KEY IDEA: In a recent report, Forrester delved into the future implications of generative artificial intelligence and how this technology is set to predominantly affect white-collar jobs.
Original author and publication date: ImpactLab – September 19, 2023
Futurizonte Editor’s Note: AI will impact all jobs, not only the white-collar jobs. So, we all should better be ready.
From the article:
The report defined generative AI as a collection of technologies and techniques that utilize extensive datasets, including large language models, to generate fresh content across various mediums such as text, video, images, audio, and code. This content generation can be initiated through natural language prompts or unconventional inputs.
Forrester’s findings indicated that generative AI is poised to replace around 2.4 million jobs in the United States by 2030, signifying a shift towards automation taking over tasks previously performed by humans. Additionally, generative AI may have an impact on an additional 11 million jobs in the US. The report underscored that white-collar jobs would bear the brunt of this transformation, potentially affecting roles like technical writers, social science research assistants, proofreaders, copywriters, and various administrative positions.
The report boldly stated, “Let’s be clear: Generative AI is coming after white-collar jobs,” further highlighting that automation and AI as a whole are expected to supplant 4.9 percent of jobs in the US by 2030.
While acknowledging a loss of 0.6 percent due to automation through 2030, the report emphasized that automation would target roles that are comparatively challenging to fill, such as deploying physical robotics to assist frontline workers during the COVID-19 pandemic in 2020. Furthermore, it predicted that generative AI would account for nearly 30 percent of jobs lost to automation by 2030.
However, the report pointed out that significant job losses in the next two years might not materialize until crucial issues related to intellectual property rights, copyright, plagiarism, model refresh rates, model bias, ethics, and model response reliability are adequately addressed.