Abstract
" The risk climate of modernity is thus unsettling for everyone: no one escapes. " —Anthony Giddens, 2013 Typically, models are designed to represent reality and to produce output. In this sense, artificial intelligence (AI) can be viewed as a model of human intelligence capabilities to learn, analyze, and generate new configurations of information. In this sense, AI " machines " have been generative since the inception of the AI discipline in the 1950s and we should not be surprised by what we are now seeing in the form of " generative AI " (gen AI) applications, but we are! The recent widespread appreciation of the generative aspects of AI applications is due to the ready availability (all that is needed is a connected browser on any device!) of such applications to the masses, ease of use, the increased speeds at which gen AI outputs can be produced, and the impressive usefulness of its novel output. Gen AI has achieved fast-food status on a consumer level and is rapidly being commoditized and woven into the socioeconomic fabric of human society. As we look to the future, strategic human enhancive AI architectures, for example , adaptive cognitive fit (ACF), have the potential to help unleash iterations of rapid and complex advancements that will be treated as hyper-value creation opportunities, and emerging latent risks could be underestimated (Samuel et al. 2022; Kasirzadeh 2025). We have a solemn responsibility to ensure the development of ACF and similar human-centered AI architectures, which will help nurture a society that supports mass-human ascendancy over AIs, as opposed to the converse (Kashyap et al. 2024). The field of AI and machine learning (ML) is rapidly evolving, with new and improved techniques emerging each year. The recent development of gen AI is a huge breakthrough in AI and ML. Gen AI can create, imitate, and produce content that is very similar to human-created content. Through algorithms and ML techniques, gen AI can be 1 WWW.JBDAI.ORG