The transition problem in AI involves understanding and facilitating the shift from a predominantly human-driven economy to an economy where AI and automation play a central role. This concept encompasses not just technological advancements and their integration into economic systems, but also the societal, ethical, and policy implications that arise as AI assumes greater roles in traditionally human-held jobs. The transition problem raises questions about labor markets, economic inequality, and the need for retraining and education to equip humans for roles that remain or are newly created. It is a multifaceted challenge that involves balancing innovation with socio-economic stability, ensuring that technological progress benefits society as a whole, while also mitigating potential disruptions that such a profound shift might cause.
The term first emerged in discussions around AI and economic futures in the early 2010s but gained significant traction and mainstream acknowledgment in the latter half of the decade, particularly as AI technologies began to demonstrate transformative potential in diverse industries.
Key contributors to the discourse on the transition problem include economists and AI ethicists like Erik Brynjolfsson and Andrew McAfee, who have provided influential analyses on the impacts of AI on economic structures and labor markets, as well as scholars focusing on technology policy and ethics.