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We investigate the potential implications of large language models (LLMs), such as Generative Pre-trained Transformers (GPTs), on the U.S. labor market, focusing on the increased capabilities arising from LLM-powered software compared to LLMs on their own. Using a new rubric, we assess occupations based on their alignment with LLM capabilities, integrating both human expertise and GPT-4 classifications. Our findings reveal that around 80% of the U.S. workforce could have at least 10% of their work tasks affected by the introduction of LLMs, while approximately 19% of workers may see at least 50% of their tasks impacted. We do not make predictions about the development or adoption timeline of such LLMs. The projected effects span all wage levels, with higher-income jobs potentially facing greater exposure to LLM capabilities and LLM-powered software. Significantly, these impacts are not restricted to industries with higher recent productivity growth. Our analysis suggests that, with access to an LLM, about 15% of all worker tasks in the US could be completed significantly faster at the same level of quality. When incorporating software and tooling built on top of LLMs, this share increases to between 47 and 56% of all tasks. This finding implies that LLM-powered software will have a substantial effect on scaling the economic impacts of the underlying models. We conclude that LLMs such as GPTs exhibit traits of general-purpose technologies, indicating that they could have considerable economic, social, and policy implications.

Supplemental Material for Characterising variability in youth mental health service populations: A detailed and scalable approach using digital technology by William Capon, Ian B Hickie, Sarah McKenna, Mathew Varidel, Matthew Richards, Haley M LaMonica, Daniel Rock, Elizabeth M Scott and Frank Iorfino in Australasian Psychiatry

Supplemental Material, Supplement_material for Patterns of Mental Health Care in Remote Areas: Kimberley (Australia), Nunavik (Canada), and Lapland (Finland): Modèles de soins de santé mentale dans les régions éloignées: Kimberley (Australie), Nunavik (Canada) et Laponie (Finlande) by Jose A. Salinas-Perez, Mencia R. Gutierrez-Colosia, Mary Anne Furst, Petra Suontausta, Jacques Bertrand, Nerea Almeda PhD, John Mendoza, Daniel Rock, Minna Sadeniemi, Graça Cardoso and Luis Salvador-Carulla in The Canadian Journal of Psychiatry

Supplemental material, Supplementary_material for Service availability and capacity in rural mental health in Australia: Analysing gaps using an Integrated Mental Health Atlas by Bregje A van Spijker, Jose A Salinas-Perez, John Mendoza, Tanya Bell, Nasser Bagheri, Mary Anne Furst, Julia Reynolds, Daniel Rock, Andrew Harvey, Alan Rosen and Luis Salvador-Carulla in Australian & New Zealand Journal of Psychiatry

The Archaeological Journal, 26, 35-51

The Archaeological Journal, 20, 355-366

The Archaeological Journal, 24, 226-228

A novel phenotyping strategy in schizophrenia, targeting different neurocognitive domains, neurobehavioral features, and selected personality traits, has allowed us to identify a homogeneous familial subtype of the disease, characterized by pervasive neurocognitive deficit. Our genome scan data indicate that this subtype, which accounts for up to 50% of our sample, has a distinct genetic basis and explains linkage to chromosome 6p24 reported previously. If representative of other populations, the ratio of schizophrenia subtypes observed in our families could have a profound impact on sample heterogeneity and on the power of genetic studies to detect linkage and association. Our proposed abbreviated battery of tests should facilitate phenotype characterization for future genetic analyses and allow a focus on a crisply defined schizophrenia subtype, thus promoting a more informed search for susceptibility genes.