Based on notes from the AFTRS Digital Future Summit 2026: Ancestral to Artificial Intelligence, featuring Lisa Russell (Kenya), Tory Stephens (USA), and Jake Duczynski (AUS), moderated by Pauline Clague.

From Creative Work to Training Data: Ownership and Control

The shift from artwork to data has intensified these tensions. Creative outputs are scraped, indexed and fed into models that are then commercialised by large corporations. Artists are increasingly questioning why they must pay to access tools built from their own labour. The issue extends beyond intellectual property to questions of data sovereignty, authorship and custodianship.

For Indigenous communities, the stakes are particularly high. Collective knowledge systems risk being flattened into homogenised datasets that strip away nuance and context. Concerns are growing that AI may accelerate cultural loss by detaching stories from the custodial structures that give them meaning.

Economic pressures compound the problem. Job displacement is already visible in design, animation, advertising and post‑production. Many creatives feel compelled to adopt AI tools simply to remain competitive, while others face criticism from peers for engaging with the technology at all. The result is a fractured creative community grappling with rapid technological change and limited structural support.

Environmental impacts add further complexity. Large‑scale data centres, often located in regional or low‑income areas, consume significant water and energy. Communities report noise, pollution and inadequate regulation. While sustainable practice is a growing priority in the arts, most creatives lack the resources to meaningfully address their own carbon footprints, let alone influence the environmental footprint of global AI infrastructure.

Despite these challenges, alternative pathways are emerging. Cooperative models and locally trained AI systems are being explored to redistribute power. They also aim to ensure creative workers have a stake in the technologies shaping their industries. Some practitioners are experimenting with protective measures to assert control over their work.

There is also increasing interest in using AI to preserve endangered languages, document artisan knowledge and support ageing practitioners whose skills risk disappearing. These efforts show how AI could strengthen cultural continuity, but only if governance protects the integrity of stories and the communities they come from.

The Need for AI Regulation

Policy, however, remains a weak point. Without stronger regulation, transparent governance and meaningful artist representation, the gap between creators and corporations is likely to widen. Many in the sector argue that the goal should be to build the AI systems society needs, rather than simply accepting the systems currently offered by industry.

The creative community is calling for time and resources to experiment, develop frameworks and design protocols that respect diverse knowledge systems. At present, that responsibility falls disproportionately on individual artists and small organisations — the groups least equipped to shoulder it.

AI is already reshaping creative work. The question now asks who will shape AI in return, and whether creators define the future of cultural production through extraction or through more equitable models.