For Work / Against Work
Debates on the centrality of work

"The wrong kind of AI? Artificial intelligence and the future of labour demand"

by Acemoglu, Daron; Restrepo, Pascual (2019)


Artificial intelligence (AI) is set to influence every aspect of our lives, not least the way production is organised. AI, as a technology platform, can automate tasks previously performed by labour or create new tasks and activities in which humans can be productively employed. Recent technological change has been biased towards automation, with insufficient focus on creating new tasks where labour can be productively employed. The consequences of this choice have been stagnating labour demand, declining labour share in national income, rising inequality and lowering productivity growth. The current tendency is to develop AI in the direction of further automation, but this might mean missing out on the promise of the ‘right’ kind of AI, with better economic and social outcomes.

Key Passage

Most AI researchers and economists studying its consequences view it as a way of automating yet more tasks. No doubt, AI has this capability, and most of its applications to date have been of this mould—for example, image recognition, speech recognition, translation, accounting, recommendation systems and customer support. But we do not need to accept this as the primary way that AI can be and indeed ought to be used. [...] Since AI is not just a narrow set of technologies with specific, pre-determined applications and  functionalities but a technology platform, it can be deployed for much more than automation; it can be used to restructure the production process in a way that creates many new, high-productivity tasks for labour. If this type of “reinstating AI” is a possibility, there would be potentially large societal gains both in terms of improved productivity and greater labour demand (which will not only create more inclusive growth but also avoid the social problems spawned by joblessness and wage declines). (p.29)


Artificial Intelligence, Machine Learning, Inequality, Automation, Technology, Labor Demand



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