Events

Artificial Intelligence (AI)’s impact on work is important but ambiguous. We first present a conceptual framework that considers the two dimensions of AI’s job content and job opportunity effects. We develop a novel occupational AI exposure measure using a sentence transformer model to compare the semantic similarity between the occupation descriptions and AI patents. We find that, on average, occupations with higher AI exposure experience a decrease in the importance of a wide range of work activities, coupled with an increase in job opportunities. In addition, we observe important heterogeneity in AI’s work activity and job opportunity effects across occupations with different education requirements. We further study how the application of predictive AI or machine learning (ML) changes job requirements for workers. We conceptualize machine learning as “expertise-biased” technological change. To test this proposition, we develop two empirically testable hypotheses based on how expertise is (1) developed through prior work experience and (2) applied in higher-order tasks. Our empirical analyses employ over 51 million job postings and we find that firms utilizing ML technologies also raise their job requirements for prior work experience and skills, especially those related to higher-order tasks such as decision-making and problem-solving. These effects are evident not only for knowledge workers but also for roles that typically do not require a college education. Furthermore, these effects are especially pronounced in occupations characterized by high skill turnover and non-routine work. These findings demonstrate how machine learning utilization within the firm may have changed the skill composition of workers and hence reshaping the nature of work at scale.