Techno-positivist narratives of efficiency, productivity, and “democratised” creativity risk obscuring the material, situated realities of creative work; narrowing how HCI can imagine and enable alternative futures. This pictorial presents four futures workshops with seventy creative professionals that surfaced nuanced visions of creative practice with AI in 2030 through structured exploration of three temporal forces: the weight of history, the push of the present, and the pull of the future. We contribute to HCI and DIS by offering a visually-documented framework for futures workshops with creative communities, extending futures-oriented design methods with a structured, dialogic approach that makes ambivalent attitudes toward technology visible rather than reducing them to simple polarities. By translating these discussions into visual vignettes, created in collaboration with an illustrator, we show how pictorials can surface and compare nuances across cohorts, making the work accessible to researchers, educators, and the creative communities that this research is intended to support.

While generative AI promises to democratize creativity, we lack empirical understanding of how creative professionals begin using these systems. Through a 10-week longitudinal study following one artist’s self-directed exploration, we ask three case-bounded questions: what interaction patterns emerged? What dynamics characterized sustainable co-creation for this artist? How might we evaluate successful integration in this case? Our findings from this case study reveal: (1) physical environments and material practices drove digital interaction; (2) specific temporal patterns emerged (short prompt bursts, consolidation periods, multi-day resurfacing cycles) and (3) productive discomfort and sustained tension marked successful sessions. These patterns suggest alternative design spaces worth exploring. We contribute: (1) four empirically-grounded design patterns (park-and-resurface, warm-up modes, affect-to-action bridges, comfort controls) that could be prototyped and tested; and (2) speculative provocations that challenge efficiency-first paradigms. This case study demonstrates that some users may benefit from approaches orthogonal to current design paradigms.

This paper investigates the direct risks and harms associated with modern text-to-image generative models, such as DALL-E and Midjourney, through a comprehensive literature review. While these models offer unprecedented capabilities for generating images, their development and use introduce new types of risk that require careful consideration. Our review reveals significant knowledge gaps concerning the understanding and treatment of these risks despite some already being addressed. We offer a taxonomy of risks across six key stakeholder groups, inclusive of unexplored issues, and suggest future research directions. We identify 22 distinct risk types, spanning issues from data bias to malicious use. The investigation presented here is intended to enhance the ongoing discourse on responsible model development and deployment. By highlighting previously overlooked risks and gaps, it aims to shape subsequent research and governance initiatives, guiding them toward the responsible, secure, and ethically conscious evolution of text-to-image models.