Towards the Terminator Economy: Assessing Job Exposure to Ai Through Llms

Abstract

AI and related technologies are reshaping jobs and tasks, either by automating or augmenting human skills in the workplace. Many researchers have been working on estimating if and to what extent jobs and  tasks are exposed to the risk of being automatized by AI-related technologies. Our work tackles this issue  through a data-driven approach by: (i) developing a reproducible framework that uses cutting-edge open-source large language models to assess the current capabilities of AI and robotics in performing job-related tasks; (ii) formalizing and computing a measure of AI exposure by occupation,  the TEAI (Task Exposure to AI) index, and a measure of Task Replacement by AI, the TRAI index, both validated through a human user evaluation and compared with the state of the art. Our results show that the TEAI index is positively correlated with cognitive, problem-solving and management skills, while it is negatively correlated with social skills. Applying the index to the US,  we obtain that about one-third of US employment is highly exposed to AI, primarily in high-skill jobs, requiring a graduate or postgraduate level of education. We also find that AI exposure is positively associated with both employment and wage growth in 2003-2023, suggesting that AI has an overall positive effect on productivity.Considering specifically the TRAI index, we find that even in high-skill occupations, AI exhibits high variability in task substitution, suggesting that AI and humans complement each other within the same occupation, while the allocation of tasks within occupations is likely to change.         All results, models, and code are freely available  online to allow the community to reproduce our  results, compare outcomes, using our work as a benchmark to monitor AI's progress over time.

Keywords: AI, Large Language models, employment, skills.