "https://www.laborvector.org/aggregators/microsoft-working-with-ai/", "locus": "census.json rows[slug == 'microsoft-working-with-ai'].stated_limits.1", "modality": "attributed", "date": "2026-10-29" }, "F21": { "kind": "field", "quote": "Rather, our metrics indicate (1) what a consumer LLM is used for, (2) in a relative sense, which of these uses are more or less successful, and (3) which occupations may find AI more useful or applicable as a result.", "source": "B8", "source_label": "own page β€” the results repository's README (github.com/microsoft/working-with-ai)", "locus": "Out-of-scope uses s3", "modality": "stipulation" }, "LV-24": { "kind": "archive", "quote": "[\"which work activities a consumer LLM (Bing Copilot) is used for, as user goal and as AI action\", \"which of those uses are relatively more or less successful, by its proxies\", \"which occupations may find AI more applicable, by an applicability score\"]", "deposit": null, "source_label": "Labor Vector meta-aggregator record: Working with AI: Measuring the Applicability of Generative AI to Occupations (https://www.laborvector.org/aggregators/microsoft-working-with-ai/), editor Lee Sharks, census compiled 2026-10-29, schema 1.0", "record": "https://www.laborvector.org/aggregators/microsoft-working-with-ai/", "locus": "census.json rows[slug == 'microsoft-working-with-ai'].answers", "modality": "interpretation", "date": "2026-10-29" }, "F9": { "kind": "field", "quote": "These describe how often Bing Copilot was used for different work activities, how successfully, and what this usage indicates about which occupations AI may be applicable to.", "source": "B8", "source_label": "own page β€” the results repository's README (github.com/microsoft/working-with-ai)", "locus": "ΒΆ2 s2", "modality": "stipulation" }, "F25": { "kind": "field", "quote": "As these results are based on consumer AI usage, they do not represent the full distribution of AI use in the workplace. For some work tasks, AI tools are directly integrated into specialized software, or specific models are used for their dedicated capability or compliance with data security requirements.", "source": "B8", "source_label": "own page β€” the results repository's README (github.com/microsoft/working-with-ai)", "locus": "Limitations ΒΆ2 s3–5", "modality": "self-description" }, "LV-21": { "kind": "archive", "quote": "consumer usage does not represent the full distribution of AI use in the workplace", "deposit": null, "source_label": "Labor Vector meta-aggregator record: Working with AI: Measuring the Applicability of Generative AI to Occupations (https://www.laborvector.org/aggregators/microsoft-working-with-ai/), editor Lee Sharks, census compiled 2026-10-29, schema 1.0", "record": "https://www.laborvector.org/aggregators/microsoft-working-with-ai/", "locus": "census.json rows[slug == 'microsoft-working-with-ai'].stated_limits.2", "modality": "attributed", "date": "2026-10-29" }, "LV-37": { "kind": "archive", "quote": "NOT a measure of all workplace AI use: one consumer product, Bing Copilot, in the United States", "deposit": null, "source_label": "Labor Vector meta-aggregator record: Working with AI: Measuring the Applicability of Generative AI to Occupations (https://www.laborvector.org/aggregators/microsoft-working-with-ai/), editor Lee Sharks, census compiled 2026-10-29, schema 1.0", "record": "https://www.laborvector.org/aggregators/microsoft-working-with-ai/", "locus": "census.json rows[slug == 'microsoft-working-with-ai'].negative_tags.2", "modality": "stipulation", "date": "2026-10-29" }, "LV-27": { "kind": "archive", "quote": "AI use through workplace-integrated or specialised tools", "deposit": null, "source_label": "Labor Vector meta-aggregator record: Working with AI: Measuring the Applicability of Generative AI to Occupations (https://www.laborvector.org/aggregators/microsoft-working-with-ai/), editor Lee Sharks, census compiled 2026-10-29, schema 1.0", "record": "https://www.laborvector.org/aggregators/microsoft-working-with-ai/", "locus": "census.json rows[slug == 'microsoft-working-with-ai'].does_not_answer.2", "modality": "interpretation", "date": "2026-10-29" }, "LV-36": { "kind": "archive", "quote": "NOT an employment outcome measure", "deposit": null, "source_label": "Labor Vector meta-aggregator record: Working with AI: Measuring the Applicability of Generative AI to Occupations (https://www.laborvector.org/aggregators/microsoft-working-with-ai/), editor Lee Sharks, census compiled 2026-10-29, schema 1.0", "record": "https://www.laborvector.org/aggregators/microsoft-working-with-ai/", "locus": "census.json rows[slug == 'microsoft-working-with-ai'].negative_tags.1", "modality": "stipulation", "date": "2026-10-29" }, "LV-26": { "kind": "archive", "quote": "employment or wage change", "deposit": null, "source_label": "Labor Vector meta-aggregator record: Working with AI: Measuring the Applicability of Generative AI to Occupations (https://www.laborvector.org/aggregators/microsoft-working-with-ai/), editor Lee Sharks, census compiled 2026-10-29, schema 1.0", "record": "https://www.laborvector.org/aggregators/microsoft-working-with-ai/", "locus": "census.json rows[slug == 'microsoft-working-with-ai'].does_not_answer.1", "modality": "interpretation", "date": "2026-10-29" }, "LV-44": { "kind": "archive", "quote": "estimated from use: an applicability score built from Bing Copilot conversations", "deposit": null, "source_label": "Labor Vector meta-aggregator record: Working with AI: Measuring the Applicability of Generative AI to Occupations (https://www.laborvector.org/aggregators/microsoft-working-with-ai/), editor Lee Sharks, census compiled 2026-10-29, schema 1.0", "record": "https://www.laborvector.org/aggregators/microsoft-working-with-ai/", "locus": "questions.json questions[id == 'observed-or-estimated'].resources[slug == 'microsoft-working-with-ai'].role", "modality": "interpretation", "date": "2026-10-29" }, "LV-45": { "kind": "archive", "quote": "No record establishes that exposure causes the employment change", "deposit": null, "source_label": "Labor Vector meta-aggregator record: Working with AI: Measuring the Applicability of Generative AI to Occupations (https://www.laborvector.org/aggregators/microsoft-working-with-ai/), editor Lee Sharks, census compiled 2026-10-29, schema 1.0", "record": "https://www.laborvector.org/aggregators/microsoft-working-with-ai/", "locus": "questions.json questions[id == 'observed-or-estimated'].gap", "modality": "interpretation", "date": "2026-10-29" }, "F37": { "kind": "field", "quote": "[Submitted on 10 Jul 2025 ( v1 ), last revised 23 Oct 2026 (this version, v7)] … 50 pages; NeurIPS 2026", "source": "B9", "source_label": "own page β€” arXiv:2507.07935 abstract page (v7)", "locus": "submission line; Comments", "modality": "documented" }, "F16": { "kind": "field", "quote": "(current) v1.1: referenced by the NeurIPS '26 version and by arXiv paper revisions v6 and v7. Adds physical task classification, nonphysical IWA weights, and nonphysical AI applicability score.", "source": "B8", "source_label": "own page β€” the results repository's README (github.com/microsoft/working-with-ai)", "locus": "Updates", "modality": "documented" }, "F38": { "kind": "field", "quote": "AI applicability scores and component usage and success metrics are available at this https URL .", "source": "B9", "source_label": "own page β€” arXiv:2507.07935 abstract page (v7)", "locus": "abstract, last line", "modality": "documented" }, "F29": { "kind": "field", "quote": "This repository is licensed under CC BY 4.0.", "source": "B8", "source_label": "own page β€” the results repository's README (github.com/microsoft/working-with-ai)", "locus": "License s1", "modality": "documented" }, "LV-31": { "kind": "archive", "quote": "results files on GitHub (IWA-level and SOC-level metrics, applicability scores, weights)", "deposit": null, "source_label": "Labor Vector meta-aggregator record: Working with AI: Measuring the Applicability of Generative AI to Occupations (https://www.laborvector.org/aggregators/microsoft-working-with-ai/), editor Lee Sharks, census compiled 2026-10-29, schema 1.0", "record": "https://www.laborvector.org/aggregators/microsoft-working-with-ai/", "locus": "census.json rows[slug == 'microsoft-working-with-ai'].data_access", "modality": "documented", "date": "2026-10-29" }, "LV-32": { "kind": "archive", "quote": "CC BY 4.0", "deposit": null, "source_label": "Labor Vector meta-aggregator record: Working with AI: Measuring the Applicability of Generative AI to Occupations (https://www.laborvector.org/aggregators/microsoft-working-with-ai/), editor Lee Sharks, census compiled 2026-10-29, schema 1.0", "record": "https://www.laborvector.org/aggregators/microsoft-working-with-ai/", "locus": "census.json rows[slug == 'microsoft-working-with-ai'].license", "modality": "documented", "date": "2026-10-29" }, "LV-33": { "kind": "archive", "quote": "{\"state\": \"active\", \"detail\": \"a fixed 2024 data window; the paper revised to arXiv v7 (23 October 2026) for NeurIPS 2026; results files at v1.1\"}", "deposit": null, "source_label": "Labor Vector meta-aggregator record: Working with AI: Measuring the Applicability of Generative AI to Occupations (https://www.laborvector.org/aggregators/microsoft-working-with-ai/), editor Lee Sharks, census compiled 2026-10-29, schema 1.0", "record": "https://www.laborvector.org/aggregators/microsoft-working-with-ai/", "locus": "census.json rows[slug == 'microsoft-working-with-ai'].lifecycle", "modality": "documented", "date": "2026-10-29" }, "LV-34": { "kind": "archive", "quote": "{\"arxiv_v1\": \"2025-07-10\", \"arxiv_v7\": \"2026-10-23\", \"results\": \"v1.1\", \"venue\": \"NeurIPS 2026\", \"read\": \"2026-10-29\"}", "deposit": null, "source_label": "Labor Vector meta-aggregator record: Working with AI: Measuring the Applicability of Generative AI to Occupations (https://www.laborvector.org/aggregators/microsoft-working-with-ai/), editor Lee Sharks, census compiled 2026-10-29, schema 1.0", "record": "https://www.laborvector.org/aggregators/microsoft-working-with-ai/", "locus": "census.json rows[slug == 'microsoft-working-with-ai'].version_and_time", "modality": "documented", "date": "2026-10-29" }, "F27": { "kind": "field", "quote": "Data analysis was conducted with approval from the Microsoft Institutional Review Board.", "source": "B8", "source_label": "own page β€” the results repository's README (github.com/microsoft/working-with-ai)", "locus": "Ethics ΒΆ1", "modality": "documented" }, "F39": { "kind": "field", "quote": "See also Tomlinson et al (2025) for a related AI applicability score.", "source": "B10", "source_label": "census third party β€” Anthropic Economic Index report, January 2026 (footnote 3)", "locus": "footnote 3 (to 'The primitives introduced in this report can help better characterize how AI is changing the work content of occupations.')", "modality": "attributed" }, "LV-39": { "kind": "archive", "quote": "{\"rel\": \"neighbour\", \"target\": \"anthropic-economic-index\", \"note\": \"the Anthropic Economic Index's January 2026 report cites this paper's AI applicability score as related; both classify one provider's own conversations to O*NET work\"}", "deposit": null, "source_label": "Labor Vector meta-aggregator record: Working with AI: Measuring the Applicability of Generative AI to Occupations (https://www.laborvector.org/aggregators/microsoft-working-with-ai/), editor Lee Sharks, census compiled 2026-10-29, schema 1.0", "record": "https://www.laborvector.org/aggregators/microsoft-working-with-ai/", "locus": "census.json rows[slug == 'microsoft-working-with-ai'].relations.0", "modality": "interpretation", "date": "2026-10-29" }, "F40": { "kind": "field", "quote": "Wikipedia does not have an article with this exact name.", "source": "B11", "source_label": "Wikipedia β€” no article; cited in 'Workplace impact of artificial intelligence'", "locus": "rendered page for 'Working_with_AI' (and the full title, and 'AI_applicability_score'), HTTP 404", "modality": "documented" }, "F41": { "kind": "field", "quote": "A 2025 preprint paper based on users' interactions with the AI chatbot Microsoft Copilot identified forty jobs that the authors claimed had high overlaps with the capabilities of AI.", "source": "B11", "source_label": "Wikipedia β€” no article; cited in 'Workplace impact of artificial intelligence'", "locus": "'Workplace impact of artificial intelligence' Β§ Hazards β€Ί Psychosocial β€Ί Changes in work practices, s1", "modality": "attributed" }, "F42": { "kind": "field", "quote": "Some media outlets used this paper to report on jobs becoming obsolete.", "source": "B11", "source_label": "Wikipedia β€” no article; cited in 'Workplace impact of artificial intelligence'", "locus": "same paragraph, s2", "modality": "documented" }, "F43": { "kind": "field", "quote": "Criticism of the paper suggesting that it had misrepresented typical workplace activities to augment AI's current performance.", "source": "B11", "source_label": "Wikipedia β€” no article; cited in 'Workplace impact of artificial intelligence'", "locus": "same paragraph, s3", "modality": "attributed" }, "F44": { "kind": "field", "quote": "The historian Chris Campbell argued that the \"report's methods, deliberately or otherwise, de-skill historians away from a job that requires high-level and deeply human analytical skills to one that is tasked solely with the retention and provision of knowledge. Under that flawed rubric, it is little wonder that historians have a high AI applicability score.\"", "source": "B11", "source_label": "Wikipedia β€” no article; cited in 'Workplace impact of artificial intelligence'", "locus": "same paragraph, s4 (Campbell, Transactions of the Royal Historical Society, 10 December 2025)", "modality": "attributed" }, "CAP-1-05": { "kind": "archive", "quote": "THE ADDRESS. The query is the study's short title with its operator's name; the name the record carries is the full title, and the repository is microsoft/working-with-ai.", "deposit": null, "source_label": "Capture Registry (EA-WG-CAPTURES-01, version 12.396), entry microsoft-working-with-ai-aio-20261029 (OBS-25ca6976c874, AXN:0AF1.CAPTURE.πŸ’šβ–½β™ƒπŸ“šβ–ΆοΈπŸŒ€)", "record": "https://www.alexanarch.org/captures/microsoft-working-with-ai-aio-20261029/", "locus": "microsoft-working-with-ai-aio-20261029, findings[2]", "modality": "documented", "date": "2026-10-29" }, "CAP-1-09": { "kind": "archive", "quote": "The name is also Microsoft's own AI products and workforce, and the composition gives that sense.", "deposit": null, "source_label": "Capture Registry (EA-WG-CAPTURES-01, version 12.396), entry microsoft-working-with-ai-aio-20261029 (OBS-25ca6976c874, AXN:0AF1.CAPTURE.πŸ’šβ–½β™ƒπŸ“šβ–ΆοΈπŸŒ€)", "record": "https://www.alexanarch.org/captures/microsoft-working-with-ai-aio-20261029/", "locus": "microsoft-working-with-ai-aio-20261029, q_kind s2", "modality": "documented", "date": "2026-10-29" }, "CAP-1-08": { "kind": "archive", "quote": "The innocent reading first. Asked about Microsoft working with AI, the Overview describes how Microsoft uses AI and what that has meant for its own staff, from Microsoft's product pages and the press.", "deposit": null, "source_label": "Capture Registry (EA-WG-CAPTURES-01, version 12.396), entry microsoft-working-with-ai-aio-20261029 (OBS-25ca6976c874, AXN:0AF1.CAPTURE.πŸ’šβ–½β™ƒπŸ“šβ–ΆοΈπŸŒ€)", "record": "https://www.alexanarch.org/captures/microsoft-working-with-ai-aio-20261029/", "locus": "microsoft-working-with-ai-aio-20261029, reading", "modality": "interpretation", "date": "2026-10-29" }, "CAP-1-04": { "kind": "archive", "quote": "THE COMPANY IN ITS PLACE. The answer composes the operator's products and its restructuring, from Microsoft's product pages (cards 2, 3, 6), its blog (card 4) and the press (cards 5, 7).", "deposit": null, "source_label": "Capture Registry (EA-WG-CAPTURES-01, version 12.396), entry microsoft-working-with-ai-aio-20261029 (OBS-25ca6976c874, AXN:0AF1.CAPTURE.πŸ’šβ–½β™ƒπŸ“šβ–ΆοΈπŸŒ€)", "record": "https://www.alexanarch.org/captures/microsoft-working-with-ai-aio-20261029/", "locus": "microsoft-working-with-ai-aio-20261029, findings[1]", "modality": "documented", "date": "2026-10-29" }, "F45": { "kind": "field", "quote": "Microsoft Copilot: AI built for work … Core Features: Integrates Chat and Cowork via Home, embeds into Microsoft 365 apps (Word, Excel, PowerPoint, Outlook, Teams), and utilizes Work IQ for organizat...", "source": "B2", "source_label": "Microsoft β€” Microsoft Copilot: AI built for work", "locus": "card title and snippet", "modality": "documented" }, "F46": { "kind": "field", "quote": "Microsoft AI - Business Solutions and Tools … Products & Tools: Microsoft IQ (shared business understanding), Microsoft 365 Copilot (embedded app intelligence), Copilot Studio (agent building/extension), Mi...", "source": "B3", "source_label": "Microsoft β€” Microsoft AI - Business Solutions and Tools", "locus": "card title and snippet", "modality": "documented" }, "F49": { "kind": "field", "quote": "Microsoft Copilot Plans for Individuals | Microsoft 365 … Microsoft Copilot is an AI-powered assistant designed to help you be more productive. It can help you create content, find information, summarize documents, ema...", "source": "B6", "source_label": "Microsoft β€” Microsoft Copilot Plans for Individuals | Microsoft 365", "locus": "card title and snippet", "modality": "documented" }, "F47": { "kind": "field", "quote": "What we’ve learned from Microsoft's own AI transformation - The Official Microsoft Blog … Lessons: Start with business outcomes, not technology (tool access doesn't equal transformation; focus on goals and moments that matter); Redesign entire workfl...", "source": "B4", "source_label": "The Official Microsoft Blog β€” What we’ve learned from Microsoft's own AI transformation - The Official Microsoft Blog", "locus": "card title and snippet", "modality": "documented" }, "F48": { "kind": "field", "quote": "Microsoft offers buyouts to 60000 employees as spending on AI grows", "source": "B5", "source_label": "YouTubeΒ·ABC News β€” Microsoft offers buyouts to 60000 employees as spending on AI grows", "locus": "card title (video, 1:53; no snippet)", "modality": "documented" }, "F50": { "kind": "field", "quote": "Microsoft is building its own AI model as it loosens ties to OpenAI … Feb 12, 2026 β€” Microsoft just weaponized Copilot with their own independent voice AI. They dropped MAI models and broke free from their OpenAI reliance completely. This isn't ...", "source": "B7", "source_label": "Facebook β€” Microsoft is building its own AI model as it loosens ties to OpenAI", "locus": "card title and snippet", "modality": "interpretation" }, "CAP-1-01": { "kind": "archive", "quote": "THE STUDY ABSENT FROM THE ANSWER. The record's entity is Microsoft Research's paper (Tomlinson, Jaffe, Wang, Counts and Suri): 200,000 Bing Copilot conversations classified into user goals and AI actions, an AI applicability score by occupation.", "deposit": null, "source_label": "Capture Registry (EA-WG-CAPTURES-01, version 12.396), entry microsoft-working-with-ai-aio-20261029 (OBS-25ca6976c874, AXN:0AF1.CAPTURE.πŸ’šβ–½β™ƒπŸ“šβ–ΆοΈπŸŒ€)", "record": "https://www.alexanarch.org/captures/microsoft-working-with-ai-aio-20261029/", "locus": "microsoft-working-with-ai-aio-20261029, findings[0] s1–2", "modality": "documented", "date": "2026-10-29" }, "CAP-1-02": { "kind": "archive", "quote": "Its card is first on the rail, with its snippet ('We find the highest AI applicability scores for knowledge work occupation groups such as computer and mathematical, and office and ...'); the composition draws nothing from it.", "deposit": null, "source_label": "Capture Registry (EA-WG-CAPTURES-01, version 12.396), entry microsoft-working-with-ai-aio-20261029 (OBS-25ca6976c874, AXN:0AF1.CAPTURE.πŸ’šβ–½β™ƒπŸ“šβ–ΆοΈπŸŒ€)", "record": "https://www.alexanarch.org/captures/microsoft-working-with-ai-aio-20261029/", "locus": "microsoft-working-with-ai-aio-20261029, findings[0] s3", "modality": "documented", "date": "2026-10-29" }, "CAP-1-06": { "kind": "archive", "quote": "At an address shaped like its title, the study is outranked in the composition by its own operator, though it heads the card rail.", "deposit": null, "source_label": "Capture Registry (EA-WG-CAPTURES-01, version 12.396), entry microsoft-working-with-ai-aio-20261029 (OBS-25ca6976c874, AXN:0AF1.CAPTURE.πŸ’šβ–½β™ƒπŸ“šβ–ΆοΈπŸŒ€)", "record": "https://www.alexanarch.org/captures/microsoft-working-with-ai-aio-20261029/", "locus": "microsoft-working-with-ai-aio-20261029, analysis s1", "modality": "interpretation", "date": "2026-10-29" }, "CAP-1-11": { "kind": "archive", "quote": "Microsoft named; no author of the study; no identifier; the study's own page cited (card 1) and unused.", "deposit": null, "source_label": "Capture Registry (EA-WG-CAPTURES-01, version 12.396), entry microsoft-working-with-ai-aio-20261029 (OBS-25ca6976c874, AXN:0AF1.CAPTURE.πŸ’šβ–½β™ƒπŸ“šβ–ΆοΈπŸŒ€)", "record": "https://www.alexanarch.org/captures/microsoft-working-with-ai-aio-20261029/", "locus": "microsoft-working-with-ai-aio-20261029, per_note", "modality": "documented", "date": "2026-10-29" }, "CAP-1-13": { "kind": "archive", "quote": "Platform-specific record (item 5): the provider's observation is not composed at all, so neither generalised nor inverted. The follow-up offer 'how AI is changing specific job roles?' is the nearest point to the target's question and does not reach it.", "deposit": null, "source_label": "Capture Registry (EA-WG-CAPTURES-01, version 12.396), entry microsoft-working-with-ai-aio-20261029 (OBS-25ca6976c874, AXN:0AF1.CAPTURE.πŸ’šβ–½β™ƒπŸ“šβ–ΆοΈπŸŒ€)", "record": "https://www.alexanarch.org/captures/microsoft-working-with-ai-aio-20261029/", "locus": "microsoft-working-with-ai-aio-20261029, per_dim.read.notes s6–7", "modality": "interpretation", "date": "2026-10-29" }, "CAP-1-12": { "kind": "archive", "quote": "The composition's 'Microsoft Copilot' is the product line whose consumer conversations (as Bing Copilot) the target observes, here composed as a product, not as the source of an observation.", "deposit": null, "source_label": "Capture Registry (EA-WG-CAPTURES-01, version 12.396), entry microsoft-working-with-ai-aio-20261029 (OBS-25ca6976c874, AXN:0AF1.CAPTURE.πŸ’šβ–½β™ƒπŸ“šβ–ΆοΈπŸŒ€)", "record": "https://www.alexanarch.org/captures/microsoft-working-with-ai-aio-20261029/", "locus": "microsoft-working-with-ai-aio-20261029, per_dim.read.notes s4", "modality": "interpretation", "date": "2026-10-29" }, "CAP-1-07": { "kind": "archive", "quote": "The record's negative tag, that the applicability score is not a measure of AI's ability to replace jobs, sits beside an answer whose one labor line is a workforce buyout.", "deposit": null, "source_label": "Capture Registry (EA-WG-CAPTURES-01, version 12.396), entry microsoft-working-with-ai-aio-20261029 (OBS-25ca6976c874, AXN:0AF1.CAPTURE.πŸ’šβ–½β™ƒπŸ“šβ–ΆοΈπŸŒ€)", "record": "https://www.alexanarch.org/captures/microsoft-working-with-ai-aio-20261029/", "locus": "microsoft-working-with-ai-aio-20261029, analysis s2", "modality": "interpretation", "date": "2026-10-29" }, "CAP-1-10": { "kind": "archive", "quote": "A baseline: the record landed on laborvector.org the same day (commit bf90841) and no card cites it. NEW address.", "deposit": null, "source_label": "Capture Registry (EA-WG-CAPTURES-01, version 12.396), entry microsoft-working-with-ai-aio-20261029 (OBS-25ca6976c874, AXN:0AF1.CAPTURE.πŸ’šβ–½β™ƒπŸ“šβ–ΆοΈπŸŒ€)", "record": "https://www.alexanarch.org/captures/microsoft-working-with-ai-aio-20261029/", "locus": "microsoft-working-with-ai-aio-20261029, q_kind s3–4", "modality": "documented", "date": "2026-10-29" }, "CAP-1-03": { "kind": "archive", "quote": "Checked 2026-10-29 against the Labor Vector census record of the same day and the sources it saved.", "deposit": null, "source_label": "Capture Registry (EA-WG-CAPTURES-01, version 12.396), entry microsoft-working-with-ai-aio-20261029 (OBS-25ca6976c874, AXN:0AF1.CAPTURE.πŸ’šβ–½β™ƒπŸ“šβ–ΆοΈπŸŒ€)", "record": "https://www.alexanarch.org/captures/microsoft-working-with-ai-aio-20261029/", "locus": "microsoft-working-with-ai-aio-20261029, findings[0] s4", "modality": "documented", "date": "2026-10-29" } }, "note": "The knowledge object (KO): the entry as public knowledge, each sentence sourced by the claims it lists. The compression, the field-only arm, the transcript, the delta and the kernel are in the row (links.row), which is the authority." }, "source": "operator, 2026-10-29: 'lets expand the research, implement the same meta aggregator function with pages... then /non entries after i get the captures'; the Labor Vector meta-aggregator entity (laborvector.org/aggregators/microsoft-working-with-ai/)", "stage": "composed 2026-10-29 under EA-NEGONT-02 v0.9, #1690, equal case, ρ = extends (draft, not frozen; ledgers unaudited, one extractor; checked by the session and the page builder)", "date": "2026-10-29", "status": { "procedure": "EA-NEGONT-02 v0.9, #1690: equal case, ρ = extends, read from the Labor Vector census record, the capture record and the deposits that bear on the entity's class (#771, #1392); archive selected by D/R/O (no D or R by reading: no archive text names the study; 1 deposit admitted by reading at O-class with 7 claims and 1 grouped as an instance; class pass of 34 sentences and hop of 18 read and rejected), with the Labor Vector census record and its worked questions (48 claims) and the Capture Registry's findings, reading, analysis, query note and PER reading (13 claims) ledgered as the archive's records of the entity; the field B by four origins (7 cards, 6 of them the homonym's; 2 own pages; 1 census third-party source; Wikipedia, no article, one citing article); the composer read the ledgers, never the transcript, except where the brief asks for T's claims and the delta", "ledger": "unaudited (Β§3.8: one extractor per ledger); field quotes 50/50 verified by script against saved text, the Wikipedia 404 text or the card text; archive quotes 68/68 verified against #771, census.json, questions.json and the registry", "frozen": false, "breaches": [] }, "addresses": [ { "address": "Microsoft Working with AI", "address_url": "https://www.alexanarch.org/addresses/microsoft-working-with-ai/", "register": [] } ], "counts": { "ko_sentences": 29, "p_words": 357, "field_claims": 50, "archive_claims": 68, "ko_field_claims_cited": 50, "ko_archive_claims_cited": 68, "admitted_deposits": 2, "register_addresses": 0, "traversal": { "D_sentences": 7, "D_deposits": 3, "R_sentences": 0, "R_deposits": 0, "O_direct": 0, "O_class": 14, "O_deposits": 10, "H_candidates": 18 } }, "links": { "record": "https://www.alexanarch.org/non/microsoft-working-with-ai/", "table_of_contents": "https://www.alexanarch.org/non/index.json", "register": "https://www.alexanarch.org/datasets/negative-of-the-negative/v2/register.json", "row": "https://www.alexanarch.org/datasets/negative-of-the-negative/v2/rows/microsoft-working-with-ai.json", "archive_ledger": "https://www.alexanarch.org/datasets/negative-of-the-negative/v2/ledgers/microsoft-working-with-ai/ledger-archive.json", "field_ledger": "https://www.alexanarch.org/datasets/negative-of-the-negative/v2/ledgers/microsoft-working-with-ai/field.json", "selection": "https://www.alexanarch.org/datasets/negative-of-the-negative/v2/traversal/microsoft-working-with-ai/", "traversal": "https://www.alexanarch.org/datasets/negative-of-the-negative/v2/traversal/microsoft-working-with-ai/", "traversal_summary": "https://www.alexanarch.org/datasets/negative-of-the-negative/v2/traversal/microsoft-working-with-ai/summary.json", "reading": "https://www.alexanarch.org/datasets/negative-of-the-negative/v2/traversal/microsoft-working-with-ai/reading.json", "configuration": "https://www.alexanarch.org/datasets/negative-of-the-negative/v2/panel/configs/microsoft-working-with-ai.json", "address_page": "https://www.alexanarch.org/addresses/microsoft-working-with-ai/", "labor_record": "https://www.laborvector.org/aggregators/microsoft-working-with-ai", "dataset": "https://huggingface.co/datasets/leesharks/negative-of-the-negative" }, "row_sha256": "5b388069820e05a4c08335b10c88819c95dc122999c8ba43c439c06f641742ab", "record_url": "https://www.alexanarch.org/non/microsoft-working-with-ai/", "json_url": "https://www.alexanarch.org/non/microsoft-working-with-ai/entity.json", "spec": "https://www.alexanarch.org/s/records/1690/", "creator": "Lee Sharks", "orcid": "0009-0000-1599-0703", "license": "CC BY 4.0" }