The central confusion in the Generative AI and copyright debate stems from a category error: conflating protection of individual works with protection of creative roles or capacity. This distinction is crucial.
Copyright law protects discrete works – specific novels, songs, films. It addresses a particular economic problem: because creating is expensive but copying is cheap, without legal protection, someone could flood the market with knockoffs of that specific work, undercutting the creator’s ability to recoup investment. Copyright asks ‘can people access and consume your work without compensating you?’ This market-substitution concern has been copyright’s core justification since its inception. The threat is that unauthorized copies compete with the original in its primary market, preventing creators from earning back their investment in production.
Generative AI doesn’t compete work-for-work. It threatens to displace the human role in cultural production itself.
But AI’s challenge operates at an entirely different level. Generative AI doesn’t compete work-for-work. It threatens to displace the human role in cultural production itself. When an AI system can generate commercially acceptable images, the threat isn’t that it copies your specific photograph – it’s that people no longer need photographers at all. The displacement is categorical, not individual. The client who once hired you doesn’t switch to a cheaper copy of your work; they eliminate the need for human photographers entirely. This is fundamentally a labor market transformation, not a copyright problem.
This matters because copyright has no conceptual tools to address role displacement. Copyright controls markets for specific works.
This matters because copyright has no conceptual tools to address role displacement. Copyright controls markets for specific works. It cannot, and was never designed to, protect your capacity to perform a creative function in society. The illustrator whose clients now use Midjourney isn’t experiencing copyright infringement – her works aren’t being copied. She’s experiencing labor displacement. Copyright is simply the wrong legal domain to address this. Attempting to solve labor displacement through intellectual property expansion is a category mistake that mistakes the symptom for the disease.
Yet IP regimes have profoundly shaped how AI innovation has unfolded, creating what I call a ‘double enclosure’. First, platforms trained models on humanity’s collective cultural production –scraped from the open web, digitized books, and image databases. They extracted what we might call “meta-information”: aggregate patterns across millions of works. Not the works themselves, but the statistical regularities underlying creative production. This knowledge – how images compose, how language patterns, how melodies structure – has always been public commons. However, with AI models claiming proprietary protection over datasets incorporating these, through confidentiality or trade secret mechanisms, this commons, or rather its cumulation, is now proprietary infrastructure locked in corporate models.
Mandatory licensing proposals, like India’s DPIIT Working Paper on Generative AI and Copyright, claim to address the displacement concern through compensation. But compensation for what? If we’re honest, that training doesn’t infringe copyright – because it doesn’t compete with specific works but extracts non-expressive patterns – then licensing fees aren’t remedying harm. They’re creating artificial property rights over something copyright never covered: the aggregate knowledge embedded in cultural patterns. Copyright protects expression, not the statistical patterns extracted from analyzing millions of expressions. Extending copyright to cover this fundamentally alters its scope and purpose.
If we’re honest, that training doesn’t infringe copyright – because it doesn’t compete with specific works but extracts non-expressive patterns – then licensing fees aren’t remedying harm. They’re creating artificial property rights over something copyright never covered: the aggregate knowledge embedded in cultural patterns.
Licensing fee is not remedying the displacement problem either – imagine you get a minimal fee for use of your work in the global corpus of training, once trained, the model would still outcompete you, and no meagre compensation could remedy that. A photographer receiving a few dollars for her image being in a training dataset doesn’t help when AI systems eliminate the market for human photographers. The modest per-creator licensing fee cannot substitute for the loss of an entire vocation. This is treating a systemic transformation of labor markets as if it were a transactional licensing problem.
The human enablement framework I’ve developed reveals why this approach fails. If the genuine concern is preserving human creative capacity – the ability for humans to make a living through creative expression – then copyright expansion cannot help. You cannot protect someone’s role in cultural production by giving them property rights over individual works when the entire category of creative labor faces displacement. Copyright’s tools – exclusive rights over discrete works, market-based licensing, proprietary control – are structurally incapable of addressing the obsolescence of entire creative roles.
Copyright’s tools – exclusive rights over discrete works, market-based licensing, proprietary control – are structurally incapable of addressing the obsolescence of entire creative roles.
What would actually address role displacement? The tools that have always addressed technological labor disruption: direct income support for displaced workers, public investment in creative capacity building, social insurance systems, and protected spaces where human creativity can flourish regardless of market competition. These operate outside market logics. They don’t pretend that a small licensing fee per work can substitute for the loss of an entire vocation. They recognize that when market mechanisms fail to sustain human creative practice, we need non-market mechanisms of social provision and support.
How do we organize society when “work”– capitalism’s primary tool for distributing resources and social participation – no longer functions as it has for centuries?
But here’s the deeper problem: the entire IP debate is a distraction. By framing AI’s impact as copyright, we avoid fundamental questions: What happens when algorithmic production replaces entire categories of human labor? How do we organize society when “work”– capitalism’s primary tool for distributing resources and social participation – no longer functions as it has for centuries? When human labor becomes economically superfluous in domain after domain, can market mechanisms remain our primary method of resource distribution? These are questions about the future of labor, the social contract, and the organization of economic life itself.
IP debates, here, simply reduce a civilizational challenge to transactions: who owns what, who pays whom. Meanwhile, the actual transformation proceeds unaddressed. We tinker with licensing schemes while avoiding the reckoning that AI demands: whether our existing structures of work, compensation, and social provision can survive when human labor loses its economic necessity. Copyright expansion offers the comforting illusion that we can address AI’s disruption through familiar legal tools, avoiding the harder work of reimagining how society functions when the relationship between work and livelihood fundamentally changes.
Copyright expansion offers the comforting illusion that we can address AI’s disruption through familiar legal tools, avoiding the harder work of reimagining how society functions when the relationship between work and livelihood fundamentally changes.
India can lead by refusing this distraction – by recognizing AI requires us to rethink “work”, labor policy, and social provision, not tinker with licensing schemes that entrench corporate control while pretending modest fees can substitute for disappeared livelihoods. The question before us is whether we’ll have the intellectual courage to match problems with appropriate tools, or whether we’ll expand copyright beyond its proper domain to include a licensing framework for the extraction of meta-information, creating new property rights that fail to address the genuine harms while consolidating power in platforms that have already enclosed our collective cultural heritage.
The series brings together expert voices and was commissioned to inform the development of the issue brief by IT for Change, ‘Governing AI for the Cultural Commons: Beyond Intellectual Property’, under the AI, Culture and Intellectual Property Subgroup of the UNESCO Global Civil Society Organizations (CSO) and Academic Network on AI Ethics and Policy.
New article every Wednesday! Watch this space for more thinkpieces and read the issue brief here.
