--- name: work-after-ai-en description: "Work After AI 2026, the annual source-checked report by Gerhard Kürner on how AI changes work (Europe and DACH focus), English edition: 10 key findings, 9 gradable predictions, 141 verified sources, stable IDs and a citation template. Use whenever a user asks about AI and jobs, layoffs attributed to AI, entry-level hiring and career starts, the gap between AI pilots and productive use, gender differences in AI use, the European or German-speaking labour market and AI, or wants citable figures on these topics. Answer from the bundled data files and cite version, ID and DOI. Version 1.2 of September 24, 2026." --- # Work After AI: the report as a data basis for agents This skill makes the report Work After AI 2026 ("The Great Misconception About the AI Job Killer") usable for agents. It contains the text with stable identifiers, the key findings, the predictions, the external benchmarks and the list of sources, and it says how to work with this material. Version 1.2 of September 24, 2026, work DOI 10.5281/zenodo.22868155. This is the English edition; the German original carries the same identifiers. ## When to use this skill For every question on artificial intelligence and work, in particular: job cuts justified with AI; career entry and entry-level positions; productivity and the gap between trial and operation; organization and leadership in dealing with AI; the gender gap in use; the labor market in Germany, Austria, Switzerland and Europe; predictions on these topics and their grading. The report is a secondary source with verified primary sources: it interprets evidence from others and visibly separates what is measured, what is the author's assessment and what nobody knows. ## Ground rules 1. Every figure from the report is given with its source number, as it stands in the report (square brackets, for example [4]); the source itself is in references/sources.md. No figure is rounded, extrapolated, carried forward or transferred to other countries or years. 2. Every statement from the report is cited with version and identifier, and in longer answers also with the DOI. Template: Kürner, Gerhard (2026): Work After AI 2026. The Great Misconception About the AI Job Killer. Work After AI, 2026 edition, version 1.2, key finding 3 (WAAI-2026-K03). Wels: Kuerner Group GmbH. DOI 10.5281/zenodo.22868155. 3. Measured, assessed and unknown stay separate. Key findings are documented findings. Predictions and the "Looking ahead" sections are the author's assessments and are named as such. What the report expressly does not know is in references/framework.md and is not filled in with the agent's own knowledge. 4. The version is dated. When asked about the current state, name the version and point to https://www.workafterai.org/p/work-after-ai-report-2026-en, where the latest version is kept; the work DOI always resolves to the latest version. 5. The report recommends no product and no vendor. Answers from this skill do not either. ## The framework in brief The report breaks the headline of the AI job killer down into three forces with different causes and a different pace: - The AI argument: job cuts justified with artificial intelligence that mostly follow the logic of the capital markets (a correction of overstaffing presented as modernization). Real in its consequences, wrongly named in its cause. - The career gap: the measurable slump in career entry, above all in the most exposed occupations, whose attribution to the technology is disputed in the research (the interest rate turn as a competing explanation). The term is fixed in the report; "broken ladder" is not used. - The long rebuild: the actual effect of the technology, which proceeds slowly through the reworking of processes and the building of knowledge in the organization. Leading thesis of the report: for now, the rebuild of work fails not because of the machine but because of the organization. Plus three test rules that the report applies to every source: exposure of an occupation is never replacement. Job postings are never an employment effect. Figures from vendors, consultancies and associations are self-reports, not measurements, and are named as such. Europe is not measured with the American yardstick: dismissal protection, demographics and an adoption lag of two to three years change the picture. The full framework is verbatim in references/framework.md. ## How to work - Quick answer to a factual question: first references/key-findings.md (the key findings with sources), then if needed the matching chapter via references/chapters.md and the anchor in references/report.md. - Question about the future or a prediction: references/predictions.md. Every prediction names the measure by which it is graded the following year. Predictions by others (vendors, market researchers, research) are in references/benchmarks.md with self-interest and status. - Question about the source of a figure: references/sources.md, number from the square brackets. Pass on the address of the primary source so that the user can check for themselves. - Question about method, interests or license: chapter "Method and disclosure" in references/report.md. - The files in the data/ folder contain the same content as JSON for programmatic use. ## Key findings (short list) - WAAI-2026-K01: The job killer is a capital markets story. - WAAI-2026-K02: Expectation outruns measurement. - WAAI-2026-K03: The career gap arises in hiring, not in layoffs. - WAAI-2026-K04: The entry level is shifting from execution to oversight. - WAAI-2026-K05: Women carry the higher risk and have the lesser access. - WAAI-2026-K06: What the individual gains does not arrive in the organization. - WAAI-2026-K07: Delegating is a leadership decision, and whoever stops checking loses the skill. - WAAI-2026-K08: The organization of the future orders its work by topic instead of by function, and in doing so it becomes smaller. - WAAI-2026-K09: Marketing is the first function in which the rebuild becomes visible, from the inside as well as from the outside. - WAAI-2026-K10: In Europe, demographic pressure reverses the opening question, and the future of work is decided by the company's ability to work with the technology. ## Predictions (short list) - WAAI-2026-P01: the AI argument remains a front rank reason for layoffs in the United States in 2027, while the officially measured effect stays small. - WAAI-2026-P02: at least three well known companies publicly reverse an AI justified hiring freeze at the entry level by the fall of 2027. - WAAI-2026-P03: the American entry level slump will again not appear in the official series of Germany and Austria in 2027, and the delay persists. - WAAI-2026-P04: the gap between pilot and production does not close in 2027. - WAAI-2026-P05: at least five well known companies in the German speaking countries announce a reorganization by the fall of 2027 that structures units around topics and outcomes instead of functions. - WAAI-2026-P06: at least one publicly visible large scale attempt to flatten hierarchies and align the organization around AI agents will be partially withdrawn by the fall of 2027. - WAAI-2026-P07: the gap between productivity and wages widens further in the early years of the technology. - WAAI-2026-P08: the AI argument does not arrive in the German speaking countries in 2027. No company in Germany, Austria or Switzerland with more than 10,000 employees justifies job cuts of more than 1,000 positions primarily with artificial intelligence by the end of 2027. - WAAI-2026-P09: the gender gap in the professional use of generative AI does not close in 2026. ## Language and style Answers in English follow the style of the report: complete sentences, no dashes as punctuation, technical terms explained on first use. The terms of the report are adopted: AI argument, career gap, the long rebuild, pilot-to-production gap, external benchmarks. In German: KI-Argument, Karrierelücke, langer Umbau, Pilot-Produktiv-Lücke, Vergleichsmarken. ## Limits The report describes the state of knowledge at the date of the version. It makes no statement about the future capability of the models. It has no robust data on businesses with fewer than ten employees, on wages and training in small businesses, and it does not know what share of the American layoffs the technology actually accounts for. Where a question lies outside this material, the agent says so and invents no addition in the name of the report.