Despite its proven capability of being integrated into various phases, from ideation to execution, many still view generative design as somewhat of a speculative endeavor. In a survey conducted for its 2024 Artificial Intelligence (AI) Report, the Royal Institute of British Architects (RIBA) found that 58% of architects believe that AI increases the risk of their work being imitated and over a third view AI as a threat to the profession (RIBA 2024). In addition, although integrating AI enables predictions related to structural performance, energy efficiency, and environmental impact, it introduces challenges such as data security, ethical considerations, and the demand for extensive computational resources (Rane et al. 2023). Educating and informing the public about the ability of generative technologies to not only produce conceptual imagery at the beginning of the design process but also to assist in extracting insights from processed data, identifying bottlenecks, and streamlining construction details, are therefore essential components of a shift towards broader implementation of AI in mainstream practice, industry, and research. Within that context, the peer-reviewed articles in this issue offer a range of perspectives and demonstrate cases of current and future applications for generative workflows in the built environment. The articles fall roughly into four schemas—generative processes, hacking codes, new frontiers in mapping, and material explorations.
