Born in Bezuidenhout Valley, Johannesburg, Adilson De Oliveira is an emerging multi-disciplinary artist, self-taught animator, and cultural practitioner with a Bachelor of Fine Arts from the University of the Witwatersrand. Raised by a mother who worked as a high school history teacher, De Oliveira's upbringing was steeped in contested narratives and the politics of memory—elements that continue to shape the core of his artistic inquiry. He approaches his work as a method of cannibalising history, not in the literal sense, but as a means of consuming, digesting, and reconfiguring complex and often uncomfortable pasts into new visual vocabularies.
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De Oliveira began his career as a printmaker, producing screen-prints for a range of established artists across the Southern African region. Over time, he grew disillusioned with the role of technician, prompting a deeper engagement with the possibilities of the machine as both collaborator and emancipator. This marked a pivotal shift in his practice—away from the service of others and toward a mode of creative subversion.
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Since co-founding The Magolide Collective in 2019, De Oliveira has cultivated an artistic methodology rooted in deception, satire, and speculative technologies. His work critiques the authoritative tone of the so-called ‘Western Art Canon’, aiming to erode the boundaries between digital and physical, truth and fabrication. By utilising mediums such as drawing, painting, video, printmaking, and XR (Extended Reality), he positions his practice as an insurgent tool of decoloniality.
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Through this kaleidoscopic lens, De Oliveira explores the vibrant Lusophone enclaves of Johannesburg—mining the myths, contradictions, and inherited traumas of Luso-African identity. His work operates as both an act of resistance and a living archive, capturing the socio-political entanglements that define postcolonial urban life in South Africa.
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De Oliveira is currently a creative partner with Runway AI, an innovative platform for AI-assisted video creation and editing. His current research focuses on the potential of machine learning to generate digital works that defy easy categorisation—blurring the line between human intention and algorithmic chance.
