Generative vs. Non-Generative AI: Analyzing the Effects of AI on the Architectural Design Process

Abstract

The rapid advancements in artificial intelligence (AI) and machine learning (ML) have led to numerous practical applications across various fields. Architects and researchers have also begun exploring the potential of applying AI & ML to enhance their work. The advent of generative AI led to new opportunities for the architects who started using image generation models to aid in the concept phase as well as visualizing projects, plans generation, etc. Moreover, more experiments were done with non-generative AI in planning, predicting materials, and classification to serve architectural design and analysis purposes. However, existing applications often fail to provide precise and readily usable architectural models within the standard design software used by architects. Moreover, architects started to rely on generative (gen) AI models to generate designs in the form of rendered photos and many experimentations with such tools are being done with little focus on the use of non-generative (non-gen) AI models. In this paper, we analyze the mechanism and technology behind different gen-AI models as well as the product of these models to give insights on the authenticity of these products and the effects of applying such technologies on the architectural design process. This analytical study is supported by reviewing different applications from researchers and architects regarding both types of algorithms. The research concludes with a strong suggestion to rely more on non-gen AI models which aid in a more human-centered design approach. The findings also suggest that gen-AI models could affect the design process negatively, especially if the design concept is purely generated using text or even undetailed photos. And finally, possible applications of both gen and non-gen AI models are suggested as a result.