India’s titanic vision for artificial intelligence leadership is careening towards a regulatory iceberg. The Department for Promotion of Industry and Internal Trade (DPIIT)’s new proposal for AI copyright—called “One Nation One License One Payment”—aims to balance innovation with creator compensation. Instead, it threatens to strangle India’s AI ambitions while gifting China an unassailable lead in this century’s defining technology.
The proposal envisions a mandatory licensing system in which AI companies pay revenue-based royalties to a government-linked collective for training models on copyrighted content. Developers would surrender a percentage of their revenues—merely for using creative work to help the model “learn.”
The objectives are admirable. India possesses extraordinary creativity that deserves protection and fair compensation. But rushing to legislate while generative AI is still in its nascent phases of development would be a mistake. Copyright law emerged from the age of the printing press, and evolved as new forms of information media surfaced. AI isn’t more of the same problem; copying AI is fundamentally different; it internalizes patterns rather than copying content, and is likely to transform content markets in unpredictable ways, likely opening up new opportunities for creators.
Locking in a rigid framework now, while the technology is still only in its early days, risks cementing the wrong approach and potentially robbing creators of new models of value exchange that could emerge from new AI tools. More urgently, it would make India one of the most hostile environments in the world for serious AI development, effectively ensuring that the “AI sovereignty” New Delhi seeks remains a pipe dream.
Consider the perverse economic incentives. AI tools commercialized in India would have to pay royalties based on their total revenues, regardless of the amount of copyrighted content used to train its models. That’s like charging a filmmaker a percentage of their box office revenues for having studied classic films in film school—not just when they remake those films, but for everything they ever create after watching them. The system also pegs that percentage to achieving a certain level of success in global revenues, punishing Indian successful startups by having to pay the government with funds that might otherwise be reinvested into innovation.
The compliance burden alone would be prohibitive. To adhere, companies would have to navigate government bureaucracy, disclose sensitive revenue data, submit to audits, and face potential injunctions. Why would any serious AI player train models in India when Singapore and Japan bypass those headaches entirely in their regulations?
These practical obstacles are downstream of the fact that the revenue-based model misapprehends how AI systems work. Training data isn’t “used” the way copyrighted content traditionally is—it’s distilled into patterns, which the model uses to generate novel outputs. Charging AI companies based on overall revenues treats training data as if it’s embodied in every output, overstating its contribution and creating impossible valuation problems.
But the most serious flaw with the plan may be strategic. While India tangles itself in costly regulatory complexity, China will race ahead. Beijing views AI development as central to its government’s ambitions, and will leave no stone unturned in ensuring that its companies have every advantage possible in pursuit of breakneck AI progress. China already pilfers hundreds of billions in intellectual property annually. Chinese AI developers won’t respect Indian copyright when they brazenly ignore far more established IP protections. While Indian companies and global companies investing in AI in India navigate bureaucracy and stomach the extra costs of revenue sharing, labs in China will scrape Indian data and train without the slightest hesitation, and no extra costs. China’s competitive advantage would be immediate and enduring, as would all other countries with a more lax approach to the issue.
This asymmetry has global implications. If Chinese AI tools—built on unrestricted access to copyrighted content—capture market share while Indian developers face obstructive restrictions, Beijing will effectively come to set international AI norms. The copyright protections India seeks would end up entirely routed by China’s AI dominance.
There is a better path. Rather than targeting AI training inputs, India should focus on outputs. Tools like output filters can help prevent the reproduction of copyrighted content, while “notice and takedown” systems can allow rightsholders to alert companies of infringing content on their platforms. This approach effectively manages infringement while preserving space for new creativity. It also avoids the impossible task of valuing every training input’s contribution to a model’s aggregate performance. In other words, a focus on outputs aligns incentives properly; companies would have reason to avoid generating outputs that infringe on copyright, while still being free to learn from the full expanse of human knowledge.
India’s global leadership in AI can be further strengthened by focusing on collaborative, incentive-driven approaches to building Indian training datasets rather than centralised mandates like mandatory blanket licensing. These alternatives would prioritise voluntary participation, public-private partnerships, and innovation ecosystems to build indigenous capabilities in data, compute, models, and IP. Instead of enforcing royalties through centralised entities like the one proposed, the government could offer fiscal incentives—such as tax deductions or grants under the IndiaAI Mission—to content creators and organisations that voluntarily contribute datasets to national AI repositories like AIRaoh.
The proposal is currently open for review, and not yet official policy, meaning that India still has the opportunity to pursue alternative approaches. The options before it are stark: look for more pragmatic approaches that support India’s aspirations for global AI leadership, or double down on an elaborate copyright framework that would stifle India’s AI ecosystem.
Bill Drexel is a Senior Fellow at the Hudson Institute, where he focuses on U.S.-India relations and international technology competition. Chitra Shekhawat is a Research Fellow at India Foundation, where she focuses on economics, and international relations.
