[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-308784-105":59,"doc-detail-308784-en":131},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":124,"head_meta":126,"extra_data":128,"updated_unix":130},105,"en","gpts-are-gpts-an-early-look-at-the-labor-market-impact-potential-of-large-language-models","GPTs are GPTs - An Early Look at the Labor Market Impact Potential of Large Language Models","","Investigates how large language models (LLMs), including GPT-style systems, may affect the U.S. labor market. The study uses a new rubric to estimate task exposure to LLM capabilities by combining human expertise with GPT-4-based classifications of occupations from O*NET. Results indicate about 80% of workers could see at least 10% of tasks affected, and roughly 19% could see at least 50% affected. Impacts likely extend across wage levels and beyond high-productivity-growth industries, with software built on LLMs amplifying the share of tasks completed faster at similar quality.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/gpts-are-gpts-an-early-look-at-the-labor-market-impact-potential-of-large-language-models/308784/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/gpts-are-gpts-an-early-look-at-the-labor-market-impact-potential-of-large-language-models/308784.png","ImageObject",300,407,{"name":92,"@type":93},"Olivia Brown","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-23","2026-09-20",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",12,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What does the paper analyze about large language models and jobs?","Question",{"text":113,"@type":114},"It assesses the potential implications of LLM-powered capabilities for the U.S. labor market by estimating how much workers’ tasks could be exposed to LLM capabilities and LLM-powered software.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"How is task exposure measured in the study?",{"text":118,"@type":114},"The paper introduces a new rubric and applies it to U.S. occupational data, using both human annotations and GPT-4 classifications aligned to task-level labels.",{"name":120,"@type":111,"acceptedAnswer":121},"What are the main quantitative findings about workforce task impact?",{"text":122,"@type":114},"Approximately 80% of the U.S. workforce may have at least 10% of tasks affected by LLMs, while about 19% of workers may see at least 50% of tasks affected. With LLM-powered software and tooling, the share of tasks potentially completed significantly faster at the same quality increases to between 47% and 56%.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},308784,1790139397,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":140,"language":141,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":142,"faqs":143,"seo_title":144,"seo_description":67,"update_tm":145,"read_time":146,"preview_page_count":4},16904993612988,"https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd","arXiv :2303 . 10130v5 [ econ .GN] 21 Aug 2023  \nGPTs are GPTs: An Early Look at the Labor Market Impact Potential  \nof Large Language Models  \nTyna Eloundou 1 , Sam Manning 1,2 , Pamela Mishkin∗1, and Daniel Rock3  \n1 OpenAI  \n2 OpenResearch  \n3University of Pennsylvania  \nAugust 22, 2023  \nAbstract  \nWe investigate the potential implications of large language models (LLMs), such as Generative Pretrained 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.  \n1 Introduction  \nAs shown in Figure 1, recent years, months, and weeks have seen remarkable progress in the field of generative AI and large language models (LLMs) . While the public often associates LLMs with various iterations of the Generative Pre-trained Transformer (GPT), LLMs can be trained using a range of architectures, and are not limited to transformer-based models (Devlin et al., 2019) . LLMs can process and produce various forms of sequential data, including assembly language, protein sequences and chess games, extending beyond natural language applications alone. In this paper, we use LLMs and GPTs somewhat interchangeably, and specify in our rubric that these should be considered similar to the GPT-family of models available via ChatGPT orthe OpenAI Playground (which at the time of labeling included models in the GPT-3.5 family but not in the GPT-4 family) . We examine LLMs with text-and code-generating abilities, use the term \"generative AI\" to additionally include modalities such as images or audio, and use \"LLM-powered software\" to cover tools built on top of LLMs or that combine LLMs with other generative AI models.  \n∗ Corresponding author ([pamela@openai.com](pamela@openai.com)) . Authors contributed equally and are listed alphabetically.  \nFigure 1: Taken directly from GPT-4 Technical Report (OpenAI, 2023b) . To get a sense of how quickly model capabilities are progressing – consider the jump in exam performance between GPT-3.5 and GPT-4 (OpenAI, 2023b) .  \nOur study is motivated less by the progress of these models alone though, and more by the breadth, scale, and capabilities we’ve seen in the complementary technologies developed around them. The role of complementary technologies remains to be seen, but maximizing the impact of LLMs appears contingent on integrating them with larger systems (Bresnahan, 2019; Agrawal et al., 2021) . While the focus of our discussion is primarily on the generative capabilities of LLMs, it is important to note that these models can also be utilized for various tasks beyond text generation. For example, embeddings from LLMs can be used for custom search a","cbCaiocg6ToL5f2C","https://ap.wps.com/l/cbCaiocg6ToL5f2C","pdf",2119311,36,"English","# Abstract\n# 1 Introduction\n## Research motivation and scope\n## Rubric for assessing exposure to LLM capabilities\n## Exposure measurement method and dataset construction","[{\"question\":\"What does the paper analyze about large language models and jobs?\",\"answer\":\"It assesses the potential implications of LLM-powered capabilities for the U.S. labor market by estimating how much workers’ tasks could be exposed to LLM capabilities and LLM-powered software.\"},{\"question\":\"How is task exposure measured in the study?\",\"answer\":\"The paper introduces a new rubric and applies it to U.S. occupational data, using both human annotations and GPT-4 classifications aligned to task-level labels.\"},{\"question\":\"What are the main quantitative findings about workforce task impact?\",\"answer\":\"Approximately 80% of the U.S. workforce may have at least 10% of tasks affected by LLMs, while about 19% of workers may see at least 50% of tasks affected. With LLM-powered software and tooling, the share of tasks potentially completed significantly faster at the same quality increases to between 47% and 56%.\"}]","GPTs are GPTs - An Early Look at the Labor Market Impact Potential of Large Language Models | PDF",1789872332,91]