Enrique Dans examines how the rapid adoption of artificial intelligence, particularly since the launch of ChatGPT, challenges traditional models of innovation diffusion like those proposed by Everett Rogers and Frank Bass.

While these models describe adoption as following a predictable curve with distinct user groups, Dans argues that AI's low cost, easy access, and viral social influence are compressing these phases, leading to a much faster, almost simultaneous adoption for some users.

He suggests that while the structure of diffusion may remain, the speed necessitates a reevaluation of existing frameworks and the incorporation of new metrics like social interactions and integrated machine learning.

The ensuing comments from readers discuss practical applications of AI, the potential for AI responses to become less creative due to training data limitations, and differing perspectives on user passivity in the face of these new technologies.

You can also read this article in English on my Medium page, «The great AI acceleration: rewriting the rules of innovation«