Generative Ai : The Revolution Was Prevented[IA Générative : La Révolution était empêchée]

Why did Generative Artificial Intelligence (GAI) only emerge in the public eye in 2022, even though it is based on concepts formulated as early as the 1950s? The answer lies less in a sudden breakthrough than in the gradual removal of longstanding technical barriers—obstacles identified long ago by AI researchers (Jordan & Mitchell, 2015; Brynjolfsson & McAfee, 2015). The recent history of AI has been marked by several "AI winters"—periods of disillusionment and underinvestment in the field, triggered by the technology's failure to live up to its promises (Crevier, 1993; Hendler, 2008). These successive stagnations highlighted the limitations of a still-constrained system: insufficient data, expensive storage, limited computing power, and poor connectivity. These bottlenecks, both technical and economic, long hindered the maturation of AI. Over the past decade, these barriers have gradually fallen. The volume of available data has exploded thanks to open data initiatives and connected devices, among others (Manyika et al., 2011); storage costs have plummeted (McCallum, 2023); GPUs/TPUs have dramatically increased computing power (Jouppi et al., 2017); and cloud computing has made this power widely accessible (Armbrust et al., 2010). These conditions have enabled the emergence of tools such as ChatGPT and Mistral.ai (Bommasani et al., 2022; Dwivedi et al., 2023), which are now transforming professional practices. This article offers an interpretation of this transition: understanding the former barriers, analyzing how they were lifted, and anticipating the concrete implications for businesses and their managers.