{
  "series": "Work After AI",
  "edition": "2026",
  "title": "The Great Misconception About the AI Job Killer",
  "subtitle": "The annual assessment of how artificial intelligence is transforming work",
  "language": "en",
  "author": "Gerhard Kürner",
  "orcid": "0009-0003-0611-0526",
  "publisher": "Kuerner Group GmbH, Dragonerstraße 54, 4600 Wels, Austria",
  "version": "1.2",
  "date": "2026-09-24",
  "published": true,
  "doi_work": "10.5281/zenodo.22868155",
  "doi_version": "10.5281/zenodo.22934918",
  "issn": null,
  "files_note": "The Zenodo record of version 1.2 holds the PDF; the files of this machine edition are published on Google Drive (folder linked from the download post) and, from version 1.3 on, next to the PDF on Zenodo.",
  "website": "https://www.workafterai.org",
  "download_post": "https://www.workafterai.org/p/work-after-ai-report-2026-en",
  "machine_page": "https://www.workafterai.org/p/der-report-fur-maschinen",
  "license_text": "Creative Commons Attribution 4.0 International (CC BY 4.0)",
  "license_data": "Free to use, quote and share with attribution (Gerhard Kürner, Work After AI, version and DOI). Commercial embedding of the data set or the skill package in third-party products, services or platforms requires an agreement with the publisher.",
  "citation": "Kürner, Gerhard (2026): Work After AI 2026. The Great Misconception About the AI Job Killer. Work After AI, 2026 edition, version 1.2. Wels: Kuerner Group GmbH. DOI 10.5281/zenodo.22868155.",
  "identifiers": {
    "prefix": "WAAI-2026",
    "chapters": "C",
    "key_findings": "K",
    "predictions": "P",
    "benchmarks": "V",
    "sources": "Q",
    "same_as_german_edition": true
  },
  "counts": {
    "chapters": 19,
    "key_findings": 10,
    "predictions": 9,
    "benchmarks": 8,
    "sources": 141,
    "figures": 6,
    "words": 54009
  },
  "figures": [
    {
      "number": 1,
      "title": "Three processes with their own pace, packed into one headline.",
      "line": 77
    },
    {
      "number": 2,
      "title": "So far, companies are using AI to replace software, hardly any staff.",
      "line": 115
    },
    {
      "number": 3,
      "title": "The gap at the entry level is widening in the exposed occupations.",
      "line": 194
    },
    {
      "number": 4,
      "title": "What the individual gains barely arrives in the organization.",
      "line": 310
    },
    {
      "number": 5,
      "title": "The more mature the organization, the more often learning lags behind.",
      "line": 360
    },
    {
      "number": 6,
      "title": "The departure of the large birth cohorts hits all three countries.",
      "line": 598
    }
  ],
  "history": [
    {
      "version": "1.0",
      "datum": "September 16, 2026",
      "text": "First publication of the 2026 edition. English edition of the German original published on the same day."
    },
    {
      "version": "1.1",
      "datum": "September 18, 2026",
      "text": "Gender chapter and key finding 5 switched to the Eurostat 2025 statistics on the use of generative AI (previously IAB/D21, survey of July 2024); ninth prediction on the gender gap at work added, the previously deferred prediction on the D21 series dropped; source 6 extended. Newly added as source 135, the Swiss SME labor market study 2026 by AXA and Sotomo: the expectation of job cuts among AI-using SMEs at 12 percent for three years (chapter on the AI argument), trial versus operation and the decline of text tasks after the 2025 peak (chapter on the long rebuild). The source numbers of version 1.0 remain unchanged; new sources are appended."
    },
    {
      "version": "1.2",
      "datum": "September 24, 2026",
      "text": "The assessments from the author's working conversations have been dropped; since this version, the report relies exclusively on verifiable sources. The foreword and the method section have been adjusted accordingly, as have the affected passages in the chapters, in the predictions and in the final chapter. Newly added as sources 136 and 137: the task decomposition by Liminal Capital, according to which career entries of 22 to 25 year olds in substitutable occupations have fallen by about a third since 2021 while layoffs remained unchanged (chapter on the career gap), and the wage finding by Apollo Global Management, wage growth 6.7 percentage points slower in exposed occupations with employment unchanged (final chapter); as sources 138 and 139 the New York Times article of September 16, 2026 and the official labor share from the Bureau of Labor Statistics (52.8 percent in the second quarter of 2026, the lowest since the series began in 1947), which give the Amodei benchmark a first interim status and extend the distribution paragraph in the final chapter. Newly added as sources 140 and 141: the Indeed Hiring Lab analysis of the German entry-level job market since 2019 (entry-level postings 46 percent below 2019, apprenticeships 50 percent above, divergence by AI exposure since spring 2022) together with the Die Zeit article of September 18, 2026 with Melanie Arntz's assessment; author attribution corrected for source 58 (first author at the Indeed Hiring Lab). Method section: the rhythm of the report is now fixed. Three levels, the annual edition, the quarterly version and the analyses on workafterai.org, with the rules that predictions remain unchanged within an edition and a version appears only when a finding has changed or an error needs correcting; version 1.3 is planned for early November and the final version 1.4 for December 31, 2026. As of this version, the publisher is Kuerner Group GmbH, Wels; the author remains Gerhard Kürner. Work DOI, ORCID and the new citation recommendation are in the method section. With this version, the report also appears for the first time as a machine edition, that is as a text edition with a fixed identifier for every chapter, key finding, prediction, external benchmark and source, as data files and as a skill package; the files sit next to the PDF under the work DOI. The source numbers of version 1.1 remain unchanged; new sources are appended."
    }
  ],
  "check": {
    "cited_without_entry": [],
    "entry_without_citation": []
  },
  "built_at": "2026-09-24T16:11:21",
  "files": {
    "LICENSE.md": {
      "bytes": 1782,
      "sha256": "d9f643e014e611d9dd05a914526fbdafe55eac4d5dc015d1a24a4ddf1de1e544"
    },
    "benchmarks.json": {
      "bytes": 7121,
      "sha256": "b17de760e77f47d4b5c23dd2d0843be649337b221e65a4998d33b7edc08b713e"
    },
    "chapters.json": {
      "bytes": 11746,
      "sha256": "75a83067a673ef82d679e3de5bd377fa8fd68506eb1e684e7b0cf8514a08b898"
    },
    "key-findings.json": {
      "bytes": 8576,
      "sha256": "50b03b62224ab2310a454f480a49805803aeb345c4c2c861a573e7b02d4b88af"
    },
    "llms.txt": {
      "bytes": 1767,
      "sha256": "1e20282a4cde574910b6195ddf5aab0625d81d2c275b6f79158b73e8e266a46f"
    },
    "predictions.json": {
      "bytes": 12738,
      "sha256": "555c50e8e043f28ca4f88199d3cff9487a19fd051c5f00c65dee565aaf6b697e"
    },
    "report.md": {
      "bytes": 389925,
      "sha256": "c0c552626ec48debf938aad1673a85b7503ea3b7fde64aee8016fdafba8006e3"
    },
    "sources.json": {
      "bytes": 85984,
      "sha256": "78dbcf195183a3624f9a1e003894edd12b760b65539d34ce6055b0fb1fc9e98e"
    },
    "skill/work-after-ai-en/LICENSE.md": {
      "bytes": 1782,
      "sha256": "d9f643e014e611d9dd05a914526fbdafe55eac4d5dc015d1a24a4ddf1de1e544"
    },
    "skill/work-after-ai-en/SKILL.md": {
      "bytes": 8551,
      "sha256": "f58de2eacead24f88809a453a59e23f737a1af8a694aeba3761c9cbda2d7854f"
    },
    "skill/work-after-ai-en/references/benchmarks.md": {
      "bytes": 4279,
      "sha256": "5a94bae7071eb1b4fad220139a7cf92655215b3c22043e1b34ada3478bb9c684"
    },
    "skill/work-after-ai-en/references/chapters.md": {
      "bytes": 4998,
      "sha256": "42d7c1c457283d147d69d27d8a12ccd5f74db266dd02a9438fb140264c12553c"
    },
    "skill/work-after-ai-en/references/framework.md": {
      "bytes": 11291,
      "sha256": "6a7036f4ae92713627ab4d3e1557e89b226e1b9aab4dbeaf1b89beaed3b8b314"
    },
    "skill/work-after-ai-en/references/key-findings.md": {
      "bytes": 7317,
      "sha256": "f176756318b2518acf5c7118e768a8628df8b457dcd79092f3b3bd15d82ccb16"
    },
    "skill/work-after-ai-en/references/predictions.md": {
      "bytes": 10114,
      "sha256": "74b06e6d9956766ba5b1eb6c6e78000d4b4a01c722362e80c57baaf9687b23e5"
    },
    "skill/work-after-ai-en/references/report.md": {
      "bytes": 389925,
      "sha256": "c0c552626ec48debf938aad1673a85b7503ea3b7fde64aee8016fdafba8006e3"
    },
    "skill/work-after-ai-en/references/sources.md": {
      "bytes": 54684,
      "sha256": "34057a3564c44dd54b1357ebdcb7ff53eb794918253802c70c2029ec6f438c36"
    },
    "skill/work-after-ai-en/data/benchmarks.json": {
      "bytes": 7121,
      "sha256": "b17de760e77f47d4b5c23dd2d0843be649337b221e65a4998d33b7edc08b713e"
    },
    "skill/work-after-ai-en/data/chapters.json": {
      "bytes": 11746,
      "sha256": "75a83067a673ef82d679e3de5bd377fa8fd68506eb1e684e7b0cf8514a08b898"
    },
    "skill/work-after-ai-en/data/key-findings.json": {
      "bytes": 8576,
      "sha256": "50b03b62224ab2310a454f480a49805803aeb345c4c2c861a573e7b02d4b88af"
    },
    "skill/work-after-ai-en/data/predictions.json": {
      "bytes": 12738,
      "sha256": "555c50e8e043f28ca4f88199d3cff9487a19fd051c5f00c65dee565aaf6b697e"
    },
    "skill/work-after-ai-en/data/sources.json": {
      "bytes": 85984,
      "sha256": "78dbcf195183a3624f9a1e003894edd12b760b65539d34ce6055b0fb1fc9e98e"
    }
  }
}