Hacia un nuevo esquema de incentivos en el contexto de la inteligencia artificial

This paper examines the intersection of the artificial intelligence and theories of human motivation, focusing on implications for the future of work and economic incentive systems. We analyze how the rapid evolution of AI challenges traditional economic theories of motivation, which primarily rely on extrinsic incentives. Through a literature review and analysis of empirical studies, we demonstrate the growing importance of intrinsic motivation in an increasingly automated work environment. In this regard, we explore the crowding out effect, where extrinsic incentives can diminish intrinsic motivation, and its implications for designing incentive systems. In particular, we present two potential scenarios: a symbiosis between human work and AI, and a radical transformation that could make human work optional in many sectors. We conclude that it is necessary to re-evaluate and adapt economic models and incentive systems to reflect a more nuanced understanding of human motivation in the context of AI. This study highlights the importance of an interdisciplinary approach for the transition to a future where the relationship between humans, work and technology will have to be redefined.