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Most studies of automation focus on manufacturing or use aggregate data. In one of the first studies of the service sector using establishment-level data, we examine the impact of robot adoption on staffing in nursing homes.
In the last year, AI models have rapidly improved on public benchmarks measuring their ability to carry out research tasks autonomously. Such performance suggests that models may be capable of automating parts of the AI R&D process itself, thereby accelerating the pace of AI progress.
In the last year, AI models have rapidly improved on public benchmarks measuring their ability to carry out research tasks autonomously. Such performance suggests that models may be capable of automating parts of the AI R&D process itself, thereby accelerating the pace of AI progress.
This talk will cover key insights from the third AI100 report, AI Goes Mainstream: The Scaling Era and the Rise of Generative AI, set for release in November 2026. The report was commissioned by the AI100 standing committee and led by study panel chair Michael Wooldridge.
This talk will cover key insights from the third AI100 report, AI Goes Mainstream: The Scaling Era and the Rise of Generative AI, set for release in November 2026. The report was commissioned by the AI100 standing committee and led by study panel chair Michael Wooldridge.
Sessions run Wednesdays from 4:30–5:30 PM in CoDa E160. Each session features a different Stanford speaker; talk titles are announced by the organizers.
Sessions run Wednesdays from 4:30–5:30 PM in CoDa E160. Each session features a different Stanford speaker; talk titles are announced by the organizers.
This setting is important, because robots are increasingly being adopted in many countries to address the challenges posed by population aging. Japan, in particular, has been actively developing and deploying robots in nursing homes to deal with labor shortages, and since 2015 has subsidized nursing home purchase of robots. Analyzing 2017 data from Japanese nursing homes, we document that facilities that adopt robots are larger, with more functionally-impaired residents, greater numbers of care workers and nurses, many other assistive technologies, better management practices, and located in prefectures with higher planned subsidies for robots per nursing home. However, using variation in those robot subsidies as an instrumental variable, we find that robot adoption has little causal impact on overall staffing or wages, but leads to additional non-regular nurse hours, and increasing turnover of regular care workers. Robot adoption also reduces the wage share, consistent with compositional change in staffing toward non-regular workers. The tight labor market in Japan appears to have prompted nursing homes to adopt a more capital-intensive production process without many detrimental effects on labor. Our results contrast with recent findings that show negative effects of robots on employment and wages in the US manufacturing sector, suggesting that the impact of robots likely differs by labor market conditions and industry.
