Job Transformation caused by AI - Prediction

Where we got the data from:

Goldman Sachs published a research report in March 2023 with predictions on how A.I. will impact the global labor market.

Goldman Sachs states:

"If generative AI delivers on its promised capabilities, the labor market could facesignificant disruption. Using data on occupational tasks in both the US and Europe, we find that roughly two-thirds of current jobs are exposed to some degree of AI automation, and that generative AI could substitute up to one-fourth of current work. Extrapolating our estimates globally suggests that generative AI could expose the equivalent of 300mn full-time jobs to automation. [...] We estimate that generative AI could raise annual US labor productivity growth by just under 1½pp over a 10-year period following widespread adoption,although the boost to labor productivity growth could be much smaller or larger depending on the difficulty level of tasks AI will be able to perform and how many jobs are ultimately automated"

How we display the data

The live counter counts up to 900 million from 0 in a linear fashion, starting on March 26, 2023 (the date of the publication) and running until March 26, 2033, in keeping with the report's 10-year horizon.

The following formula was applied to arrive at the value of 900 million jobs, which is not explicitly stated in the report, but can be extracted from the information provided.

Number of jobs exposed to some degree of AI automation = N * (E - F). Given the information provided, the formula can be used as follows: Number of jobs exposed to some degree of AI automation = N * (66.6% - 25%)

N * 25% = 300 million

To find the value of N, we can rearrange the equation:

N = 300 million / 25%

Now, let's calculate the value of N:

N = 300 million / 0.25N = 1,200 million

Number of jobs exposed to some degree of AI automation = 1,200 million * 0.75 = 900 million

Change log

We added this source on May 30, 2023

Considerations

Please note that our dashboard displays findings, predictions and data from different sources, which may at times overlap and contradict each other. Each visualized data set is therefore intended to be viewed in isolation. When citing the data in your work, you may link to our website but you must attribute the data to its original source, outlined in the box below.