Study reveals 80% of the U.S. workforce may see at least 10% of their tasks affected by LLMs, and higher-wage jobs face greater exposure
- Around 80% of the U.S. workforce may see at least 10% of their work tasks affected by the introduction of Large Language Models (LLMs) like GPTs.
- Higher-income jobs potentially face greater exposure to LLM capabilities and LLM-powered software.
- The impact of LLMs on the U.S. economy spans various industries and is not limited to those with higher recent productivity growth.
A recent study investigates the potential implications of Large Language Models (LLMs), such as Generative Pre-trained Transformers (GPTs), on the U.S. labor market. It focuses on the increased capabilities arising from LLM-powered software compared to LLMs on their own. The 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.
The projected effects span all wage levels, with higher-income jobs potentially facing greater exposure to LLM capabilities and LLM-powered software. Notably, these impacts are not restricted to industries with higher recent productivity growth. The analysis suggests that with access to an LLM, about 15% of all worker tasks in the U.S. 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.
The study concludes that LLMs such as GPTs exhibit traits of general-purpose technologies, indicating that they could have considerable economic, social, and policy implications. The research also acknowledges limitations, such as the focus on the United States, which restricts the generalizability of the findings to other nations where the adoption and impact of generative models may differ. The authors recommend further research to explore the broader implications of LLM advancements, including their potential to augment or displace human labor, their impact on job quality, impacts on inequality, skill development, and other outcomes.
By seeking to understand the capabilities and potential effects of LLMs on the workforce, policymakers and stakeholders can make more informed decisions to navigate the complex landscape of AI and its role in shaping the future of work.