The Content Owner’s Disadvantage in AI

In every technological revolution, there are those who build the platforms and those who supply the raw material. History shows that the former tends to capture most of the value. The music industry in the Napster era was the first warning shot: distribution shifted overnight, and labels that had spent decades building catalogues found themselves disintermediated by file-sharing networks they didn’t control. Fast forward to the rise of social media, and publishers became dependent on algorithms that could redirect traffic with a line of code. In both cases, the creators and owners of content discovered, too late, that control of the pipes matters more than ownership of the library.

We are now in the same moment with generative AI. Content owners, from film studios to news organizations to independent creators, are handing over their assets to AI companies, often with little clarity on how those assets will be used, licensed, or monetized. The imbalance is stark. AI companies train models on vast quantities of text, images, and video, some scraped without consent, others acquired in opaque licensing deals. The resulting models can generate outputs at scale, displacing the very industries whose work made the training possible.

There’s an irony here. A film studio that spent millions creating an archive can find its work distilled into a model that generates convincing lookalike scenes in seconds. A publisher’s articles become the scaffolding for a chatbot that can answer questions without ever citing the source. A musician’s back catalogue helps train a system that can produce “AI Drake” on demand. In each case, the content owner supplied the fuel but lost the engine.

Why does this happen? Partly because technology companies move faster than the legal or commercial frameworks around them. The early internet was built on “permissionless innovation”, a euphemism for “ask for forgiveness, not permission.” Search engines indexed copyrighted pages, YouTube hosted unlicensed clips, and social networks-built businesses on user-uploaded content. Over time, courts, regulators, and licensing regimes caught up. But by then, the platform dynamics were entrenched.

The same pattern is repeating with AI. The default assumption, unless challenged, is that the big model companies will collect whatever data they can, train on it, and argue that fair use or legal ambiguity gives them cover. For content owners, this is the disadvantage: they risk becoming the raw material for someone else’s product, with little leverage, transparency, or upside.

So how should it work? The starting point is provenance. Models should know not just what they are trained on, but who created it, under what terms, and with what rights attached. That sounds obvious, but most of today’s datasets are a patchwork of scraped material, open data, and inconsistent metadata. Without provenance, creators can’t be compensated, regulators can’t enforce compliance, and companies can’t guarantee safety.

Second, licensing needs to be embedded into the infrastructure. Think of the way Spotify normalized music streaming: instead of piracy, labels got predictable royalties, and users got universal access. The business model didn’t just reduce friction, it rebalanced the relationship between creators and platforms. AI needs its Spotify moment for training data: a system where content owners opt in, set terms, and share in the value their assets generate.

Finally, cultural and contextual nuance matters. A model trained on Western datasets may misinterpret or erase local practices. A saree becomes just another “dress,” or a religious ritual is mistaken for generic ceremony. Content owners don’t just supply pixels; they supply meaning. Preserving that meaning is critical if AI is to reflect the richness of human culture rather than flatten it.

This is where companies like Clairva are trying to shift the narrative. Instead of scraping, we work directly with content owners to annotate, enrich, and license their video libraries. Instead of treating video as a fungible mass of frames, we add structure: who is in the scene, what cultural practice is represented, what emotions are expressed. That creates datasets that are not only legally defensible but also far more useful: models trained on structured, rights-cleared content perform better, because they actually understand what they see.

It is not glamorous work. Annotation and metadata don’t make headlines the way a new multimodal model does. But in the long run, this layer of trusted, contextualized data is what will make AI sustainable, for companies, for regulators, and for creators. Every innovation at Clairva, from provenance tagging to cultural annotation, is designed to give content owners leverage rather than extract it from them.

History tells us that platforms always try to take more than they give. But it also shows that when industries organize around standards, the balance can be restored. The lesson from music, publishing, and social media is clear: if content owners want to avoid becoming mere suppliers to someone else’s engine, they need infrastructure that enforces consent, context, and compensation.

Generative AI is still young. The frameworks we build now will decide whether creators are sidelined or empowered in the next decade. At Clairva, our bet is simple: the future of AI should be built with content owners, not at their expense.