{"id":12182,"date":"2026-04-02T11:48:07","date_gmt":"2026-04-02T09:48:07","guid":{"rendered":"https:\/\/manualjobsearch.com\/?p=12182"},"modified":"2026-04-02T11:55:58","modified_gmt":"2026-04-02T09:55:58","slug":"ki-und-der-arbeitsmarkt-2025-wer-wirklich-gefaehrdet-ist-und-wer-nicht","status":"publish","type":"post","link":"https:\/\/manualjobsearch.com\/en_au\/ki-und-der-arbeitsmarkt-2025-wer-wirklich-gefaehrdet-ist-und-wer-nicht\/","title":{"rendered":"AI and the labour market 2025: Who is really at risk \u2013 and who isn't"},"content":{"rendered":"
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\nA recent Anthropic study provides real-time data instead of forecasts for the first time. The results contradict everything we thought we knew up until now.<\/i>\n\n

No more forecasts \u2013 real data<\/h2>\nFor years, economists, management consultants and technologists have been predicting which professions will disappear due to artificial intelligence. The predictions were mostly dramatic \u2013 and just as often wrong. The panic about outsourcing in the early 2000s cost hardly any jobs as forecast. The wave of automation in industry proceeded more slowly and selectively than expected.

\n\nThe fundamental problem with these forecasts has always been the same: they were based on what machines could theoretically achieve \u2013 not on what they actually do on a daily basis. A language model can theoretically formulate legal documents, produce financial reports, and debug code. But how often does this actually happen? To what extent? In which professions?

\n\nA study by researchers at Anthropic now answers precisely this question: Maxim Masenkov and Peter Macroory have developed a new metric \u2013 the so-called Observed Task Coverage (OTC). And the results are as astonishing as they are revealing.\n\n

The Method: Theory Meets Reality<\/h2>\nThe researchers combined three data sources:\n
    \n \t
  • O*NET \u2013 the US state occupational classifier. This database breaks down every occupation into precise micro-tasks with time allocations.<\/li>\n \t
  • Theoretical AI performance evaluation \u2013 assessments of how much AI can accelerate tasks.<\/li>\n \t
  • Real-world usage data of the Claude model \u2013 what tasks people actually delegate to AI.<\/li>\n<\/ul>\n\nThe crucial difference: instead of assumptions, real behaviour was analysed.\n\n

    The Food Processor Paradox<\/h2>\nTo illustrate the core problem of earlier studies, the researchers use a vivid analogy:\n\n
    A food processor can theoretically cook a five-course meal \u2013 in practice, it's mostly used for chopping onions.<\/div>\n\nThe same applies to AI. The central question isn't what it can do \u2013 but what is actually being used. The answer: we're still in the \"onion-chopping\" phase.\n\n

    The figures: The gap between potential and reality<\/h2>\nAn example from practice:\n\n
      \n \t
    • Theoretical AI potential (IT professions): 94 % of tasks<\/li>\n \t
    • Actual usage: only about 3 %<\/li>\n<\/ul>\n\nThis enormous gap arises from three central barriers:\n
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      • Legal liability (e.g. medicine, law)<\/li>\n \t
      • Outdated IT infrastructure in companies<\/li>\n \t
      • Human control and decision-making processes<\/li>\n<\/ul>\n\n
        The limitation is not technological \u2013 but institutional.<\/div>\n\n

        Who is truly at risk? The ranking<\/h2>\n\n
        Rang<\/th>Field of work<\/th>Observation Cover<\/th>Main task<\/th><\/tr>\n
        1<\/td>Programmer<\/td>74,5 %<\/td>Code schreiben und pflegen<\/td><\/tr>\n
        2<\/td>Customer support<\/td>70 %<\/td>Communication<\/td><\/tr>\n
        3<\/td>Data entry<\/td>67 %<\/td>Data processing<\/td><\/tr>\n
        4<\/td>Medical Documentation<\/td>~55 %<\/td>Create reports<\/td><\/tr>\n
        5<\/td>Marketing analysis<\/td>~50 %<\/td>Reporting<\/td><\/tr>\n
        6<\/td>Sales<\/td>~45 %<\/td>Offers & Follow-ups<\/td><\/tr>\n
        7<\/td>Financial analysis<\/td>~44 %<\/td>Forecasts<\/td><\/tr>\n
        8<\/td>Software testing<\/td>~40 %<\/td>Test cases<\/td><\/tr>\n
        9<\/td>IT Security<\/td>~38 %<\/td>Threat analysis<\/td><\/tr>\n<\/table>\n\nAt the lower end: artisanal and manual professions \u2013 often with 0 % AI coverage.\n\n

        The Paradox of Education<\/h2>\nA surprising result:\n\n
          \n \t
        • 17 % from the at-risk group have a master's degree<\/li>\n \t
        • Only 4.5 % of this group are barely affected by AI<\/li>\n<\/ul>\n\n
          The higher the qualification, the higher the AI exposure, often.<\/div>\n\nTasks that are purely cognitive \u2013 writing, analysing, structuring \u2013 are particularly well-suited for automation.\n\n

          Where are the mass layoffs?<\/h2>\nDie Antwort: Sie passieren indirekt.\n\nUnternehmen entlassen keine erfahrenen Mitarbeiter. Stattdessen: The answer: They happen indirectly.\n\nCompanies do not lay off experienced employees. Instead:\n
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          • Senior staff become more productive<\/li>\n \t
          • Fewer junior positions are being created<\/li>\n<\/ul>\n\nData shows:\n
              \n \t
            • New hires (aged 22\u201325) in AI roles: \u221214% % since 2022<\/li>\n<\/ul>\n\n
              The door to the job market is quietly closing \u2013 not visibly.<\/div>\n\n

              Long-term effects<\/h2>\nStatistical models show:\n\n
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              • +10 % AI coverage \u2192 \u22120.6 % employment growth<\/li>\n<\/ul>\n\nOver years, this adds up to significant effects \u2013 particularly in highly automated professions.\n\n

                Strategies: What to do?<\/h2>\n\n

                For experienced professionals<\/h3>\n
                  \n \t
                • Using AI as a productivity booster<\/li>\n \t
                • Automate workflows<\/li>\n \t
                • Take on strategic tasks<\/li>\n<\/ul>\n\n

                  For those starting their careers<\/h3>\n
                    \n \t
                  • Focus on skills that AI cannot replace<\/li>\n \t
                  • Communication, judgment, responsibility<\/li>\n<\/ul>\n\n

                    For non-academics<\/h3>\n
                      \n \t
                    • Craftsmanship and physical labour remain stable<\/li>\n \t
                    • High future security in practical professions<\/li>\n<\/ul>\n\n

                      For career changers<\/h3>\n
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                      • Choosing professions with social interaction<\/li>\n \t
                      • Mastering AI as a tool<\/li>\n<\/ul>\n\n

                        Conclusion<\/h2>\nThe Anthropic study doesn't show an extreme scenario \u2013 but rather a nuanced reality.\n\nAI won't suddenly replace millions of jobs. Instead, it will change them:\n
                          \n \t
                        • Access to the labour market<\/li>\n \t
                        • Productivity requirements<\/li>\n \t
                        • Value creation within professions<\/li>\n<\/ul>\n\n
                          The crucial question isn't: \"Will I lose my job?\" \u2013 but rather: \"Am I using AI better than others?\"<\/div>\n\nThe revolution is happening \u2013 quietly, but profoundly.\n\n