With generative AI-based tools gaining momentum, software engineers must consider how future systems will be designed, deployed, and maintained with these tools taking on a greater role. The analysis of developer data to date reveals that although generative AI tools offer numerous advantages, they will necessitate intentional collaboration with human experts. Currently, these tools operate within a constrained context window and provide recommendations within a limited scope. Nevertheless, these recommendations carry implications for the broader structure and behavior of systems, specifically in terms of how the system is architected. Several questions remain unanswered: How can relevant changes be uniformly applied throughout the code base? How can developers identify changes that shouldn’t be propagated? How can tools and humans ensure that similar looking but distinct changes aren’t mistakenly propagated? What are the implications of these changes for the overall end-to-end system quality? Are these questions even the most relevant ones to ask? Thus far, there has been limited focus on exploring the impact of generative AI tools on software architecture workflows and resulting system design and quality. While the success of these tools offers exciting opportunities, I argue that it also places significant responsibility on the shoulders of software architects. In this rapidly evolving landscape of software engineering, the rise of generative AI tools challenges us to reconsider not only how we build systems, but also our fundamental assumptions about software architects’ roles and responsibilities, the architecting process, its essential workflows, as well as the inputs and outputs of this process. In this talk, I will encourage software architecture researchers and practitioners alike to embrace the responsibility of understanding and challenging the foundations of software architecture principles and practices to catalyze purposeful and constructive transformation!