In Volume 4, Issue 2, our journal published the compelling and provocative article, A Multimodal Grammar of Artificial Intelligence: Measuring the Gains and Losses in Generative AI, by Bill Cope and Mary Kalantzis (https://doi.org/10.1177/26349795231221699). This insightful piece interrogated the scope of Generative AI through the lens of multimodal grammar, raising fundamental questions about meaning-making and learning. In the same issue, we invited two follow-up articles from leading scholars in the field to offer their reflections on Cope & Kalantzis’ article. James Paul Gee and Qing Archer Zhang offered Generative AI and Humans (https://doi.org/10.1177/26349795241230966), a thoughtful examination of human–machine relationship, while Angel M. Y. Lin and Qinghua Chen, in How Intelligent is Generative AI? Towards Trans-semiotising the Turing Test (https://doi.org/10.1177/26349795241241315), reflected on how we might evaluate AI’s capabilities. In the current volume, this scholarly dialogue continues with John Bateman’s incisive reflection, The Intrinsic Multimodality of Generative AI, followed by Cope and Kalantzis’s own response to the three reflections, Generative Transpositions: The (Anti-)Grammar of Text-Semantic, CyberSocial Intelligence. Together, these contributions exemplify the vibrancy and depth of sustained academic debate—one that unfolds not as fixed and immutable positions, but as an evolving conversation shaped by critical engagement and intellectual generosity. As editors, we have been privileged to witness the ‘behind-the-scenes’ of this exchange: a collegial yet rigorous process of probing, refining, and expanding ideas. We are grateful to Bill Cope and Mary Kalantzis for their intellectual leadership, and to all the contributors whose insightful perspectives illuminate the possibilities and challenges of generative AI and multimodality. We hope these articles will be the beginning of further exchanges of ideas and collaboration as we collectively chart the path forward in understanding and harnessing Generative AI through multimodality research. The Editors.