{"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_us\/ki-und-der-arbeitsmarkt-2025-wer-wirklich-gefaehrdet-ist-und-wer-nicht\/","title":{"rendered":"AI and the Labor 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 projections for the first time. The results contradict everything previously believed to be known.<\/i>\n\n

No more forecasts \u2013 real data<\/h2>\nFor years, economists, business consultants, and technologists have predicted 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 far fewer jobs than predicted. 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, not on what they actually do in everyday life. A language model can theoretically draft legal documents, create financial reports, and debug code. But how often does that actually happen? To what extent? In which professions?

\n\nA study by Anthropic researchers is now answering this very 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 insightful.\n\n

The Method: Theory Meets Reality<\/h2>\nResearchers combined three data sources:\n
    \n \t
  • O*NET \u2013 the US federal occupational classifier. This database breaks down every occupation into precise micro-tasks with time allocations.<\/li>\n \t
  • Theoretical AI Performance Assessment \u2013 Estimates of how much AI can accelerate tasks.<\/li>\n \t
  • Real usage data of the Claude model \u2013 what tasks people actually delegate to AI.<\/li>\n<\/ul>\n\nThe key difference: Real behavior was analyzed instead of assumptions.\n\n

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

    The Numbers: 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 is caused by three central barriers:\n
        \n \t
      • Legal liability (e.g., medical, legal)<\/li>\n \t
      • Outdated IT infrastructure in companies<\/li>\n \t
      • Human control and decision-making processes<\/li>\n<\/ul>\n\n
        \ud83d\udc49 The limitation is not technological - but institutional.<\/div>\n\n

        Who is really at risk? The ranking<\/h2>\n\n
        Rank<\/th>Occupational field<\/th>Observation coverage<\/th>Main task<\/th><\/tr>\n
        1<\/td>Programmer<\/td>74,5 %<\/td>Write and maintain code<\/td><\/tr>\n
        2<\/td>Customer support<\/td>70 %<\/td>Communication<\/td><\/tr>\n
        3<\/td>Data input<\/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>Distribution<\/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: craft and manual professions \u2013 often with 0 % AI coverage.\n\n

        The paradox of education<\/h2>\nA surprising result:\n\n
          \n \t
        • 17 % of the vulnerable group have a Master's degree<\/li>\n \t
        • Only 4.5 % of this group are hardly affected by AI<\/li>\n<\/ul>\n\n
          The higher the qualification, the higher the AI exposure, often.<\/div>\n\nCognitive activities like writing, analyzing, and structuring are particularly well-suited for automation.\n\n

          Where are the mass layoffs?<\/h2>\nThe answer: they happen indirectly.\n\nCompanies don't lay off experienced employees. Instead:\n
            \n \t
          • Senior employees become more productive<\/li>\n \t
          • Fewer junior positions are being created<\/li>\n<\/ul>\n\nData shows:\n
              \n \t
            • New hires (22\u201325 years old) in AI occupations: \u221214 % since 2022<\/li>\n<\/ul>\n\n
              \ud83d\udc49 The door to the labor market closes quietly - invisibly.<\/div>\n\n

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

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

                For experienced professionals<\/h3>\n
                  \n \t
                • Leveraging AI as a Productivity Booster<\/li>\n \t
                • Automate workflows<\/li>\n \t
                • Take on strategic tasks<\/li>\n<\/ul>\n\n

                  For career starters<\/h3>\n
                    \n \t
                  • Focus on skills that AI does not replace<\/li>\n \t
                  • Communication, judgment, responsibility<\/li>\n<\/ul>\n\n

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

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

                        Conclusion<\/h2>\nThe Anthropic study does not show an extreme scenario - but a differentiated reality.\n\nAI will not suddenly replace millions of jobs. Instead, it is changing them:\n
                          \n \t
                        • Access to the labor market<\/li>\n \t
                        • Productivity requirements<\/li>\n \t
                        • Value creation within professions<\/li>\n<\/ul>\n\n
                          \ud83d\udc49 The crucial question is not: \"Will I lose my job?\" - but: \"Am I using AI better than others?\"<\/div>\n\nThe revolution is happening \u2013 quietly, but profoundly.\n\n