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ILO Working Paper 96
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Introduction
Each new wave of technological progress intensies debates on automation and jobs. Current
debates on Articial Intelligence (AI) and jobs recall those of the early 1900s with the introduc-
tion of the moving assembly line, or even those of the 1950s and 1960s, which followed the intro-
duction of the early mainframe computers. While there have been some nods to the alienation
that technology can bring by standardizing and controlling work processes, in most cases, the
debates have centred on two opposing viewpoints: the optimists, who view new technology as
the means to relieve workers from the most arduous tasks, and the pessimists, who raise alarm
about the imminent threat to jobs and the risk of mass unemployment.
What has changed in debates on technology and workers, however, is the types of workers af-
fected. While the advances in technology in the early, mid and even late-1900s were primarily
focused on manual workers, technological development since the 2010s, in particular the rapid
progress of Machine Learning (ML), has centred on the ability of computers to perform non-rou-
tine, cognitive tasks, and by consequence potentially aect white-collar or knowledge workers.
In addition, these technological advancements have occurred in the context of much strong-
er interconnectedness of economies across the globe, leading to a potentially larger exposure
than location-based, factory-level applications. Yet despite these developments, to an average
worker, even in the most highly developed countries, the potential implications of AI have, until
recently, remained largely abstract.
The launch of ChatGPT marked an important advance in the public’s exposure to AI tools. In this
new wave of technological transformation, machine learning models have started to leave the
labs and begin interacting with the public, demonstrating their strengths and weaknesses in
daily use. The chat function dramatically shortened the distance between AI and the end user,
simultaneously providing a platform for a wide range of custom-made applications and inno-
vations. Given these signicant advancements, it is not surprising that concerns over potential
job loss have resurged.
While it is impossible to predict how generative AI will further develop, the current capabilities
and future potential of this technology are central to discussions of its impact on jobs. Sceptics
tend to believe that these machines are nothing more than “stochastic parrots” – powerful text
summarizers, incapable of “learning” and producing original content, with little future for gen-
eral purpose use and unsustainable computing costs (Bender et al. 2021). On the other hand,
more recent technical literature focused on testing the limits of the latest models suggests an
increasing capability to carry out “novel and dicult tasks that span mathematics, coding, vision,
medicine, law, psychology and more”, and a general ability to produce responses exhibiting some
forms of early “reasoning” (Bubeck et al. 2023). Some assessments go as far as suggesting that
machine learning models, especially those based on large neural networks used by Generative
Pre-trained Transformers (GPT, see Text Box 1), might have the potential to eventually become a
general-purpose technology (Goldfarb, Taska, and Teodoridis 2023; Eloundou et al. 2023).
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would have multiplier eects on the economy and labour markets, as new products and servic-
es would likely spring from this technological platform.
As social scientists, we are not in position to take sides in these technical debates. Instead, we
focus on the already demonstrated capabilities of GPT-4, including custom-made chatbots with
retrieval of private content (such as collections documents, e-mails and other material), natu-
ral language processing functions of content extraction, preparation of summaries, automated
content generation, semantic text searches and broader semantic analysis based on text em-
beddings. Large Language Models (LLMs) can also be combined with other ML models, such as
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The three main characteristics of general-purpose technologies are pervasiveness, ability to continue improving over time, and abil-
ity to spawn further innovation (Jovanovic and Rousseau, 2005).