Creators vs AI: The battle for cultural ownership

The market for training data is booming, with significant sums being paid for curated video and creative assets.

By Sabari Raju 

The internet gave creators infinite distribution. It also made them infinitely scrapable. This is the fundamental tension of the AI age. For artists, filmmakers, musicians, and influencers, the visibility required to build an audience comes at a cost: every shared post, video, song, or illustration becomes fuel for training the next generation of AI systems. In a world where machines learn by consuming what’s online, visibility turns into liability.

To thrive in the modern creator economy, one must publish widely. But in doing so, creators are also teaching the very systems that could replace them. This is not a future threat—it is already unfolding. The market for training data is booming, with significant sums being paid for curated video and creative assets. Yet much of what is most valuable was never licensed. It was scraped. And the creators behind it were neither asked nor compensated.

AI companies require vast volumes of training material, and the easiest source is the open internet. Publicly shared media—text, images, video, audio—is routinely extracted and used to fine-tune AI models. What emerges is a new kind of supply chain, not built on chips or code, but on human culture. And, like many early supply chains, it is extractive by design.

The process is straightforward. Creators make original content. They publish it online to gain traction. AI systems ingest that content as training data. Eventually, those same systems generate imitations—faster, cheaper, and often without credit to the source. In traditional industries, this might be called vertical integration. In tech, it’s disruption.

As awareness of this pattern grows, resistance is building. New approaches are emerging to address compensation. Platform-level funds are being formed to remunerate contributors whose work is used in training datasets. Licensing mechanisms are appearing, offering content creators formal ways to opt in and receive payment. Some experiments explore pay-per-use attribution, where users are more receptive to AI-generated work if the original creator is acknowledged. Others propose community-owned models, where creators directly upload and license their data under their own governance.

Yet these solutions all share a common limitation: they operate retroactively. None address the foundational asymmetry—that AI systems are built with data they did not originally earn permission to use. The tools are valuable, but the raw material was collected without negotiation.

From a creator’s perspective, this feels exploitative. From an AI developer’s view, it may fall under fair use. Legally, the situation remains unresolved. Strategically, it’s shaky ground. What’s becoming clearer, however, is that creators are realising their true value lies not only in polished products, but in the cultural DNA embedded in their work. And that culture is not evenly distributed.

The most distinctive content—the regional accents, the ritual expressions, the visual styles born from specific places and people—will be the most valuable to future models. It is not just about art. It is about representation. And those who create it are the first to be mimicked, and often, the first to be sidelined.

What comes next is not likely to be fixed through regulation alone. The real leverage lies in strategy. And that means deals. Expect new types of licensing agents to emerge—entities that represent creators’ rights the way collective management organisations once did for music. These intermediaries will broker access to video, memes, dialects, and more, optimised not for public viewership, but for machine comprehension.

We will also see the emergence of cultural premium pricing. As AI seeks to become more global, the demand for underrepresented data will grow. Content from historically overlooked communities won’t just be culturally important—it will be technically essential for reducing model bias and improving contextual accuracy.

Finally, expect creators to reclaim visibility as leverage. Watermarking, fingerprinting, or inclusion in decentralised, structured repositories will become more common. If a machine wants to train on original human work, it will need to pay for access—not just to the file, but to the permission.

For creators, this may feel like a bittersweet moment. The tools you helped train might one day outperform you. But that same reality gives you leverage. You are now part of the AI supply chain. That means you can negotiate your role in it—if you recognise your value and act accordingly.

For AI companies, the existential threat isn’t litigation. It’s a shrinking pool of credible, diverse training data. Synthetic content can supplement real data, but it can’t replace it entirely. At some point, training on your own outputs leads to degradation. To build smarter systems, you still need the real thing. And that means humans.

This is not just a technical arms race. It is a negotiation over culture, ownership, and agency. And it will be shaped by whoever understands the strategic trade first—and defines the new terms of engagement.

(Sabari Raju, Co-Founder and Chief Technology Officer of Clairva.ai.)

Views are personal, and do not represent the stance of this publication.

https://www.linkedin.com/in/sabariraju/