Towards the Terminator Economy: Assessing Job Exposure to AI through LLMs

TL;DR

This paper introduces a data-driven framework using open-source large language models to assess job exposure to AI, developing the TEAI and TRAI indices to measure AI's impact on tasks and employment across the US.

Contribution

It presents a novel, reproducible methodology for quantifying AI exposure in occupations, validated through human evaluation and applied to real-world employment data.

Findings

Approximately one-third of US jobs are highly exposed to AI.

AI exposure correlates positively with employment and wage growth.

High variability in task substitution suggests AI and humans complement each other.

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 Task Exposure to AI (TEAI) index, and a measure of Task Replacement by AI (TRAI), 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, and use our work as a benchmark to monitor AI's progress over time.