Notes on a World with Generative AI

Nikos Askitas

No 18070, IZA Discussion Papers from Institute of Labor Economics (IZA)

Abstract: 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.

Keywords: automation and outsourcing; technological change; labor economics; autoregressive models; Large Language Models; Generative Artificial Intelligence; human-machine collaboration knowledge work; epistemic norms; digital transformation (search for similar items in EconPapers)
JEL-codes: D83 J22 J24 J44 L86 O31 O33 O38 (search for similar items in EconPapers)
Date: 2025-08
New Economics Papers: this item is included in nep-ain
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Working Paper: Notes on a World with Generative AI (2025)
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