The quality and meaning of work in the age of artificial intelligence.

For the economist Malo Mofakhami, the central question is not so much whether work will disappear — because, in his view, it will — but rather to understand “who will benefit from AI, and who will bear the consequences during the transition phases.” Compared with earlier historical reorganizations of labor linked to the automation of substitutable or complementary tasks, artificial intelligence introduces a major novelty: it now affects highly qualified occupations by automating tasks that had largely been spared by previous technological revolutions, particularly “decision-making and cognitive tasks.” For Mofakhami, this raises a core risk: “that workers may adapt themselves to the requirements of AI tools rather than the other way around, at the cost of intensified labor and a loss of meaning.”

What is therefore at stake is the quality of work and the meaning workers attribute to their activities. Examples from the manufacturing and logistics sectors show that AI does not merely replace certain physical tasks; it also imposes a more standardized and constraining pace of work. This phenomenon is especially visible among platform workers — such as ride-hailing drivers or delivery couriers — whose movements and daily organization are dictated by algorithms. In other words, workers lose autonomy, as their tasks and the manner in which they must perform them are increasingly structured by machines.

This dynamic now extends to so-called “intellectual” professions — journalism, finance, consulting, and others — which are exposed to a new form of cognitive labor automation. Knowledge-based occupations are witnessing growing fragmentation of practices and increasing flexibility, accompanied by stronger organizational pressures. This development calls for moving beyond the usual approach that merely counts jobs destroyed and created, in order to examine the qualitative transformation of working conditions. Put differently: what are the gains and losses produced by the introduction of AI into the organization of work, if one considers not only productivity but also interest, motivation, and job satisfaction?

This question is all the more crucial given that worker motivation — closely linked to the perceived value and interest of an activity — has long been recognized as a key driver of efficiency and productivity. Hence Mofakhami’s warning against reducing AI to a mere tool for task optimization and cost reduction without considering its effects on working conditions and employment. He therefore calls for viewing AI within a genuine sociotechnical approach to organizational transformation: an integrated perspective that combines technical, social, psychological, and human dimensions, ensuring that innovation does not come at the expense of workers.