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

TL;DR

This study develops a reproducible, task-level framework to quantify AI exposure and task substitutability across US occupations by leveraging open-source LLMs and the O*NET taxonomy. It introduces two indices, TEAI (exposure) and TRAI (substitutability), computed through multi-model consensus and a weighted aggregation of tasks, with human evaluation validating interpretation. Empirical results show that about one-third of US employment is highly exposed to AI, with exposure clustering in high-skill areas and a positive link to employment and wage growth in recent decades; TRAI reveals substantial variability in substitution within occupations, suggesting complementarity alongside evolving task allocation. All data, code, and interactive demonstrations are provided to enable replication and ongoing monitoring of AI progress and its labor-market implications.

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.

Figures (8)

• Figure 1: Graphical overview of the framework
• Figure 2: Correlation with existing exposure indexes. Each dot represents a SOC occupation.
• Figure 3: Correlation with different skill intensity measures.
• Figure 4: Figures \ref{['fig:dist_te_emp_soc_sec']} and \ref{['fig:dist_te_emp_s']} display the distribution of exposure to the TEAI index across SOC groups and skill intensity levels, respectively, using U.S. BLS employment data (in millions of workers). For both panels, each bar represents a tertile of the TEAI score distribution.
• Figure 5: AI exposure by workers' characteristics.