gpts-are-gpts-856fb5f2·2 events·first seen Aliases: GPTs are GPTs
A new arXiv paper critically examines the 'GPTs are GPTs' occupational exposure scores (Eloundou et al., 2023), which have become a dominant empirical input to future-of-work policy debates. The authors identify two compounding gaps: structural limitations of static exposure scores (temporal, geographic, ontological) versus what policy questions actually require, and a coordination failure between researchers and policymakers who continue citing outdated measures. The paper surveys five families of methodological responses and argues that closing the research-policy gap requires participatory methods, better data infrastructure, and a shift from prediction to preparedness.
OpenAI published research examining the potential labor market impacts of large language models, analyzing which occupations and tasks are most exposed to automation or augmentation by GPT-class models. The study introduces a framework for assessing LLM 'exposure' across job categories, finding that a significant share of U.S. workers could see at least 50% of their tasks affected. The paper represents an early systematic attempt to quantify economic disruption potential from frontier AI systems.