Objective The adoption of digital technologies has historically impacted the most precarious occupations and contributed to widening labor market inequities. Large language models (LLM) may reshape this relationship. This study examines the association between occupational exposure to LLM and occupational precarity.

Methods Using Canada’s Labour Force Survey, occupational exposure to LLM and four dimensions of precarity (contractual instability, earnings inadequacy, schedule unpredictability, working-time mismatch) were examined. A multidimensional index was developed to summarize an occupation’s overall exposure to precarity. Four multivariate linear regression models with cluster-robust standard errors estimated the associations between LLM exposure and each dimension of precarity. A fifth multivariate model examined the relationship between LLM exposure and the multidimensional precarity index. Utilizing model coefficients, mean estimates of occupational LLM exposure were produced.

Results Using the multidimensional precarity index, our analysis showed that occupations characterized by low exposure to precarity had a significantly higher mean LLM exposure [mean 0.386, 95% confidence interval (CI) 0.356–0.417] compared to occupations with medium (mean 0.258, 95% CI 0.221–0.295), high (mean 0.260, 95% CI 0.194–0.328) or very high precarity (mean 0.205, 95% CI 0.136–0.275). Apart from earning adequacy, LLM exposure was also lower among occupations using each separate dimension of precarity.

Conclusion Occupations most likely to be exposed to LLM are those where precariousness is lowest. These occupations have previously been sheltered from technological change. There is a need of examine the impacts of LLM on workers in job where the technology is prominent.

Key terms AI; AI exposure; artificial intelligence; Canada; Canadian labor force; job quality; labor market inequity; large language model; LLM; precarious job; precarious occupation; precarious work; precarity; workplace

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