This paper explores the impact of machine learning (ML) utilization on job requirements for workers. We postulate that by automating common tasks and processes, ML shifts human workers toward handling more complex and novel scenarios that demand higher professional expertise. Consequently, the adoption of ML may reduce entry-level job opportunities. Our empirical analyses employ over 51 million job postings of S&P 500 companies from 2011 to 2023 with a Shift-Share IV estimation strategy, leveraging texts from occupational descriptions and AI patents. We find that firms utilizing ML significantly raise job requirements for prior work experience and skills, especially those related to decision-making. These effects are evident not only for knowledge workers but also for roles that typically do not require a college education, such as customer service representatives and retail sales. Moreover, we observe large impacts in occupations characterized by high skill turnover and non-routine work. These findings underscore the evolving nature of work in the ML era and its implications for workforce development in a rapidly changing job market.

Research Paper:

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5070648