Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/328200 
Autor:innen: 
Erscheinungsjahr: 
2025
Schriftenreihe/Nr.: 
IZA Discussion Papers No. 18070
Verlag: 
Institute of Labor Economics (IZA), Bonn
Zusammenfassung: 
Generative AI (GenAI) and Large Language Models (LLMs) are moving into domains once seen as uniquely human: reasoning, synthesis, abstraction, and rhetoric. Addressed to labor economists and informed readers, this paper clarifies what is truly new about LLMs, what is not, and why it matters. Using an analogy to auto-regressive models from economics, we explain their stochastic nature, whose fluency is often mistaken for agency. We place LLMs in the longer history of human–machine outsourcing, from digestion to cognition, and examine disruptive effects on white-collar labor, institutions, and epistemic norms. Risks emerge when synthetic content becomes both product and input, creating feedback loops that erode originality and reliability. Grounding the discussion in conceptual clarity over hype, we argue that while GenAI may substitute for some labor, statistical limits will, probably but not without major disruption, preserve a key role for human judgment. The question is not only how these tools are used, but which tasks we relinquish and how we reallocate expertise in a new division of cognitive labor.
Schlagwörter: 
Generative Artificial Intelligence
Large Language Models
autoregressive models
labor economics
technological change
automation and outsourcing
human-machine collaboration knowledge work
epistemic norms
digital transformation
JEL: 
J24
O33
O31
J22
D83
L86
J44
O38
Dokumentart: 
Working Paper

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