Goldman Sachs, on Sunday, released a new report warning of “significant disruption” to the labor market as a result of the impact of artificial intelligence (AI) on jobs.
The report suggested that as many as two-thirds of jobs in the U.S. and Europe could be automated to some extent, with around 300 million jobs worldwide potentially affected.
In the U.S., “of those occupations which are exposed, most have a significant — but partial — share of their workload (25-50%) that can be replaced,” Goldman Sachs analysts said in the research paper.
However, the report also acknowledges that historically, technological progress has not only made jobs redundant but also created new ones. Additionally, the use of AI technology could boost labor productivity growth and global GDP by up to 7% over time.
While the impact of AI on jobs is a concern, the report highlights the potential for positive economic growth and the creation of new job opportunities in the future.
The most affected Jobs
Jobs that involve a lot of physical work are less likely to be significantly impacted. In the US, office and administrative support jobs are most likely to be automated, with 46% of tasks that could be automated, followed by legal work at 44% and architecture and engineering at 37%.
Conversely, building and ground cleaning and maintenance have only 1% of vulnerable tasks. In Europe, clerical support roles are the most affected with 45% of their work being automated, and crafts and related trades have only 4% of vulnerable work. Overall, 24% of work in Europe could be automated, slightly below the 25% average in the U.S.
Most affected countries
Goldman Sachs further suggested that 18% of work worldwide could be automated by AI, with Hong Kong, Israel, Japan, Sweden, and the U.S. predicted to be the top five most affected countries.
However, employees in mainland China, Nigeria, Vietnam, Kenya, and India are the least likely to see their work taken over by AI. While the report highlights the impact of AI on the labor market, it also emphasizes that the extent of disruption remains unclear, as it will depend on the capability and adoption timeline of the technology. Ultimately, two key factors will be how powerful the technology becomes and how much it is used in practice.