The upper panel shows the share of survey respondents who use generative AI for work. The lower panel reports the share of last week’s working hours spent using generative AI. For daily usage, respondents could indicate (i) no usage, (ii) less than 15 minutes, (iii) 15–60 minutes, (iv) 1–4 hours, or (v) more than 4 hours. To obtain the total minutes spent using generative AI, the authors assume daily usage of 0, 7.5 minutes, 37.5 minutes, 2.5 hours, and 4 hours for each option, respectively. Weekly usage is calculated by combining daily usage with the reported number of days used. If respondents report using generative AI on ‘some days’ (rather than one or all days), the authors assume they used it on half of their working days. For non-users, the share of work hours using generative AI is mechanically 0.
The economic impact of generative AI will depend on the speed and breadth of adoption by workers and firms. Drawing on a survey of workers in the US and six European countries and a firm survey covering 32 European countries, this column explores the speed of AI adoption across countries and whether an ‘AI gap’ will exacerbate existing productivity differences between the US and Europe. There are large differences in AI adoption across countries, much of which are accounted for by management practices. Higher AI adoption rates are associated with faster productivity growth but not with changes in employment.
A Bick, A Blandin, D Deming, N Fuchs-Schündeln, & J Jessen find large differences in AI adoption across countries, much of which are accounted for by management practices. Higher adoption rates are associated w/ + productivity growth but not w/ employment changes.
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